WO1998010307A1 - Location of a mobile station - Google Patents
Location of a mobile station Download PDFInfo
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- WO1998010307A1 WO1998010307A1 PCT/US1997/015892 US9715892W WO9810307A1 WO 1998010307 A1 WO1998010307 A1 WO 1998010307A1 US 9715892 W US9715892 W US 9715892W WO 9810307 A1 WO9810307 A1 WO 9810307A1
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- Prior art keywords
- location
- bcation
- station
- mobile station
- stations
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Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/025—Services making use of location information using location based information parameters
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S1/00—Beacons or beacon systems transmitting signals having a characteristic or characteristics capable of being detected by non-directional receivers and defining directions, positions, or position lines fixed relatively to the beacon transmitters; Receivers co-operating therewith
- G01S1/02—Beacons or beacon systems transmitting signals having a characteristic or characteristics capable of being detected by non-directional receivers and defining directions, positions, or position lines fixed relatively to the beacon transmitters; Receivers co-operating therewith using radio waves
- G01S1/022—Means for monitoring or calibrating
- G01S1/026—Means for monitoring or calibrating of associated receivers
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S1/00—Beacons or beacon systems transmitting signals having a characteristic or characteristics capable of being detected by non-directional receivers and defining directions, positions, or position lines fixed relatively to the beacon transmitters; Receivers co-operating therewith
- G01S1/02—Beacons or beacon systems transmitting signals having a characteristic or characteristics capable of being detected by non-directional receivers and defining directions, positions, or position lines fixed relatively to the beacon transmitters; Receivers co-operating therewith using radio waves
- G01S1/022—Means for monitoring or calibrating
- G01S1/028—Simulation means, e.g. of beacon signals therefor
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/0009—Transmission of position information to remote stations
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/0009—Transmission of position information to remote stations
- G01S5/0045—Transmission from base station to mobile station
- G01S5/0054—Transmission from base station to mobile station of actual mobile position, i.e. position calculation on base station
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0205—Details
- G01S5/021—Calibration, monitoring or correction
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0205—Details
- G01S5/0244—Accuracy or reliability of position solution or of measurements contributing thereto
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0257—Hybrid positioning
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0278—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves involving statistical or probabilistic considerations
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S2205/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S2205/001—Transmission of position information to remote stations
- G01S2205/006—Transmission of position information to remote stations for emergency situations
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S2205/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S2205/001—Transmission of position information to remote stations
- G01S2205/008—Transmission of position information to remote stations using a mobile telephone network
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/0009—Transmission of position information to remote stations
- G01S5/0018—Transmission from mobile station to base station
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/06—Position of source determined by co-ordinating a plurality of position lines defined by path-difference measurements
Definitions
- the present invention is directed generally to a system and method for locating people or objects, and in particular, to a system and method for locating a wireless mobile station using a plurality of simultaneously activated mobile station location estimators.
- Wireless communications systems are becoming increasingly important worldwide.
- Wireless cellular telecommunications systems are rapidly replacing conventional wire-based telecommunications systems in many applications.
- Cellular radio telephone networks (“CRT")
- specialized mobile radio and mobile data radio networks are examples.
- the general principles of wireless cellular telephony have been described variously, for example in U. S. Patent 5,295,180 to Vendetti, et al, which is incorporated herein by reference.
- GPS Global Positioning Satellite system
- a ground-based GPS receiver at or near the object to be located determines the difference between the time at which each satellite transmits a time signal and the time at which the signal is received and, based on the time differentials, determines the object's location.
- GPS is impractical in many applications.
- the signal power levels from the satellites are low and the GPS receiver requires a clear, line-of-sight path to at least three satellites above a horizon of about 60 degrees for effective operation. Accordingly, inclement weather conditions, such as clouds, terrain features, such as hills and trees, and buildings restrict the ability of the GPS receiver to determine its position. Furthermore, the initial GPS signal detection process for a GPS receiver is relatively long (i.e., several minutes) for determining the receiver's position. Such delays are unacceptable in many applications such as, for example, emergency response and vehicle tracking.
- Differential GPS, or DGPS systems offer correction schemes to account for time synchronization drift.
- Such correction schemes include the transmission of correction signals over a two-way radio link or broadcast via FM radio station subcarriers. These systems have been found to be awkward and have met with limited success.
- GPS-based location systems have been attempted in which the received GPS signals are transmitted to a central data center for performing location calculations. Such systems have also met with limited success.
- each of the various GPS embodiments have the same fundamental problems of limited reception of the satellite signals and added expense and complexity of the electronics required for an inexpensive location mobile station or handset for detecting and receiving the GPS signals from the satellites.
- Radio Propagation Background The behavior of a mobile radio signal in the general environment is unique and complicated. Efforts to perform correlations between radio signals and distance between a base station and a mobile station are similarly complex. Repeated attempts to solve this problem in the past have been met with only marginal success. Factors include terrain undulations, fixed and variable clutter, atmospheric conditions, internal radio characteristics of cellular and PCS systems, such as frequencies, antenna configurations, modulation schemes, diversity methods, and the physical geometries of direct, refracted and reflected waves between the base stations and the mobile.
- Noise such as man-made externally sources (e.g., auto ignitions) and radio system co-channel and adjacent channel interference also affect radio reception and related performance measurements, such as the analog carrier-to- interference ratio (C/l), or digital e ⁇ ergy-per-bit Noise density ratio (E b/No ) and are particular to various points in time and space domains.
- C/l analog carrier-to- interference ratio
- E b/No digital e ⁇ ergy-per-bit Noise density ratio
- Fig. I illustrates a definition of channel types arising in communications: Over the last forty years various mathematical expressions have been developed to assist the radio mobile cell designer in establishing the proper balance between base station capital investment and the quality of the radio link, typically using radio energy field- strength, usually measured in microvolts/meter, or decibels.
- L f transmission line loss from coaxials used to connect radio to antenna, in dB,
- G t gain of transmitting antenna
- G r gain of receiving antenna
- Free space path loss L p as discussed in Mobile Communications Design Fundamentals, William C r. Lee, 2nd, Ed across the propagation channel is a function of distanced, frequency f (for f values ⁇ I GHz, such as the 890-950 mHz cellular band):
- ⁇ , (dB) (equation 3) indicates that the free propagation path loss is 20 dB per decade.
- Frequencies between I GHz and 2GHz experience increased values in the exponent, ranging from 2 to 4, or 20 to 40 dB/decade, which would be predicted for the new PCS 1.8 - 1.9 GHz band.
- Equation 6 (Equation 6) assuming f is equal to or greater than 400 mHz.
- the typical suburban model correction was found to be:
- the typical rural model modified the urban formula differently, as seen below:
- d represents the distance between the mobile station (MS) and the base station (BS); P or represents the received power in free space) for a given set of unchanging environmental conditions, it may be possible to dynamically measure P or and then determine d.
- Ui. Patent 5,055,851 to Sheffer taught that if three or more relationships have been established in a triangular space of three or more base stations (BSs) with a location database constructed having data related to possible mobile station (MS) locations, then arculatio ⁇ calculations may be performed, which use three distinct P or measurements to determine an X,Y, two dimensional location, which can then be projected onto an area map.
- the triangulation calculation is based on the fact that the approximate distance of the mobile station (MS) from any base station (BS) cell can be calculated based on the received signal strength.
- Sheffer acknowledges that terrain variations affect accuracy, although as noted above, Sheffer's disclosure does not account for a sufficient number of variables, such as fixed and variable location shadow fading, which are typical in dense urban areas with moving traffic.
- path loss was a function of three factors: (I) the path loss between antennas in free space; (2) the reduction of rooftop wave fields due to settling; and (3) the effect of diffraction of the rooftop fields down to ground level. The last two factors were summarily termed L ex> gj ven Dv:
- a substantial difficulty with the two-ray model in practice is that it requires a substantial amount of data regarding building dimensions, geometries, street widths, antenna gain characteristics for every possible ray path, etc. Additionally, it requires an inordinate amount of computational resources and such a model is not easily updated or maintained.
- Greenstein studied the communication channels variable Bit-Error-Rate (BER) in a spatial domain, which was a departure from previous research that limited field measurements to the RF propagation channel signal strength alone.
- BER Bit-Error-Rate
- Greenstein based his finding on two suspicious assumptions: I) he assumed that distance correlation estimates were identical for uplink and downlink transmission paths; and 2) modulation techniques would be transparent in terms of improved distance correlation conclusions. Although some data held very correlations, other data and environments produced poor results. Accordingly, his results appear unreliable for use in general location context.
- Greenstein et al, authored "A Measurement-Based Model for Predicting Coverage Areas of Urban Microcells", in the IEEE Journal On Sele ⁇ ed Areas in Communications, Vol. 11, No.7, 9/93.
- Greenstein reported a generic measurement-based model of RF attenuation in terms of constant-value contours surrounding a given low-power, low antenna microcell environment in a dense, rectilinear neighborhood, such as New York City. However, these contours were for the cellular frequency band. In this case, LOS and non-LOS clutter were considered for a given microcell site.
- the physical radio propagation channel perturbs signal strength, frequency (causing rate changes, phase delay, signal to noise ratios (e.g., C/l for the analog case, or l , RF energy per bit, over average noise density ratio for the digital case) and Doppler-shift.
- Signal strength is usually characterized by: • Free Space Path Loss (L p ) • Slow fading loss or margin (L,
- Loss due to slow fading includes shadowing due to clutter blockage (sometimes included in Lp).
- Fast fading is composed of multipath reflections which cause: I) delay spread; 2) random phase shift or Rayleigh fading; and 3) random frequency modulation due to different Doppler shifts on different paths.
- FIG.3 the figure illustrates key components of a typical cellular and PCS power budget design process.
- the cell designer increases the transmitted power P ⁇ x by the shadow fading margin L,. ow which is usually chosen to be within the I -2 perc ⁇ ntile of the slow fading probability density fun ⁇ ion (PDF) to minimize the probability of unsatisfactorily low received power level P u at the receiver.
- the P ⁇ level must have enough signal to noise energy level (e.g., 10 dB) to overcome the receiver's internal noise level (e.g., -ll ⁇ dBm in the case of cellular 0.9 GHz), for a minimum voice quality standard.
- P ⁇ must never be below -108 dBm, in order to maintain the quality standard.
- fast fading margin L ⁇ which is typically also chosen to be a few percentiles of the fast fading distribution.
- the I to 2 percentiles compliment other network blockage guidelines.
- the cell base station traffic loading capacity and network transport facilities are usually designed for a I -2 percentile blockage fartor as well.
- both fading margins are simultaneously exceeded, thus causing a fading margin overload.
- transmitter power GHz band with a transmitter antenna height of 6.4m and an MS receiver antenna height of 2m, and assumptions regarding total path loss, transmitter power would be calculated as follows:
- the fast fading margin was determined to be:
- Related objectives for the present invention include providing a system and method that: (I.I) can be readily incorporated into existing commercial wireless telephony systems with few, if any, modifications of a typical telephony wireless infrastru ⁇ ure;
- (1.2) can use the native electronics of typical commercially available telephony wireless mobile stations (e.g., handsets) as location devices;
- (I J) can be used for effectively locating people and/or obje ⁇ s wherein there are few (if any) line-of-sight wireless receivers for receiving location signals from a mobile station (herein also denoted MS);
- (1.6) can substantially automatically adapt and/or (re)train and/or ( recalibrate itself according to changes in the environment and/or terrain of a geographical area where the present invention is utilized.
- Yet another objective is to provide a low cost location system and method, adaptable to wireless telephony systems, for using simultaneously a plurality of location techniques for synergistically increasing MS location accuracy and consistency.
- MS location techniques can be utilized by various embodiments of the present invention: (2.1) time-of-arrival wireless signal processing techniques; (2.2) time-difference-of-arrival wireless signal processing techniques;
- adaptive wireless signal processing techniques having, for example, learning capabilities and including, for instance, artificial neural net and genetic algorithm processing;
- wireless herein is, in general, an abbreviation for "digital wireless", and in particular, “wireless” refers to digital radio signaling using one of standard digital protocols such as CDMA, NAMPS, AMPS, TDMA and GSM, as one skilled in the art will understand.
- MS mobile station
- MS refers to a wireless device that is at least a transmitting device, and in most cases is also a wireless receiving device, such as a portable radio telephony handset. Note that in some contexts herein instead or in addition to MS, the following terms are also used: “personal station” (PS), and “location unit” (LU). In general, these terms may be considered synonymous.
- infrastru ⁇ ure includes telephony wireless base stations (BS) such as those for radio mobile communication systems based on CDMA, AMPS, NAMPS, TDMA, and GSM wherein the base stations provide a network of cooperative communication channels with an air interface with the MS, and a conventional telecommunications interface with a Mobile Switch Center (MSC).
- BS telephony wireless base stations
- MS Mobile Switch Center
- an MS user within an area serviced by the base stations may be provided with wireless communication throughout the area by user transparent communication transfers (i.e., "handoffs") between the user's MS and these base stations in order to maintain effective telephony service.
- the mobile switch center provides communications and control connectivity among base stations and the public telephone network.
- composite wireless signal characteristic values denotes the result of aggregating and filtering a colle ⁇ ion of measurements of wireless signal samples, wherein these samples are obtained from the wireless communication between an MS to be located and the base station infrastructure (e.g., a plurality of networked base stations).
- signal chara ⁇ eristic values for signal samples between an MS to be located and a single base station.
- signal chara ⁇ eristic values or “signal chara ⁇ eristic data” are used when either or both a location signature(s) and/or a location signature cluster(s) are intended.
- the present invention relates to a wireless mobile station location system.
- a wireless mobile station location system may be decomposed into: (i) a first low level wireless signal processing subsystem for receiving, organizing and conditioning low level wireless signal measurements from a network of base stations cooperatively linked for providing wireless communications with mobile stations (MSs); and (ii) a second high level signal processing subsystem for performing high level data processing for providing most likelihood location estimates for mobile stations.
- MSs mobile stations
- second high level signal processing subsystem for performing high level data processing for providing most likelihood location estimates for mobile stations.
- the present invention is a novel signal processor that includes at least the functionality for the high signal processing subsystem mentioned hereinabove. Accordingly, assuming an appropriate ensemble of wireless signal measurements chara ⁇ erizing the wireless signal communications between a particular MS and a networked wireless base station infrastructure have been received and appropriately filtered of noise and transitory values (such as by an embodiment of the low level signal processing subsystem disclosed in a upending PCT patent application titled, "Wireless Location Using A Plurality of Commercial Network Infrastructures," by F. W. LeBlanc, and the present applicant(s); this copending patent application being herein incorporated by reference), the present invention uses the output from such a low level signal processing system for determining a most likely location estimate of an MS.
- target MS a wireless telephony infrastru ⁇ ure
- the signal data measurements are ensembles of samples from the wireless signals received from the target MS by the base station infrastru ⁇ ure, wherein these samples are subsequently filtered using analog and digital spe ⁇ ral filtering, the present invention accomplishes the objectives mentioned above by the following steps:
- each of the models output MS location estimates having substantially identical data stru ⁇ ures (each such data structure denoted a "location hypothesis").
- each location hypothesis may also includes a confidence value indicating the likelihood or probability that the target MS whose location is desired resides in a corresponding location estimate for the target MS; (4.4) adjusting or modifying location hypotheses output by the models according to, for example, 2.4 through 2.6 above so that the adjusted location hypotheses provide better target MS location estimates.
- such adjustments are performed on both the target MS location estimates of the location hypotheses as well as their corresponding confidences; and (4.4) subsequently computing a "most likely" target MS location estimate for outputting to a location requesting application such as 911 emergency, the fire or police departments, taxi services, etc. Note that in computing the most likely target MS location estimate a plurality of location hypotheses may be taken into account.
- the most likely MS location estimate is determined by computationally forming a composite MS location estimate utilizing such a plurality of location hypotheses so that, for example, location estimate similarities between location hypotheses can be effectively utilized.
- the filtered and aggregated wireless signal chara ⁇ eristic values are provided to a number of location hypothesizing models (denoted Fi ⁇ t Order Models, or FOMs), each of which yields a location estimate or location hypothesis related to the location of the target MS.
- FOMs location hypothesizing models
- confidence values of the location hypotheses are provided as a continuous range of real numbers from, e.g., -I to I, wherein the most unlikely areas for locating the target MS are given a confidence value of -I, and the most likely areas for locating the target MS are given a confidence value of I. That is, confidence values that are larger indicate a higher likelihood that the target MS is in the corresponding MS estimated area, wherein I indicates that the target MS is absolutely NOT in the estimated area, 0 indicates a substantially neutral or unknown likelihood of the target MS being in the corresponding estimated area, and I indicates that the target MS is absolutely within the corresponding estimated area.
- the following capabilities are provided by the present invention:
- this data base includes: (a) a plurality of previously obtained location signature clusters (i.e., composite wireless signal characteristic values)
- the composite signal chara ⁇ eristic values used to generate various location hypotheses for the target MS are compared against wireless signal data of known MS locations stored in the location signature data base for determining the reliability of the location hypothesizing models for particular geographic areas and/or environmental conditions; (53) a capability for reasoning about the likeliness of a location hypothesis wherein this reasoning capability uses heuristics and constraints based on physics and physical properties of the location geography;
- the present invention utilizes adaptive signal processing techniques.
- One particularly important utilization of such techniques includes the automatic tuning of the present invention so that, e.g., such tuning can be applied to adjusting the values of location processing system paramete ⁇ that affect the processing performed by the present invention.
- system parameters as those used for determining the size of a geographical area to be specified when retrieving location signal data of known MS locations from the historical (location signature) data base can substantially affect the location processing.
- a system parameter specifying a minimum size for such a geographical area may, if too large, cause unnecessary inaccuracies in locating an MS.
- an adaptation engine is included in the present invention for automatically adjusting or tuning paramete ⁇ used by the present invention.
- the adaptation engine is based on genetic algorithm techniques.
- a novel aspe ⁇ of the present invention relies on the discovery that in many areas where MS location services are desired, the wireless signal measurements obtained from communications between the target MS and the base station infrastructure are extensive enough to provide sufficiently unique or peculiar values so that the pattern of values alone may identify the location of the target MS. Further, assuming a sufficient amount of such location identifying pattern information is captured in the composite wireless signal chara ⁇ eristic values for a target MS, and that there is a technique for matching such wireless signal patterns to geographical locations, then a FOM based on this technique may generate a reasonably accurate target MS location estimate.
- the present invention e.g., the location signature data base
- this captured data e.g., location signatures
- the present invention includes one or more FOMs that may be generally denoted as classification models wherein such FOMs are trained or calibrated to associate particular composite wireless signal characteristic values with a geographical location where a target MS could likely generate the wireless signal samples from which the composite wireless signal chara ⁇ eristic values are derived.
- the present invention includes the capability for training (calibrating) and retraining (recalibrating) such classification FOMs to automatically maintain the accuracy of these models even though substantial changes to the radio coverage area may occur, such as the construction of a new high rise building or seasonal variations (due to, for example, foliage variations).
- classification FOMs that are trained or calibrated to identify target MS locations by the wireless signal patterns produced constitute a particularly novel aspect of the present invention.
- the phenomenon of signal multipath and shadow fading rende ⁇ most analytical location computational techniques such as time-of- arrival (TOA) or time-differe ⁇ ce-of-arrival (TDOA) substantially useless in urban areas and particularly in dense urban areas.
- TOA time-of- arrival
- TDOA time-differe ⁇ ce-of-arrival
- this same multipath phenomenon also may produce substantially distinct or peculiar signal measurement patterns, wherein such a pattern coincides with a relatively small geographical area.
- the present invention utilizes multipath as an advantage for increasing accuracy where for previous location systems multipath has been a source of substantial inaccuracies.
- classification FOMs in high multipath environments is especially advantageous in that high multipath environments are typically densely populated.
- high multipath environments are typically densely populated.
- training or calibration data captured by the present invention for training or calibrating such classification FOMs and for progressively improving the MS location accuracy of such models.
- LBS location dete ⁇ ion base stations
- a grid of such LBSs can be utilized for providing location signal data (from the built-in MS) for ( retraining or (re)calibrati ⁇ g such classification FOMs.
- one or more classification FOMs may each include a learning module such as an artificial neural network (ANN) for associating target MS location signal data with a target MS location estimate.
- ANN artificial neural network
- one or more classification FOMs may be statistical prediction models based on such statistical techniques as, for example, principle decomposition, partial least squares, or other regression techniques.
- the pe ⁇ onal communication system (PCS) infrastructures currently being developed by telecommunication providers offer an appropriate localized infrastructure base upon which to build various p ⁇ onal location systems (PLS) employing the present invention and/or utilizing the techniques disclosed herein.
- the present invention is especially suitable for the location of people and/or obje ⁇ s using code division multiple access (CDMA) wireless infrastructures, although other wireless infrastru ⁇ ures, such as, time division multiple access (TDMA) infrastructures and GSM are also contemplated.
- CDMA code division multiple access
- TDMA time division multiple access
- CDMA personal communications systems are described in the Telephone industries Association standard IS-95, for frequencies below I GHz, and in the Wideband Spread- Spectrum Digital Cellular System Dual-Mode Mobile Station-Base Station Compatibility Standard, for frequencies in the 1.8-1.9 GHz frequency bands, both of which are incorporated herein by reference.
- CDMA general principles have also been described, for example, in U. S. Patent 5,109390, to Gilhausen, et al, and CDMA Network Engineering Handbook by Qualcomm, Inc., each of which is also incorporated herein by reference.
- CDMA is an electromagnetic signal modulation and multiple access scheme based on spread spe ⁇ rum communication.
- Each CDMA signal corresponds to an unambiguous pseudorandom binary sequence for modulating the carrier signal throughout a predetermined spe ⁇ rum of bandwidth frequencies.
- Transmissions of individual CDMA signals are sele ⁇ ed by correlation processing of a pseudonoise waveform.
- the CDMA signals are separately detected in a receiver by using a correlator, which accepts only signal energy from the sele ⁇ ed binary sequence and despreads its spe ⁇ rum.
- the present invention can substantially automatically retrain and/or recalibrate itself to compensate for variations in wireless signal characteristics (e.g., multipath) due to environmental and/or topographic changes to a geographic area serviced by the present invention.
- the present invention optionally includes low cost, low power base stations, denoted location base stations (LBS) above, providing, for example, CDMA pilot channels to a very limited area about each such LBS.
- LBS location base stations
- the location base stations may provide limited voice traffic capabilities, but each is capable of gathering sufficient wireless signal characteristics from an MS within the location base station's range to facilitate locating the MS.
- additional MS location accuracy can be obtained. That is, due to the low power signal output by such location base stations, for there to be signaling control communication (e.g., pilot signaling and other control signals) between a location base station and a target MS, the MS must be relatively near the location base station. Additionally, for each location base station not in communication with the target MS, it is likely that the MS is not near to this location base station.
- signaling control communication e.g., pilot signaling and other control signals
- the present invention can substantially narrow the possible geographic areas within which the target MS is likely to be. Further, by providing each location base station (LBS) with a co-located stationary wireless transceiver (denoted a built-in MS above) having similar functionality to an MS, the following advantages are provided:
- the stationary transceiver can be signaled by another compo ⁇ ent(s) of the present invention to a ⁇ ivate or deactivate its associated base station capability, thereby conserving power for the LBS that operate on a restri ⁇ ed power such as solar electrical power; (62) the stationary transceiver of an LBS can be used for transferring target MS location information obtained by the LBS to a conventional telephony base station;
- the present invention is able to (re)train and/or (re)calibrate itself in geographical areas having such LBSs. That is, by activating each LBS stationary transceiver so that there is signal communication between the stationai ⁇ transceiver and surrounding base stations within range, wireless signal chara ⁇ eristic values for the location of the stationary transceiver are obtained for each such base station. Accordingly, such characteristic values can then be associated with the known location of the stationary transceiver for training and/or calibrating various of the location processing modules of the present invention such as the classification FOMs discussed above.
- training and/or calibrating may include:
- portable location verifying ele ⁇ ronics are provided so that when such ele ⁇ ronics are sufficiently near a located target MS, the ele ⁇ ronics: (I) dete ⁇ the proximity of the target MS; (ii) determine a highly reliable measurement of the location of the target MS; (iii) provide this measurement to other location determining components of the present invention so that the location measurement can be associated and archived with related signal chara ⁇ eristic data received from the target MS at the location where the location measurement is performed.
- the use of such portable location verifying ele ⁇ ronics allows the present invention to capture and utilize signal characteristic data from verified, substantially random locations for location system calibration as in (63) above. Moreover, it is important to note that such location verifying ele ⁇ ronics can verify locations automatically wherein it is unnecessary for manual activation of a location verifying process.
- One embodiment of the present invention includes the location verifying ele ⁇ ronics as a "mobile (location) base station" (MBS) that can be, for example, incorporated into a vehicle, such as an ambulance, police car, or taxi.
- MBS mobile (location) base station
- Such a vehicle can travel to sites having a transmitting target MS, wherein such sites may be randomly located and the signal chara ⁇ eristic data from the transmitting target MS at such a location can consequently be archived with a verified location measurement performed at the site by the mobile location base station.
- a mobile location base station as its name implies also includes base station ele ⁇ ronics for communicating with mobile stations, though not necessarily in the manner of a conventional infrastructure base station.
- a mobile location base station may only monitor signal characteristics, such as MS signal strength, from a target MS without transmitting signals to the target MS.
- a mobile location base station can periodically be in bi-directional communication with a target MS for determining a signal time-of-arrival (or time-difference-of- arrival) measurement between the mobile location base station and the target MS.
- each such mobile location base station includes components for estimating the location of the mobile location base station, such mobile location base station location estimates being important when the mobile location base station is used for locating a target MS via, for example, time-of-arrival or time-difference-of-arrival measurements as one skilled in the art will appreciate.
- a mobile location base station can include: (7.1) a mobile station (MS) for both communicating with other components of the present invention (such as a location processing center included in the present invention);
- a GPS receiver for determining a location of the mobile location base station
- a mobile location base station includes modules for integrating or reconciling distinct mobile location base station location estimates that, for example, can be obtained using the components and devices of (7.1) through (7.4) above. That is, location estimates for the mobile location base station may be obtained from: GPS satellite data, mobile location base station data provided by the location processing center, dead reckoning data obtained from the mobile location base station vehicle dead reckoning devices, and location data manually input by an operator of th ⁇ mobile location base station.
- the location estimating system of the present invention offers many advantages over existing location systems.
- the system of the present invention for example, is readily adaptable to existing wireless communication systems and can accurately locate people and/or obje ⁇ s in a cost effective manner.
- the present invention requires few, if any, modifications to commercial wireless communication systems for implementation.
- existing personal communication system infrastru ⁇ ure base stations and other components of, for example, commercial CDMA infrastru ⁇ ures are readily adapted to the present invention.
- the present invention can be used to locate people and/or obje ⁇ s that are not in the line-of-sight of a wireless receiver or transmitter, can reduce the detrimental effects of multipath on the accuracy of the location estimate, can potentially locate people and/or obje ⁇ s located indoors as well as outdoors, and uses a number of wireless stationary transceivers for location.
- the present invention employs a number of distinctly different location computational models for location which provides a greater degree of accuracy, robustness and versatility than is possible with existing systems.
- the location models provided include not only the radius- radius/TOA and TDOA techniques but also adaptive artificial neural net techniques.
- the present invention is able to adapt to the topography of an area in which location service is desired.
- the present invention is also able to adapt to environmental changes substantially as frequently as desired.
- the present invention is able to take into account changes in the location topography over time without extensive manual data manipulation.
- the present invention can be utilized with varying amounts of signal measurement inputs.
- a location estimate is desired in a very short time interval (e.g., less than approximately one to two seconds)
- th ⁇ present location estimating system can be used with only as much signal measurement data as is possible to acquire during an initial portion of this time interval. Subsequently, after a greater amount of signal measurement data has been acquired, additional more accurate location estimates may be obtained.
- this capability can be useful in the context of 911 emergency response in that a first quick course wireless mobile station location estimate can be used to route a 911 call from the mobile station to a 911 emergency response center that has responsibility for the area containing the mobile station and the 911 caller. Subsequently, once the 911 call has been routed according to this first quick location estimate, by continuing to receive additional wireless signal measurements, more reliable and accurate location estimates of the mobile station can be obtained. Moreover, there are numerous additional advantages of the system of the present invention when applied in CDMA communication systems.
- the location system of the present invention readily benefits from the distinct advantages of the CDMA spread spe ⁇ rum scheme, namely, these advantages include the exploitation of radio frequency spe ⁇ ral efficiency and isolation by (a) monitoring voice activity, (b) management of two-way power control, (c) provisioning of advanced variable-rate modems and error correcting signal encoding, (d) inherent resistance to fading, (e) enhanced privacy, and (f) multiple "rake" digital data receivers and searcher receivers for correlation of signal multipaths.
- it is an aspect of the present invention to demonstrate the utilization of various novel computational paradigms such as:
- hypotheses are: (8.1.1) generated by modular independent hypothesizing computational models;
- the novel computation archite ⁇ ure of the pres ⁇ t invention can be utilized by (I) providing a plurality of document scanning models as the first order models, (ii) building a chara ⁇ er recognition data base for archiving a correspondence between chara ⁇ eristics of a ⁇ ual printed chara ⁇ er variations and the intended characters (according to, for example, font types), and additionally archiving a correspondence of performance of each of the models on previously encountered a ⁇ ual printed chara ⁇ er variations (note, this is analogous to the Signature Data Base of th ⁇ MS location application described herein), and (iii) determining any generic constraints and/or heuristics that are desirable to be satisfied by a plurality of the models.
- such models running at various remote computational facilities can transfer weather predi ⁇ ion hypotheses (e.g., the likely path of a hurricane) to a site that performs hypothesis adjustments according to: (i) past performance of the each model; (ii) particular constraints and/or heuristics, and subsequently outputs a most likely estimate for a particular weather condition.
- weather predi ⁇ ion hypotheses e.g., the likely path of a hurricane
- each having an initial location estimate) by the first order models may be such that this processing can be provided on Internet user nodes and the first order models may reside at Internet server sites.
- an internet user may request hypothes ⁇ s from such remote first order models and perform the remaining processing at his/her node.
- a fast, abeit less accurate location estimate may be initially performed for very time critical location applications where approximate location information may be required. For example, less than I second response for a mobile station location embodiment of the present invention may be desired for 911 emergency response location requests. Subsequ ⁇ ntly, once a relatively course location estimate has be ⁇ n provided, a more accurate most likely location estimate can be performed by repeating the location estimation processing a second time with, e.g., additional with measurements of wireless signals transmitted between a mobile station to be located and a network of base stations with which the mobile station is communicating, thus providing a second, more accurate location estimate of the mobile station.
- central location development sites may be networked to, for example, geographically dispersed location cent ⁇ rs providing location services according to the present invention, wherein the FOMs may be accessed, substituted, enhanced or removed dynamically via network conne ⁇ ions (via, e.g., the Internet) with a central location dev ⁇ lopment site.
- network conne ⁇ ions via, e.g., the Internet
- a small but rapidly growing municipality in substantially flat low density area might initially be provided with access to, for example, two or three FOMs for generating location hypotheses in the municipality's relatively uncluttered radio signaling environment.
- FOMs may be transferred via the network to the location center for the municipality.
- the FOMs can be incorporated into an expert system, if desired.
- each FOM may be a ⁇ ivated from an antec ⁇ dent of an expert system rule.
- the antecedent for such a rule can evaluate to TRUE if the FOM outputs a location hypothesis, and the consequent portion of such a rule may put the output location hypothesis on a list of location hypotheses occurring in a particular time window for subsequent processing by the location cent ⁇ r.
- a ⁇ ivation of the FOMs may be in the consequents of such expert system rules. That is, th ⁇ antecedent of such an expert system rule may determine if the conditions are appropriate for invoking the FOM(s) in the rule's consequent.
- Fig. I illustrates various perspectives of radio propagation opportunities which may b ⁇ considered in addressing correlation with mobile to base station ranging.
- Fig.2 shows aspe ⁇ s of the two-ray radio propagation model and the effects of urban clutter.
- Fig.3 provides a typical exampl ⁇ of how the statistical power budg ⁇ t is calculated in design of a Commercial Mobile Radio
- Fig.4 illustrates an overall view of a wireless radio location network architecture, based on AIN principl ⁇ s.
- Fig.5 is a high l ⁇ v ⁇ l block diagram of an ⁇ mbodiment of the present invention for locating a mobile station (MS) within a radio coverage area for the present invention.
- Fig.6 is a high level block diagram of the location ce ⁇ t ⁇ r 142.
- Fig.7 is a high level block diagram of the hypothesis evaluator for the location center.
- Fig.8 is a substantially comprehensive high level block diagram illustrating data and control flows between the components of the location cent ⁇ r, as well th ⁇ functionality of the components.
- Fig.9 is a high level data stru ⁇ ure diagram describing the fields of a location hypothesis obje ⁇ generated by the first order mod ⁇ ls 1224 of the location center.
- Fig. 10 is a graphical illustration of the computation performed by the most likelihood estimator 1344 of the hypothesis evaluator.
- Fig. 11 is a high level block diagram of the mobile base station (MBS).
- Fig. 12 is a high level state transition diagram describing computational states the Mobile Base station enters during operation.
- Fig. 13 is a high level diagram illustrating the data structural organization of the Mobile Base station capability for autonomously determining a most likely MBS location from a plurality of potentially confli ⁇ ing MBS location estimating sources.
- Fig. 14 shows one method of mod ⁇ li ⁇ g CDMA delay spread measurem ⁇ nt ensembl ⁇ s and interfacing such signals to a typical artificial neural network based FOM.
- Fig. 15 illustrates the nature of RF "Dead Zones" , notch area, and the importance of including location data signatures from the back side of radiating elements.
- Figs. 16a through 16c present a table providing a brief description of the attributes of the location signature data type stored in the location signature data base 1320.
- Figs. 17a through 17c present a high level flowchart of the steps performed by function, "UPDATE LOC SIG DB,” for updating location signatures in th ⁇ location signature data base 1320; note, this flowchart corresponds to the description of this fun ⁇ ion in APPENDIX C.
- Figs. 18a through 18b present a high level flowchart of the steps perform ⁇ d by fun ⁇ ion, "REDUCEJJAD DB LOC SIGS,” for updating location signatures in the location signature data base 1320; not ⁇ , this flowchart corresponds to the description of this fu ⁇ ion in APPENDIX C.
- Figs. 19a through 19b present a high level flowchart of the steps performed by fun ⁇ ion, "INCREASE CONFIDENCE OF GOOD DBJOC SIGS," for updating location signatures in the location signature data base 1320; note, this flowchart corresponds to th ⁇ description of this fun ⁇ ion in APPENDIX C.
- Figs.20a through 20d present a high level flowchart of the steps performed by fun ⁇ ion, "DETERMINE LOCATION SIGNATURE FIT RRORS," for updating location signatures in th ⁇ location signature data base 1320; note, this flowchart corresponds to th ⁇ description of this fun ⁇ ion in APPENDIX C.
- Fig.21 presents a high lev ⁇ l flowchart of th ⁇ st ⁇ ps performed by fun ⁇ ion, "ESTIMATE_L0C_5IG_FR0M_DB,” for updating location signatures in the location signature data base 1320; note, this flowchart corresponds to the description of this fun ⁇ ion in APPENDIX C.
- Figs.22a through 22b present a high level flowchart of the st ⁇ ps performed by fun ⁇ ion, "GET_AREA_TO_SEARCH,” for updating location signatures in the location signature data base 1320; note, this flowchart corresponds to the description of this fun ⁇ io ⁇ in APPENDIX C.
- Figs.23a through 23b present a high lev ⁇ l flowchart of th ⁇ st ⁇ ps performed by fun ⁇ ion, "GET_DIFFERENCE_MEASUREMENT," for updating location signatures in th ⁇ location signature data base 1320; note, this flowchart corresponds to the description of this fun ⁇ ion in APPENDIX C.
- Fig.24 is a high lev ⁇ l illustration of context adjuster data stru ⁇ ures and th ⁇ ir relationship to the radio coverage area for the present invention
- Figs.25a through 25b present a high level flowchart of the steps performed by the fun ⁇ ion, "CONTEXT ADJUSTER,” used in the context adjuster 1326 for adjusting mobile station esti at ⁇ s provided by the first order models 1224; this flowchart corresponds to the description of this function in APPENDIX D.
- Figs.26a through 26c present a high lev ⁇ l flowchart of the steps performed by the fun ⁇ ion, "GET_ADJUSTED_LOC_HYP_LIST_F0R,” used in the context adjuster 1326 for adjusting mobile station estimates provided by the first order models 1224; this flowchart corresponds to the description of this fun ⁇ ion in APPENDIX D.
- Figs.27a through 27b present a high level flowchart of the steps performed by the fun ⁇ ion, "CONFIDENCE ADJUSTER,” used in th ⁇ context adjuster 1326 for adjusting mobile station estimat ⁇ s provid ⁇ d by the first order models 1224; this flowchart corresponds to the description of this function in APPENDIX D.
- Fig.28a and 28b presents a high level flowchart of the steps performed by the function,
- Figs.29a through 29h present a high level flowchart of the steps performed by the fun ⁇ ion, "GET_PRED!CTION_MAPPED_CLUSTER_DENSITY_FOR,” used in the context adjuster 1326 for adjusting mobile station estimates provided by th ⁇ fi ⁇ t ord ⁇ r models 1224; this flowchart corresponds to the description of this fun ⁇ ion in APPENDIX D.
- Fig.30 illustrates the primary components of th ⁇ signal processing subsystem.
- Fig.31 illustrates how automatic provisioning of mobile station information from multiple CMRS occu ⁇ .
- Various digital wireless communication standards have be ⁇ n introduc ⁇ d such as Advanced Mobile Phone Service (AMPS), Narrowband Advanced Mobile Phon ⁇ Service (NAMPS), code division multiple access (CDMA) and Time Division Multiple Access (TDMA) (e.g., Global Systems Mobile (GSM).
- AMPS Advanced Mobile Phone Service
- NAMPS Narrowband Advanced Mobile Phon ⁇ Service
- CDMA code division multiple access
- TDMA Time Division Multiple Access
- GSM Global Systems Mobile
- CDMA this standard is described in the Telephone Industries Association standard IS- 95, for frequencies below I GHz, and in J-STD-008, the Wideband Spread-Spe ⁇ rum Digital Cellular System Dual-Mode Mobile Station-Base station Compatibility Standard, for frequencies in the 1.8 - 1.9 GHz frequency bands.
- Patent 5,109390 Diversity Receiver in a CDMA Cellular Telephone System by Gilhousen.
- CDMA radio technology There are numerous advantages of such digital wireless technologies such as CDMA radio technology.
- the CDMA spread spe ⁇ rum scheme exploits radio frequency spe ⁇ ral efficiency and isolation by monitoring voice activity, managing two- way power control, provision of advanced variable-rate modems and error correcting signal design, and includes inherent resistance to fading, enhanced privacy, and provides for multiple "rake" digital data receivers and searcher receivers for correlation of multiple physical propagation paths, res ⁇ mbling maximum likelihood dete ⁇ ion, as well as support for multiple base station communication with a mobile station, i.e., soft or softer hand-off capability.
- this same advanced radio communication infrastru ⁇ ure can also be used forenhanc ⁇ d radio location.
- th ⁇ capabilities of IS-41 and AIN already provide a broad-granularity of wireless location, as is necessary to, for exampl ⁇ , properly direct a terminating call to an MS.
- Such information originally intended for call processing usage, can be re-us ⁇ d in conju ⁇ ion with th ⁇ location center described herein to provide wireless location in the large (i.e., to determine which country, state and city a particular MS is located) and wirel ⁇ ss location in the small (i.e., which location, plus or minus a few hundred feet within one or more base stations a given MS is located).
- Fig.4 is a high level diagram of a wireless digital radiolocation intelligent network archite ⁇ ure for the present invention. Accordingly, this figure illustrat ⁇ s the interconnections between the components, for example, of a typical PCS network configuration and various components that are specific to the present invention.
- a typical wireless (PCS) network includes:
- MSs conventional wireless mobile stations
- each cell site includes an infrastru ⁇ ure base station such as those labeled 122 (or variations thereof such as I22A - I22D).
- the base stations 122 denote the standard high traffic, fixed location base stations used for voice and data communication with a plurality of MSs 140, and, according to the present invention, also used for communication of information related to locating such MSs 140.
- the base stations labeled 152 are more directly related to wireless location enablement.
- the base stations 152 may be low cost, low functionality transponders that are used primarily in communicating MS location related information to the location center 142 (via base stations 122 and the MSC 112).
- the base stations 152 will be referred to hereinafter as “location base statio ⁇ (s) 152" or simply “LBS(s) 152”);
- a public switched t ⁇ l ⁇ phone network (PSTN) 124 (which may include signaling system links 106 having network control components such as: a service control point (SCP) 104 , one or more signaling transfer points (STPs) 110.
- SCP service control point
- STPs signaling transfer points
- the present invention provides the following additional components: (10.1) a location center 142 which is required for determining a location of a target MS 140 using signal chara ⁇ eristic values for this target MS;
- MBS mobile base stations 148
- LBS location base stations 152
- MS 140 detection area 154 MS 140 detection area 154
- location base stations can be located on potentially each floor of a multi-story building, the wireless location technology described herein can be used to perform location in terms of height as well as by latitude and longitude.
- the MS 140 may utilize one of the wireless technologies, CDMA, TDMA, AMPS, NAMPS or GSM techniques for radio communication with: (a) one or more infrastru ⁇ ure bas ⁇ stations 122, (b) mobil ⁇ base statio ⁇ (s) 148 , (c) an LBS 152.
- base stations may be placed in any configuration, a typical deployment configuration is approximately in a cellular honeycomb pattern, although many practical tradeoffs exist, such as site availability, versus the requirement for maximal terrain coverage area.
- base stations three such exemplary base stations (BSs) are I22A, I22B and I22C, each of which radiate referencing signals within their area of coverage 169 to facilitate mobile station (MS) 140 radio frequency connectivity, and various timing and synchronization functions.
- BSs base stations
- I22A, I22B and I22C each of which radiate referencing signals within their area of coverage 169 to facilitate mobile station (MS) 140 radio frequency connectivity, and various timing and synchronization functions.
- MS mobile station
- some base stations may contain no setters 130 ( ⁇ .g.
- base station I22E thus radiating and receiving signals in a 360 degree omnidirectional coverage area pattern, or the base station may contain "smart antennas" which have specialized coverage area patterns.
- base station I22A includes sectors 130, additionally labeled a, b and c. Accordingly, each of th ⁇ sectors 130 radiate and receive signals in an approximate 120 degree arc, from an overhead view.
- a ⁇ ual base station coverage areas 169 (stylistically represented by hexagons about the bas ⁇ stations 122) generally are designed to overlap to some extent, thus ensuring seamless coverage in a geographical area.
- Control electronics within each base station 122 are used to communicate with a mobile stations 140.
- Information regarding th ⁇ coverage area for each sector 1 0, such as its range, area, and "holes" or areas of no coverage (within the radio coverage area 120), may be known and used by the location center 142 to facilitate location determination.
- the identification of each base station 122 communicating with the MS 140 as well, as any se ⁇ or identification information may be known and provided to the location center 142.
- a base station or mobility controller 174 controls, processes and provides an interface between originating and terminating tel ⁇ phone calls from/to mobile station (MS) 140, and the mobil ⁇ switch center (MSC) 112.
- the MSC 122 on-the-oth ⁇ r-hand, performs various administration fun ⁇ ions such as mobil ⁇ station 140 registration, authentication and the relaying of various system paramet ⁇ rs, as o ⁇ skilled in the art will unde ⁇ tand.
- Th ⁇ base stations 122 may be coupled by various transport facilities 176 such as leased lines, frame relay, T-Carri ⁇ r links, optical fiber links or by microwave communication links.
- a mobile station 140 When a mobile station 140 (such as a CDMA, AMPS, NAMPS mobile telephone) is powered on and in the idle state, it constantly monitors the pilot signal transmissions from each of the base stations 122 located at nearby cell sites. Since base station/sector coverage areas may often overlap, such ov ⁇ rlapping ⁇ ables mobile stations 140 to dete ⁇ , and, in the case of certain wireless technologies, communicate simultaneously along both the forward and reverse paths, with multiple base stations 122 and/or se ⁇ ors 130. In Fig.4 the constantly radiating pilot signals from base station se ⁇ ors 130, such as se ⁇ ors a, b and c of BS I22A, are detectable by mobile stations 140 within the coverage area 169 for BS I22A.
- base station se ⁇ ors 130 such as se ⁇ ors a, b and c of BS I22A
- the mobile stations 140 scan for pilot channels, corresponding to a given base station/s ⁇ or identifie ⁇ (IDs) , for determining which coverage area 169 (i.e., cell) it is contained. This is performed by comparing signals strengths of pilot signals transmitted from these particular cell-sites.
- IDs base station/s ⁇ or identifie ⁇
- the mobile station 140 then initiates a registration request with the MSC 112, via the base station controller 174.
- the MSC 112 determines whether or not the mobile station 140 is allowed to proceed with the registration proc ⁇ ss ( ⁇ xcept in th ⁇ case of a 911 call, wherein no registration process is required). At this point calls may be originated from the mobile station 140 or calls or short message service messages can be received from the network.
- the MSC 112 communicat ⁇ s as appropriate, with a class 4 ⁇ wireline telephony circuit switch or other central offices, conn ⁇ ed to th ⁇ PSTN 124 network. Such central offices conne ⁇ to wireline terminals, such as telephon ⁇ s, or any communication device compatible with the line.
- the PSTN 124 may also provide con ⁇ e ⁇ ions to long distance networks and other networks.
- the MSC 112 may also utilize IS/41 data circuits or trunks connecting to signal transfer point 110, which in turn conne ⁇ s to a service control point 104, via Signaling System #7 (SS7) signaling links (e.g., trunks) for intelligent call processing, as one skilled in the art will understand.
- SS7 Signaling System #7
- Such links are used for call routing instructions of calls interacting with the MSC 112 or any switch capable of providing service switching point functions, and the public switched telephone network (PSTN) 124, with possible termination back to the wireless network.
- PSTN public switched telephone network
- the location center (LC) 142 interfaces with the MSC 112 either via dedicated transport facilities 178, using for example, any number of LAN/WAN technologies, such as Ethernet, fast Ethernet, frame relay, virtual privat ⁇ networks, etc., or via the PSTN 124.
- the LC 142 receives autonomous (e.g., unsolicited) command/r ⁇ spons ⁇ m ⁇ ssages regarding, for ⁇ xampl ⁇ : (a) th ⁇ stat ⁇ of the wireless network of each service provider, (b) MS 140 and BS 122 radio frequency (RF) measurements, (c) any MBSs 148, (d) location applications requesting MS locations using the location center.
- autonomous e.g., unsolicited
- RF radio frequency
- the LC 142 provides data and control information to each of the above components in (a) - (d). Additionally, the LC 142 may provide location information to an MS 140, via a BS 122. Moreover, in the case of the use of a mobile base station (MBS) 148, several communications paths may exist with the LC 142.
- MBS mobile base station
- the MBS 148 a ⁇ s as a low cost, partially-fun ⁇ io ⁇ al, moving base station, and is, in one embodim ⁇ nt, situated in a vehicle where an operator may engage in MS 140 searching and tracking a ⁇ ivities.
- the MBS 148 provides a forward link pilot channel fora target MS 140, and subsequently receives unique BS pilot strength measurements from th ⁇ MS 140.
- the MBS 148 also includes a mobile station for data communication with the LC 142, via a BS 122. In particular, such data communication includes telemetering the geographic position of the MBS 148 as well as various RF measurements related to signals received from the target MS 140.
- the MBS 148 may also utilize multiple-beam fixed antenna array el ⁇ m ⁇ nts and/or a mov ⁇ able narrow beam antenna , such as a microwave dish 182.
- the antennas for such embodiments may have a known orientation in order to further deduce a radio location of the target MS 140 with respect to an estimated current location of the MBS 148.
- the MBS 148 may further contain a global positioning system (GPS), distance sensors, dead-reckoning ele ⁇ ronics, as well as an on-board computing system and display devices for locating both the MBS 148 of itself as well as tracking and locating th ⁇ target MS 140.
- Th ⁇ computing and display provides a means for communicating the position of the target MS 140 on a map display to an operator of the MBS 148.
- Each location base station (LBS) 152 is a low cost location device. Each such LBS 152 communicates with one or more of the infrastru ⁇ ure base stations 122 using one or more wireless technology interface standards. In some embodiments, to provide such LBS's cost effectively, ⁇ ach LBS 152 only partially or minimally supports th ⁇ air-interface standards of the one or more wireless technologi ⁇ s us ⁇ d in communicating with both the BSs 122 and the MSs 140. Each LBS 152, when put in service, is placed at a fixed location, such as at a traffic signal, lamp post, etc., and wherein the location of the LBS may be determined as accurately as, for example, the accuracy of the locations of the infrastru ⁇ ure BSs 122.
- each BS 122 uses a time offset of the pilot PN sequenc ⁇ to identify a forward CDMA pilot channel.
- each LBS 152 emits a unique, time-offset pilot PN sequence channel in accordance with the CDMA standard in the RF spectrum designated for BSs 122, such that the channel does not interfere with neighboring BSs 122 cell site channels, nor would it interfere with neighboring LBSs 152.
- time offsets in CDMA chip sizes, may be re-used within a PCS system, thus providing efficient use of pilot time offset chips, thereby achieving spe ⁇ rum efficiency.
- Each LBS 152 may also contain multiple wireless receivers in order to monitor transmissions from a target MS 140. Additionally, each LBS 152 contains mobile station 140 electronics, thereby allowing the LBS to both be controlled by the LC 142, and to transmit information to the LC 142, via at least one neighboring BS 122. As m ⁇ ntion ⁇ d above, when the location of a particular target MS 140 is desired, th ⁇ LC 142 can request location information about the target MS 140 from, for instance, one or more a ⁇ ivated LBSs 1 2 in a geographical area of interest.
- the LBS's pilot channel appea ⁇ to the target MS 140 as a potential neighboring base station channel, and consequently, is placed, for example, in the CDMA neighboring set, or the CDMA remaining set, of the target MS 140 (as one familiar with the CDMA standards will u ⁇ de ⁇ tand).
- the target MS 140 will, if within range of such an a ⁇ ivated LBS 152, dete ⁇ the LBS pilot presence during the CDMA pilot channel acquisition substate. Consequently, the target MS 140 performs RF measurements on the signal from ⁇ ach d ⁇ te ⁇ d LBS 152. Similarly, an a ⁇ ivated LBS 152 can perform RF measurements on the wireless signals from the target MS 140. Accordingly, ⁇ ach LBS 152 d ⁇ t ⁇ ing the target MS 140 may subsequently t ⁇ l ⁇ m ⁇ t ⁇ r back to the LC 142 measurement results related to signals from/to the target MS 140.
- the target MS 140 upon command, the target MS 140 will telemeter back to the LC 142 its own measurements of the dete ⁇ ed LBSs 152, and consequently, this new location information, in conjunction with location related information received from the BSs 122, can be used to locate the target MS 140.
- an LBS 152 will normally deny hand-off requ ⁇ sts, sinc ⁇ typically the LBS does not require th ⁇ added complexity of handling voice or traffic bearer channels, although economics and peak traffic load conditions would dictate preference here.
- GPS timing information needed by any CDMA base station, is ⁇ ith ⁇ r achi ⁇ ved via a th ⁇ inclusion of a local GPS receiver or via a telemetry process from a neighboring conventional BS 1 2, which contains a GPS receiver and timing information. Since energy requirements are minimal in such an LBS 152, (recharg ⁇ abl ⁇ ) batteries or solar cells may b ⁇ us ⁇ d to power the LBS. No expensive terrestrial transport link is typically required since two-way communication is provided by the included MS 140 (or an el ⁇ ctronic variation thereof). Thus, LBSs 152 may be placed in numerous locations, such as:
- a location application programming interface 136 (Fig.4), or L-API, is required between the location center 142 (LC) and the mobile switch cent ⁇ r (MSC) network el ⁇ m ⁇ nt type, in order to send and receiv ⁇ various control, signals and data messages.
- the L- API should be implemented using a preferably high-capacity physical layer communications interface, such as IEEE standard 8023 (10 baseT Ethernet), although other physical layer interfaces could be used, such as fiber optic ATM, frame relay, etc.
- Two forms of API implementation are possible. In the first case the signals control and data messages are realized using the MSC 112 vendor's native operations messag ⁇ s inherent in the produ ⁇ offering, without any special modifications.
- the second cas ⁇ th ⁇ L-API includes a full suite of commands and messaging content specifically optimized for wireless location purposes, which may require some, although minor development on the part of the MSC vendor.
- the signal processing subsystem receives control messages and signal measurements and transmits appropriate control messages to the wireless network via the location applications programming interface referenced earlier, for wireless location purposes.
- the signal processing subsystem additionally provides various signal idi ⁇ tification, conditioning and preprocessing fu ⁇ ions, including buffering, signal type classification, signal filtering, message control and routing fun ⁇ ions to the location estimate modules.
- the mobile station 140 may be able to det ⁇ up to three or four Pilot Channels representing three to four Base Stations, or as few as on ⁇ Pilot Channel, depending upon th ⁇ environment.
- possibly more than one BS 122 can detect a mobil ⁇ station 140 transmitter signal, as evid ⁇ nced by the provision of cell diversity or soft hand-off in the CDMA standards, and the fa ⁇ that multipl ⁇ CMRS' bas ⁇ station equipment commonly will overlap coverage areas.
- multiple delayed signals, or "fingers” may be detected and tracked resulting from multipath radio propagation conditions, from a given transmitter.
- the "first" finger represents the most direct, or least delayed multipath signal.
- S ⁇ co ⁇ d or possibly third or fourth fingers may also be detected and tracked, assuming the mobile station contains a sufficient numb ⁇ r of data receivers.
- TOA and TDOA methods would discard subsequent fingers related to the same transmitted finger, collection and use of th ⁇ s ⁇ additional values can prove useful to reduce location ambiguity, and are thus collected by the Signal Processing subsystem in the Location Cent ⁇ r 142.
- a numb ⁇ r of combinations of measurements could be made available to the Location Center. Due to the disperse and near-random nature of CDMA radio signals and propagation characteristics, traditional TOA FDOA location methods have failed in the past, because th ⁇ number of signals received in different locations area different. In a particularly small urban area, say l ⁇ ss than 500 square feet, the number of RF signals and there multipath components may vary by over 100 percent.
- th ⁇ forward link mobil ⁇ station's received relative signal strength (RRSS BS ) of detected nearby base station transmitter signals can be used directly by th ⁇ location ⁇ stimate modules
- the CDMA base station's reverse link received relative signal strength (RRSS MS ) of the dete ⁇ ed mobile station transmitter signal must be modified prior to location estimate model use, since the mobile station transmitter power l ⁇ v ⁇ l changes n ⁇ arly continuously, and would thus render relative signal strength useless for location purposes.
- One adjustment variable and one factor value are required by the signal processing subsystem in the CDMA air interface cas ⁇ : I.) instantaneous relative power lev ⁇ l in dBm (IRPL) of th ⁇ mobil ⁇ station transmitter, and 2.) th ⁇ mobile station Power Class.
- IRPL instantaneous relative power lev ⁇ l in dBm
- SRSS synthetic relative signal strength
- SRSS RRSS MS + IRPL (in dBm)
- SRSS MSj a corre ⁇ ed indication of the eff ⁇ iv ⁇ path loss in the rev ⁇ e dire ⁇ ion (mobile station to BS), is now comparable with RRSS B5 and can be used to provide a correlation with either distance or shadow fading because it now accounts for the cha ⁇ g ⁇ of th ⁇ mobile station transmitter's power level.
- the two signals RRSS BS and SRSS m can now be process ⁇ d in a variety of ways to achieve a more robust correlation with distance or shadow fading.
- a CDMA radio signal direction-independent "net relative signal strength measurement” is derived which is used to establish a correlation with ⁇ ith ⁇ r distance or shadow fading, or both.
- other means can be used in conjunction, such as the fingers of the CDMA delay spread measurement, and any other TOA IDOA calculations from other geographical points.
- the first finger of a CDMA delay spread signal is most likely to be a relatively shorter duration than the case where the mobil ⁇ station 140 and BS 122 are separated by a greater distance, since shadow fading does not materially affe ⁇ the arrival time delay of th ⁇ radio signal.
- This enhanc ⁇ d capability is provided via a control message, sent from the Location center 142 to the mobil ⁇ switch center 12, and then to the base station(s) in communication with, or in close proximity with, mobile stations 140 to be located.
- Two types of location measurement requ ⁇ st control m ⁇ ssag ⁇ s are needed: one to instruct a target mobile station 140 (i.e., the mobile station to be located) to telemeter its BS pilot channel measurements back to the primary BS 122 and from there to the mobile switch center 112 and then to the location system 42.
- the second control message is sent from the location system 42 to the mobile switch center 112, then to first the primary BS, instructing the primary BS' searcher receiver to output (i.e., return to th ⁇ initiating request m ⁇ ssage source) the dete ⁇ ed targ ⁇ t mobil ⁇ station 140 transmitter CDMA pilot channel offset signal and their corresponding delay spread finger (peak) values and related relative signal strengths.
- the control messages are impl ⁇ m ⁇ nted in standard mobile station 140 and BS 122 CDMA receivers such that all data results from the search receiver and multiplexed results from the associated data receivers are available for transmission back to the Location Center 142.
- Appropriate value ranges are required regarding mobil ⁇ station 140 param ⁇ ters T_ADDgate T_DROP treat and the ranges and values for the A ⁇ ive, Neighboring and Remaining Pilot sets regist ⁇ rs, held within the mobile station 140 memory. Further mobile station 140 receiver details have been discussed above.
- CDMA pilot chann ⁇ ls and delay spread fi ⁇ ge ⁇ can or should be measured vary according to the number of data receivers contained in each mobile station 140.
- RF chara ⁇ eristics permit, at least three pilot channels and the strongest fi ⁇ t three fingers, are collected and processed.
- the strong ⁇ st first four CDMA delay spread fingers and the mobile station power level be collected and sent to th ⁇ location syst ⁇ m 42, for each of preferably three BSs 122 which can dete ⁇ the mobile station 140.
- a much larger combination of measurements is potentially feasible using the extended data collection capability of the CDMA receivers.
- Fig.30 illustrates the components of the Signal Processing Subsystem.
- the main components consist of the input queu ⁇ (s) 7, signal classifier/filter 9, digital signaling processor 17, imaging filters 19, output queue(s) 21 , router/distributor 23, a signal processor database 26 and a signal processing controller 15.
- Input qu ⁇ u ⁇ s 7 are required in order to stage the rapid acceptanc ⁇ of a significant amount of RF signal measurement data, used for either location estimate purposes or to accept autonomous location data.
- Each location request using fixed base stations may, in one embodiment, contain from I to 128 radio frequency measurements from the mobile station, which translates to approximately 61.44 kilobytes of signal measurement data to be collected within 10 seconds and 128 measurements from each of possibly four base stations, or 245.76 kilobytes for all base stations, for a total of approximately 640 signal measurements from the five sources, or 307.2 kilobytes to arrive per mobile station location request in 10 seconds.
- An input queue storage space is assigned at the moment a location request begins, in order to establish a formatted data structure in persistent store. Depending upon the urgency of the time required to render a location estimate, fewer or more signal measurement samples can be taken and stored in th ⁇ input queue(s) 7 accordingly.
- the signal processing subsystem supports a variety of wireless network signaling measurement capabilities by dete ⁇ ing th ⁇ capabilities of th ⁇ mobile and base station through messaging stru ⁇ ures provided bt th ⁇ location application programming interface. Detection is accomplished in the signal classifier 9 (Fig.30) by referencing a mobil ⁇ station database table within the signal processor database 26, which provides, given a mobile station identification numb ⁇ r, mobil ⁇ station revision code, other mobile station chara ⁇ ersitics.
- a mobile switch center table 31 provides MSC chara ⁇ eristics and identifications to the signal classifier/filter 9.
- the signal classifier/filter adds additional message header information that further classifies the measurement data which allows the digital signal processor and image filter components to select the proper internal processing subcomponents to perform operations on the signal measurement data, for use by the location estimat ⁇ modules.
- the signal classifier/filter 9 det ⁇ mines via a signal processing database 26 query that the message is to be associated with a home base station module.
- appropriate head ⁇ r information is added to the messag ⁇ , thus ⁇ nabling th ⁇ m ⁇ ssage to pass through the digital signal processor 17 unaffected to the output queu 21, and then to the router/distributor 23.
- Th ⁇ router/distributor 23 then routes the message to the HBS fi ⁇ t order model.
- the router and distributor component 23 is responsible to dire ⁇ ing specific signal measurement data types and stru ⁇ ures to their appropriate modules. For example, th ⁇ HBS FOM has no use for digital filtering stru ⁇ ures, whereas the TDOA module would not be able to process an HBS response messag ⁇ .
- Th ⁇ controller 15 is responsible for staging th ⁇ mov ⁇ m ⁇ nt of data among the signal processing subsystem 20 components input queue 7, digital signal processor 17, router/distributor 23 and the output qu ⁇ ue 21, and to initiate signal measurments within the wireless network, in response from an internet 168 location request message in Fig. I, via the location application programming interface.
- the controller 15 receives autonomous messages from the MSC , via the location applications programming interface (Fig. I) or L-API and the input queue 7, when ⁇ v ⁇ ra 9-1-1 wireless call is originated.
- the mobile switch center provides this autonomous notification to the location system as follows: By specifiying the appropriate mobile switch center op ⁇ rations and maintenance commands to surveil calls based on certain digits dialed such as 9-1-1, the location applications programming interface, in communications with the MSCs, receives an autonomous notification whenever a mobile station user dials 9-1-1.
- a bi- dir ⁇ ional authorized communications port is configured, usually at the operations and maintenance subsystem of the MSCs, or with their associated network element manager system(s), with a data circuit, such as a DS-I, with the location applications programming interface in Fig. I .
- the "call trace" capability of the mobile switch center is a ⁇ ivated for the respe ⁇ ' rve communications port.
- the exa ⁇ implementation of the vendor-specific man-machine or Open Syst ⁇ ms Interface (OSi) commands(s) and their associated data stru ⁇ ures generally vary among MSC vendors, howev ⁇ r th ⁇ trace fun ⁇ ion is generally available in various forms, and is required in ord ⁇ r to comply with Federal Bureau of Investigation authorities for wire tap purposes.
- 9-1-1 call notifications m ⁇ ssages containing th ⁇ mobil ⁇ station identification number (MIN) and, in phase I E9-I-I implementations, a ps ⁇ udo-automatic number ide ⁇ tication (a.k.a. pANI) which provides an association with the primary base station in which the 9-1-1 caller is in communicaiton.
- MIN mobil ⁇ station identification number
- pANI ps ⁇ udo-automatic number ide ⁇ tication
- the signal processing subsystem avoids querying the MSC in question to determine the primary base station identification associated with the 9-1-1 mobile station caller.
- the controller 15 After the signal processing controller 15 receives the fi ⁇ t m ⁇ ssag ⁇ type, the autonomous notification message from the mobile switch center 112 to the location system 42, containing the mobile identification number and optionally the primary base station identification, the controller 15 qu ⁇ ries the base station table 13 in the signal processor database 26 to determine the status and availability of any neighboring base stations, including those base stations of other CMRS in the area.
- the definition of neighboring bas ⁇ stations include not only thos ⁇ within a provisionable "hop" based on the cell design reuse fattor, but also includes, in the case of CDMA, results from remaining set information autonomously queried to mobile stations, with results stored in th ⁇ bas ⁇ station tabl ⁇ .
- Remaining set information indicates that mobile stations can dete ⁇ other base station (se ⁇ or) pilot channels which may exce ⁇ d th ⁇ "hop" distance, yet are nevertheless intend ⁇ base stations (or se ⁇ ors) for wireless location purposes.
- "hop" distance is usually one or two cell coverage areas away from the primary base station's cell coverage area.
- the controller 15 Having determined a likely set of base stations which may both dete ⁇ the mobile station's transmitter signal, as well as to determine the set of likely pilot channels (i.e., base stations and their associated physical antenna se ⁇ o ⁇ ) detectable by the mobile station in the area surrounding the primary base station (sector), the controller 15 initiates messages to both the mobile station and appropriate base stations (sectors) to perform signal measurements and to return the results of such measurements to the signal processing system regarding the mobile station to be located. This step may be accomplished via several interface means. In a fi ⁇ t case the controller 15 utilizes, for a given MSC, predetermined storage information in the MSC table 31 to determine which type of commands, such as man-machine or OSI commands are needed to request such signal measurements for a given MSC.
- the controller generates the mobile and base station signal measurement commands appropriate for the MSC and passes the commands via the input queue 7 and the locations application programming interface in Fig.l, to th ⁇ appropriat ⁇ MSC, using the authoriz ⁇ d communications port m ⁇ ntioned earli ⁇ r.
- the controller 15 communicates directly with base stations within having to interface directly with the MSC for signal measurement extraction.
- the signal classifier 9 in Fig.30 examines location application programming interface-provided message header information from the source of the location measurement (for example, from a fixed BS 122, a mobile station 140, a distributed antenna system 168 in Fig. I or messag ⁇ location data related to a home base station), provid ⁇ d by the location applications programming interface (L-API) via the input queue 7 in Fig.30 and det ⁇ rmin ⁇ s wh ⁇ th ⁇ r or not d ⁇ vic ⁇ filters 17 or imag ⁇ filters 19 are need ⁇ d, and assesses a relative priority in processing, such as an emergency ve ⁇ us a background location task, in terms of grouping like data associated with a given location request.
- L-API location applications programming interface
- additional signal classifier fun ⁇ ion includes sorting and associating the appropriate incoming signal measurements togeth ⁇ r such that th ⁇ digital signal processor 17 processes related measurements in order to build ensemble data sets.
- Such ensembles allow for a variety of fun ⁇ ions such as averaging, outlier removal over a timeperiod, and related filtering fun ⁇ ions, and further prevent association errors from occuri ⁇ g in location ⁇ stimate processing.
- Another fun ⁇ ion of the signal classifier/low pass filter component 9 is to filter information that is not useable, or information that could introduce noise or the effe ⁇ of noise in the location estimat ⁇ modul ⁇ s.
- low pass matching filters are us ⁇ d to match the in-common signal processing components to the chara ⁇ eristics of the incoming signals.
- Low pass filters match: Mobile Station, bas ⁇ station, CMRS and MSC chara ⁇ eristics, as wall as to classify Home Bas ⁇ Station messages.
- the signal processing subsystem contains a bas ⁇ station databas ⁇ table 13 (Fig.30) which captures the maximum number of CDMA delay spread fingers for a given base station.
- the base station identification code or CLLI or common language lev ⁇ l identification code is useful in identifying or relating a human-labeled name descriptor to the Base Station.
- Latitude, Longitude and el ⁇ vation values are us ⁇ d by other subsystems in the location system for calibration and estimation purposes.
- base stations and/or receiver chara ⁇ eristics are added, d ⁇ l ⁇ t ⁇ d, or chang ⁇ d with respect to the network us ⁇ d for location purposes, this database table must be modified to reflett the current network configuration.
- the tabl ⁇ establishes the relationships among various mobile station equipm ⁇ nt suppliers and certain technical data relevant to this location invention. Although not strictly n ⁇ c ⁇ ssary, The MIN can be populated in this table from the PCS Service Provider's Customer Care system during subscriber a ⁇ ivation and fulfillment, and could be changed at dea ⁇ ivation, or anytime the ⁇ nd-us ⁇ r changes mobile stations.
- the MIN, manufa ⁇ urer, model number, and software revision lev ⁇ l information is available during a telephone call, this information could extracted during the call, and the remaining fi ⁇ lds populated dynamically, based on manufacturer's' specifications information previously stored in the signal processing subsystem 20. Default values are used in cases where the MIN is not found, or where certain information must be estimated.
- a low pass mobile station filter contained within the signal classifier/low pass filter 9 of the signal processing subsystem 20, uses the above table data to perform the following functions: I) act as a low pass filter to adjust the nominal assumptions related to the maximum number of CDMA fingers, pilots detectable; and 2) to determine the transmit power class and the receiver thermal noise floor.
- the required value of SRSS MS a corrected indication of the effective path loss in th ⁇ reverse dire ⁇ ion (mobile station to BS), can be calculated based data contained within the mobile station table 11, stored in the signal processing database 26.
- the effects of the maximum Number of CDMA fingers allowed and the maximum number of pilot channels allowed essentially form a low pass filter effe ⁇ , wherein the least common denominator of characteristics are used to filter the incoming RF signal measurements such that a one for one matching occurs.
- the effe ⁇ of the transmit power class and receiv ⁇ r th ⁇ rmal ⁇ ois ⁇ floor valu ⁇ s is to normalize the chara ⁇ eristics of the incoming RF signals with respe ⁇ to those RF signals used.
- the signal classifier/filter 20 is in communication with both the input queue 7 and the signal processing database 26.
- the signal processing subsystem 142 in Fig.4 will receive the initiating location request from either an autonomous 9-1-1 notification messag ⁇ from a given MSC, or from a location application (for exampl ⁇ , s ⁇ Fig.36), for which mobil ⁇ station chara ⁇ eristics about the target mobile station 140 (Fig. I) is required.
- Fig. I mobil ⁇ station chara ⁇ eristics about the target mobile station 140
- a query is made from the signal processing controller 15 to the signal processning database 26, specifically the mobile station table 11, to determine if the mobile station chara ⁇ eristics associated with the MIN to be located is available in table 11, if the data exists then there is no need for the controller 15 to query the wireless network in order to determi ⁇ th ⁇ mobile station chara ⁇ eristics, thus avoiding additional real-time processing which would otherwise be required across the air interface, in order to determine the mobile station MIN chara ⁇ eristics.
- the resulting mobile station information my be provided either via the signal processing database 26 or alternatively a query may be performed directly from the signal processing subsystem 20 to the MSC in order to determine the mobile station chara ⁇ eristics.
- L-API-CCS 139 to the appropriate CMRS customer care system provides th ⁇ mechanism to populate and update the mobile station table 11 within the database 26.
- the L-API-CCS 139 contains its own set of separate input and output queues or similar implementations and security controls to ensure that provisioning data is not sent to the incorrect CMRS, and that a given CMRS cannot access any other CMRS' data.
- the interface 1155a to the customer care system for CMRS-A 1150a provides an autonomous or periodic notification and response application layer protocol type, consisting of add, delete, change and verify message fun ⁇ ions in order to update the mobile station table 11 within the signal processing database 26, via the controller 15.
- a similar interface 1155b is used to ⁇ nabl ⁇ provisioning updates to be received from CMRS-B customer care system 1150b.
- the L-API-CCS application message set may be any protocol type which supports th ⁇ autonomous notification message with positive acknowledgment type
- the Tl Ml .5 group within the American National Standards Institute has defined a good starting point in which the L-API-CCS could be implem ⁇ nted, using the robust OSI TMN X-interface at th ⁇ service management layer.
- the DSP 17 in communication with th ⁇ signal classifier/LP filter 9, provides a time series expansion method to convert non-HBS data from a format of an signal measure data ensemble of time-series based radio frequency data measurements, collected as discrete time-slice samples, to a thre ⁇ dim ⁇ nsional matrix location data valu ⁇ image representation.
- Other techniques further filter the resultant imag ⁇ in ord ⁇ r to furnish a l ⁇ ss noisy training and a ⁇ ual data sampl ⁇ to the location estimate modules.
- the first CDMA delay spread finger may be the same value for a fixed distance between the mobile station and BS antennas, as the mobile station moves across such an arc, different finger-data are measured.
- location class ⁇ s, or squares numbered one through sev ⁇ n are shown across a particular range of line of position (LOP).
- a traditional TOA/FDOA ranging method between a given BS and mobile station only provides a range along the arc, thus introducing ambiguity error.
- a unique three dimensional image can be used in this method to specifically identify, with recurring probability, a particular unique location class along the same Line Of Position, as long as the multipath is unique by position but generally repeatable, thus establishing a method of not only ranging, but also of complete latitude, longitude location estimation in a Cartesian space.
- the unique shap ⁇ of the "mountain image" enables a correspondence to a given unique location class along a line of position, thereby eliminating traditional ambiguity error.
- thre ⁇ basic types of filtering methods can be used to reduc ⁇ matching/comparison ⁇ rror from a training cas ⁇ to a location request case: I.) select only the strongest signals from the forward path (BS to mobile station) and reverse path (mobile station to BS), 2.) Convolute the forward path 128 sample image with the reverse path 128 sample image, and 3.) process ail image samples through various digital image filters to discard noise components.
- convolution of forward and reverse images is performed to drive out noise. This is one embodiment that essentially nulls noise completely, even if strong and recurring, as long as that same noise chara ⁇ eristic does not occur in the opposite path.
- the third embodiment or technique of processing CDMA delay spread profile images through various digital image filters provides a resultant "image enhancement" in the sense of providing a more stable pattern recognition paradigm to the neural net location estimate model.
- image histogram equalization can be us ⁇ d to rearrange th ⁇ images' intensity values, or density recurrence values, so that the image's cumulative histogram is approximately linear.
- m ⁇ thods which can be used to compensate for a concentrated histogram include: I) Input Cropping, 2) Output Cropping and 3) Gamma Correction. Equalization and input cropping can provide particularly striking benefits to a CDMA delay spread profile image. Input cropping removes a large percentage of random signal chara ⁇ eristics that are non-recurring.
- filters and/or filter combinations can be used to help distinguish between stationary and variable clutter affecting multipath signals. For example, it is desirable to reject multipath fingers associated with variable clutter, since over a period of a few minutes such fingers would not likely recur. Further filtering can b ⁇ used to remove recurring (at least during the sample period), and possibly strong but narrow "pencils" of RF energy.
- a narrow pencil image component could be represented by a near perfe ⁇ reflective surface, such as a n ⁇ arby metal panel truck stopp ⁇ d at a traffic light.
- stationary clutter obje ⁇ s such as concrete and glass building surfaces, adsorb some radiation b ⁇ for ⁇ continuing with a reflected ray at some d ⁇ lay.
- Such stationary clutter-affected CDMA fingers are more likely to pass a 4X4 neighbor Median filter as well as a 40 to 50 percent Input Crop filter, and are thus more suited to neural net pattern recognition.
- pencil-shaped fingers are delet ⁇ d.
- Other filtering methods include custom linear filtering, adaptive (Weiner) filtering, and custom nonlinear filtering.
- the DSP 17 may provide data emsemble results, such as extracting the shortest time delay with a detectable relative signal strength, to the router/distributor 23, or alternatively results may b ⁇ proc ⁇ ssed via one or more image filters 19, with subsequent transmission to th ⁇ router/distributor 23.
- the router/distributor 23 examines the processed message data from the DSP 17 and stores routing and distribution information in the message header. The router/distributor 23 then forwards the data messages to the output queue 21, for subsequent queuing then transmission to the appropriate location ⁇ stimator FOMs.
- the location center 142 computes location estimates for a wireless Mobile Station 140 (denoted the “target MS” or “MS”) by performing the following steps:
- target MS location data can be generated that is uniform and consistent with location data generated from other target MSs 140.
- uniformity and consistency is both in terms of data structures and interpretation of signal chara ⁇ eristic values provided by th ⁇ MS location data;
- FOMs Transform ⁇ model estimating models
- each such model may use the input target MS location data for generating a "location hypothesis" providing an estimat ⁇ of th ⁇ location of the target MS 140;
- the location estimate is provided in a data stru ⁇ ure (or obje ⁇ class) denot ⁇ d as a "location hypothesis" (illustrated in Table LH-I).
- a location hypothesis illustrated in Table LH-I.
- this MS 140 may be substantially on a cell boundary, this cov ⁇ ring may, in som ⁇ cases, include more than one cell.
- extrapolatio ⁇ _area Ref ⁇ r ⁇ nc ⁇ to (if non-NULL) an extrapolated MS target estimate area provided by the location extrapolator submodule 1432 of the hypothesis analyzer 1332. That is, this field, if non-NULL, is an extrapolation of the "image area” field if it ⁇ xists, otherwise this field is an extrapolation of the "area est" field. Note other extrapolation fields may also b ⁇ provided depending on the embodiment of the present invention, such as an extrapolation of the "pt cov ⁇ ring".
- each location hypothesis data stru ⁇ ure includes at least one measurement, denoted hereinafter as a confidence value (or simply confidence), that is a measurement of the perc ⁇ iv ⁇ d likelihood that an MS location estimate in the location hypothesis is an accurate location estimate of the target MS 140. Since such confidence values are an important aspe ⁇ of the present invention, much of the description and use of such confidence values are d ⁇ scrib ⁇ d below; however, a brief description is provided here. Each such confidence value is in the range -1.0 to 1.0, wherein the larger the value, the greater the perceived likelihood that the target MS 140 is in (or at) a corresponding MS location estimate of the location hypothesis to which the confidenc ⁇ valu ⁇ applies.
- a location hypothesis may have more than on ⁇ MS location estimate (as will be discussed in detail below) and the confidence value will typically only correspond or apply to one of the MS location estimates in the location hypothesis.
- values for the confidence value field may be interpreted as: (a) -1.0 may be interpreted to mean that the target MS 140 is NOT in such a corresponding MS area estimat ⁇ of th ⁇ location hypothesis area, (b) 0 may be interpreted to mean that it is unknown as to the likelihood of whether the MS 140 in the corresponding MS area estimat ⁇ , and (c) + 1.0 may b ⁇ interpreted to mean that the MS 140 is perceived to positively be in the corresponding MS area estimat ⁇ .
- the location hypothesis data stru ⁇ ure may also include other related "p ⁇ rc ⁇ ption" m ⁇ asurements related to a likelihood of the target MS 140 b ⁇ ing in a particular MS location area estimate.
- other related "p ⁇ rc ⁇ ption" m ⁇ asurements related to a likelihood of the target MS 140 b ⁇ ing in a particular MS location area estimate For example, it is within the scope of the present invention to also utilize measurements such as, (a) "sufficiency factors" for indicating the likelihood that an MS location estimate of a location hypothesis is sufficient for locating the target MS 140; (b) "necessity factors” for indicating the necessity that the target MS be in an particular area estimate.
- a single confidenc ⁇ field is used having the interpretation given above.
- a confidence score, CS can be assigned to A, wherein the confidence score for such an area is a fun ⁇ ion of the confidences (both positiv ⁇ and n ⁇ gative) for all the location hypotheses whose (most pertinent) target MS location ⁇ stimates contain A. That is, in ord ⁇ r to determine a most likely target MS location area estimate for outputting from the location c ⁇ nter 1 2, a confidence score is det rmin ⁇ d for areas within the location cent ⁇ r service area.
- of location hypotheses H ; i l,2,...,N, with CS contained in the area estimate for H computer then is denoted a confidence score fun ⁇ ion.
- th ⁇ re are many ⁇ mbodiments for a confidenc ⁇ score fun ⁇ ion f that may be utilized in computing confidenc ⁇ scores with th ⁇ present invention; e.g.,
- the fun ⁇ io ⁇ f as defined in (c) immediately above is utilized.
- the simpler confidence score fun ⁇ ion of (a) may be more useful. It is important to note, though, that it is within the scope of the present invention to use other functions for the confidence score fun ⁇ ion.
- the primary wireless signaling chara ⁇ eristics for categorizing areas into at least minimally similar area types are: th ⁇ rmal noise and, more importantly, multipath characteristics (e.g., multipath fade and time delay).
- Th ⁇ pr ⁇ s ⁇ t inv ⁇ ntion provides such a determination by utilizing a novel notion of area typ ⁇ , hereinafter denoted "transmission area type” (or, “(transmission) area type” when both a generic area type classification scheme and the transmission area type discussed hereinafter are intended) for classifying "similar" areas, wherein each transmission area type class or category is intended to describe an area having at l ⁇ ast minimally similar wireless signal transmission chara ⁇ eristics.
- the novel transmission area type scheme of the present invention is based on: (a) the terrain area classifications; e.g., the terrain of an area surrounding a target MS 140, (b) the configuration of base stations 122 in the radio coverage area 120, and (c) characterizations of the wireless signal transmission paths between a targ ⁇ t MS 140 location and th ⁇ base stations 122.
- a partition (denoted h ⁇ reinafter as P 0 ) is imposed upon the radio coverage area 120 for partitioning for radio coverag ⁇ area into subareas, wherein each subarea is an estimate of an area having included MS 140 locations that are likely to have is at least a minimal amount of similarity in their wireless signaling characteristics.
- a first such colle ⁇ ion may be (for the forward transmission path) the a ⁇ ive set of base stations 122, or, th ⁇ union of th ⁇ a ⁇ ive and candidate s ⁇ ts, or, th ⁇ union of th ⁇ a ⁇ ive, candidate and/or remaining sets of base stations 122 dete ⁇ ed by "most" MSs 140 in .
- a second such colle ⁇ ion may be the base stations 122 that are expe ⁇ ed to dete ⁇ MSs 140 at locations within the subarea.
- the union or intersection of the first and second colle ⁇ ions is also within the scope of the present invention for partitioning the radio coverage area 120 according to (d) above.
- base station 122 power levels will be substantially constant. Howev ⁇ r, ⁇ v ⁇ n if this is not th ⁇ cas ⁇ , one or more colle ⁇ ions for (d) above may be determined empirically and/or by computationally simulating the power output of each bas ⁇ station 122 at a predetermined lev ⁇ l. Moreover, it is also worth mentioning that this step is relatively straightforward to implement using the data stored in the location signature data base 1320 (i.e., the verified location signature clusters discussed in detail hereinb ⁇ low). Denote the resulting partition here as P t . (23.8.42) Partition the radio coverage area 120 into subareas, wherein each subarea appears to have substantially homogeneous terrain chara ⁇ eristics.
- this may be performed periodically substantially automatically by scanning radio coverage area images obtained from aerial or satellite imaging.
- EarthWatch Inc. of Longmont, CO can provide geographic with 3 meter resolution from satellite imaging data.
- the resulting partition here as P 2 . (23.8.43)
- P 0 P, inters ⁇ ct P j
- P 0 subareas P 0 subareas
- the subareas of P 0 are provided with a first classification or categorization as follows: (23.8.4.4) Determine an area type categorization scheme for the subareas of P,.
- a subarea, A, of P may be categoriz ⁇ d or labeled according to the number of base stations 122 in each of the collections used in (23.8.4.1 )(d) above for determining subareas of P,.
- ⁇ ach category may correspond to a single number x (such as 3), wherein for a subarea, A, of this category, there is a group of x (e.g., three) base stations 122 that are expected to be detected by a most target MSs 140 in the area A.
- each category may correspond to a triple: of numbers such as (5, 2, 1), wherein for a subarea A of this category, there is a common group of 5 base stations 122 with two-way signal detection expected with most locations (e.g., within a first or second deviation) within A, there are 2 base stations that are expected to be detected by a target MS 140 in A but these base stations can not detect the target MS, and there is one base station 122 that is expe ⁇ ed to be able to dete ⁇ a target MS in A but not be dete ⁇ ed. (23.8.4.5) D ⁇ t ⁇ rmine an area type categorization scheme for the subareas of P 2 .
- th ⁇ subareas of P 2 may b ⁇ categorized according to their similarities.
- such categories may be somewhat similar to th ⁇ naive area types mentioned above (e.g., dense urban, urban, suburban, rural, mountain, etc.).
- categorizations may be used, such as a category for all areas having between 20,000 and 30,000 square feet of vertical area chang ⁇ per 11,000 square fe ⁇ t of horizontal area and also having a high traffic volume (such a category likely corresponding to a "moderately dens ⁇ urban" area type).
- a generally triangular shaped area as the transmission path wherein a first vertex of this area is at the corresponding base station for th ⁇ transmission path, and th ⁇ sid ⁇ s of th ⁇ generally triangular shaped defining the first vertex have a smallest angle between them that allows A to be completely between th ⁇ se sides.
- time varying chara ⁇ eristics of (23.82) may be incorporated in the transmission area type frame work by generating multiple ve ⁇ ions of the transmission area types such that the transmission area type for a given subarea of P 0 may change depending on the combination of time varying environmental chara ⁇ eristics to b ⁇ considered in the transmission area types. For instance, to account for seasonality, four ve ⁇ ions of the partitions Pi and P 2 may be generated, one for each of the seasons, and subsequently generate a (potentially) different partition P 0 for each season. Further, the type and/or chara ⁇ eristics of base station 122 antennas may also be included in an ⁇ mbodiment of the transmission area type.
- MS location processing performed by the location center 142 should become increasingly better at locating a target MS 140 both by (a) building an increasingly more detailed model of the signal chara ⁇ eristics of locations in the serv e area for the present invention, and also (b) by providing capabilities for the location center processing to adapt to environmental changes.
- One way these aspects of the present invention are realized is by providing one or more data base anagem ⁇ nt systems and data bases for:
- location information data bases 1232 include a data base for providing training and/or calibration data to one or more trainable/calibratable FOMs 1224, as well as an archival data base for archiving historical MS location information related to the performance of the FOMs.
- data bases will be discussed as necessary hereinbelow.
- archival data base is provided here. Accordingly, the term, "location signature data base" is us ⁇ d hereinafter to denote the archival data base and/or data base management system depending on the context of th ⁇ discussion.
- the location signature data base (shown in, for example, Fig.6 and labeled 1320) is a repository for wireless signal chara ⁇ eristic data derived from wireless signal communications between an MS 140 and one or more base stations 1 2, wherein the corresponding location of the MS 140 is known and also stored in the location signature data base 1320. More particularly, the location signature data base 1320 associates ⁇ ach such known MS location with the wireless signal chara ⁇ eristic data derived from wireless signal communications between the MS 140 and one or more base stations 122 at this MS location. Accordingly, it is an aspe ⁇ of th ⁇ present invention to utilize such historical MS signal location data for enhancing the corr ⁇ ness and/or confidence of certain location hypotheses as will be described in detail in other sections below.
- each such (verified) location signature describes the wireless signal characteristic measurements between a given base station (e.g., BS 122 or LBS 152) and an MS 140 at a (verified or known) location associated with the (verified) location signature. That is, a verified location signature corresponds to a location whose coordinates such as latitude-longitude coordinates are known, whil ⁇ simply a location signature may have a known or unknown location corresponding with it.
- a verified location signature corresponds to a location whose coordinates such as latitude-longitude coordinates are known, whil ⁇ simply a location signature may have a known or unknown location corresponding with it.
- the term (verified) location signature is also denoted by the abbreviation, "(verified) loc sig" hereinb ⁇ low;
- Each such (verifi ⁇ d) location signature cluster includes a colle ⁇ ion of (verified) location signatures corresponding to ali the location signatures between a target MS 140 at a (possibly verified) presumed substantially stationary location and each BS (e.g., 1 2 or 152) from which th ⁇ targ ⁇ t MS 140 can dete ⁇ the BS's pilot channel gardless of th ⁇ classification of the BS in the target MS (i.e., for CDMA, regardless of whether a BS is in the MS's a ⁇ ive, candidate or remaining bas ⁇ station sets, as one skilled in the art will understand). Note that for simplicity here, it is presumed that each location signature cluster has a single fixed primary base station to which the target MS 140 synchronizes or obtains its timing;
- Composite location objects Each such entity is a more general entity than the verifi ⁇ d location signature cluster.
- An obje ⁇ of this type is a collection of (verified) location signatures that are associated with th ⁇ same MS 140 at substantially the same location at the same time and each such loc sig is associated with a different base station. Howev ⁇ r, there is no requirement that a loc sig from each BS 122 for which the MS 140 can dete ⁇ the BS's pilot channel is included in the "composite location obje ⁇ (or entity)"; and
- MS location estimation data that includes MS location estimates output by one or more MS location estimating fi ⁇ t order models 1224, such MS location estimate data is described in detail hereinbelow.
- a loc sig is, in one embodiment, an instance of the data structure containing the signal characteristic measurements output by the signal filtering and normalizing subsystem also denoted as the signal processing subs/stem 1220 describing the signals between: (i) a specific base station 122 (BS) and (n) a mobile station 140 (MS), wherein the BS's location is known and the MS's location is assumed to be substantially constant (during a 2-5 second interval in one embodiment of the present invention), during communication with the MS 140 (or obtaining a single instance of loc sig data, although the MS location may or may not be known, further, for notational purposes, the BS 122 and the MS 140 for a loc sig hereinafter will be denoted the "BS associated with the loc sig", and the "MS associated with the loc sig" respectively. Moreover, the location of the MS 140 at the time the loc sig data is obtained will be denoted the "location associated with the loc sig" (this location possibly being unknown). In particular, for
- MS type the make and model of the target MS 140 associated with a location signature instantiation; note that the type of MS 140 can also be derived from this entry; e.g., whether MS 140 is a handset MS, car-set MS, or an MS for location only. Not ⁇ as an aside, for at least CDMA, the type of MS 140 provides information as to the number of fingers that may be measured by the MS, as one skilled in the will appreciate.
- BS id an identification of the base station 122 (or, location base station 152) communicating with the target MS;
- MS Joe a representation of a geographic location (e.g., latitude-longitude) or area representing a verifrcd/known MS location wh ⁇ re signal chara ⁇ eristics between the associated (location) base station and MS 140 were received. That is, if the "verified Jlag" attribute (discussed below) is TRUE, then this attribute includes an estimated location of the target MS. If verified Jlag is FALSE, then this attribute has a value indicating "location unknown”.
- MS Joe may include the following two subfields: an area within which th ⁇ targ ⁇ t MS is presumed to be, and a point location (e.g., a latitud ⁇ and longitude pair) wh ⁇ re the target MS is presumed to be (in on ⁇ embodiment this is the centroid of the area);
- a point location e.g., a latitud ⁇ and longitude pair
- BS id is the current primary base station for th ⁇ target MS
- similarity in this context may be operationaiized by at least designating a geographic proximity of a loc sig in which to determine if it is similar to other loc sigs in this designated geographic proximity and/or area type (e.g, transmission area typ ⁇ as elsewhere herein).
- area type e.g, transmission area typ ⁇ as elsewhere herein.
- environmental characteristics may also be used in determining similarities such as: similar time of occurrence (e.g., of day, and/or of month), similar w ⁇ ath ⁇ r ( ⁇ .g., snowing, raining, ⁇ tc).
- similar time of occurrence e.g., of day, and/or of month
- similar w ⁇ ath ⁇ r ⁇ .g., snowing, raining, ⁇ tc
- these latter characteristics are different from the notion of geographic proximity since proximity may be only a distance measurement about a location.
- a loc sig having a confidence fa ⁇ or value below a predetermined threshold may not be used in evaluating MS location hypotheses
- the signal topography chara ⁇ eristics retained can be represented as chara ⁇ eristics of at least a two-dimensional generated surface. That is, such a surface is generated by the signal processing subsystem 1220 from signal chara ⁇ eristics accumulated over (a relatively short) time interval.
- the dimensions for the generated surface may be, for example, signal strength and time delay. That is, the accumulations over a brief time interval of signal chara ⁇ eristic measurements between the BS 122 and the MS 140 (associated with the loc sig) may be classified according to the two signal chara ⁇ eristic dimensions (e.g., signal strength and corresponding time delay).
- each cell correspondi to a different range of signal strengths and time delays a tally of the number of samples falling in the range of each cell can be maintained. Accordingly, for each cell, its corresponding tally may be interpreted as height of the cell, so that when the heights of all cells are considered, an undulating or mountainous surface is provided.
- the "mountainous surface" is believed to, under most circumstances, provide a contour that is substantially unique to the location of the target MS 140.
- th ⁇ signal samples are typically obtained throughout a predetermined signal sampling time interval of 2-5 seconds as is discussed elsewhere in this specification.
- the signal topography chara ⁇ eristics retained for a loc sig include certain topographical chara ⁇ eristi ⁇ of such a generated mountainous surface.
- each loc sig may include: for each local maximum (of the loc sig surface) above a predetermined noise ceiling threshold, the (signal strength, time delay) coordinates of the cell of the local maximum and the corresponding height of th ⁇ local maximum.
- certain gradients may also be included for chara ⁇ erizing the "steepness" of the surface mountains.
- a frequency may also be associated with each local maximum.
- th ⁇ data retained for each selected local maximum can include a quadruple of signal strength, time delay, height and frequency. Further note that the data types here may vary. However, for simplicity, in parts of th ⁇ description of loc sig processing related to the signal chara ⁇ eristi ⁇ here, it is assumed that the signal chara ⁇ eristic topography data stru ⁇ ure here is a vetto ⁇
- quality_obj signal quality (or error) measurements, e.g., Eb/No values, as one skilled in the art will understand;
- ⁇ oise_ceiling noise ceiling values used in the initial filtering of noise from the signal topography chara ⁇ eristi ⁇ as provided by the signal processing subsystem 1220;
- powerjevel power levels of the base station (e.g 122 or 152) and MS 140 for the signal measurements;
- timing error an estimated (or maximum) timing error between the present (associated) BS (e.g., an infrastru ⁇ ure base station 122 or a location base station 152) detecting the target MS 140 and the current primary BS 122 for the target MS 140. Note that if the BS 122 associated with the loc sig is the primary bas ⁇ station, th ⁇ n the value here will be zero;
- cluster_pt ⁇ a pointer to the location signature composite entity to which this loc sig belongs.
- each verified loc sig is designated as either "repeatable” or "random”.
- a loc sig is repeatable if th ⁇ (v ⁇ rifi ⁇ d/known) location associated with the loc sig is such that signal chara ⁇ eristic measurements between the associated BS 122 and this MS can be either replaced at periodic time intervals, or updated substantially on d ⁇ mand by most recent signal characteristic measurements between the associated base station and the associated MS 140 (or a comparable MS) at the verified/known location.
- Repeatable loc sigs may be, for exampl ⁇ , provided by stationary or fixed location MSs 140 (e.g., fixed location transceivers) distributed within certain areas of a geographical region serviced by the location center 142 for providing MS location estimates. That is, it is an aspe ⁇ of the present invention that each such stationary MS 140 can be contacted by the location center 142 (via the base stations of the wireless infrastru ⁇ ure) at substantially any time for providing a new colle ⁇ ion (L ⁇ ., cluster ) of wireless signal chara ⁇ eristics to be associated with the verified location for the transceiver.
- stationary or fixed location MSs 140 e.g., fixed location transceivers
- repeatable loc sigs may be obtained by, for example, obtaining location signal measurements manually from work ⁇ rs who regularly traverse a predetermined route through some portion of the radio coverage area; i.e, postal workers (as will be described in more detail hereinbelow).
- a loc sig is random if the loc sig is not repeatable. Random loc sigs are obtained, for exampl ⁇ , from verifying a previously unknown target MS location once the MS 140 has been located. Such verifications may be accomplished by, for example, a vehicle having one or more location v ⁇ rifying devices such as a GPS receiver and/or a manual location input capability becoming sufficiently close to the located targ ⁇ t MS 140 so that the location of the vehicle may be associated with the wireless signal chara ⁇ eristics of the MS 140.
- location v ⁇ rifying devices such as a GPS receiver and/or a manual location input capability becoming sufficiently close to the located targ ⁇ t MS 140 so that the location of the vehicle may be associated with the wireless signal chara ⁇ eristics of the MS 140.
- Vehicles having such location dete ⁇ ion devices may include: (a) vehicles that travel to locations that are primarily for another purpose than to verify loc sigs, e.g., police cars, a bulanc ⁇ s, fire trucks, rescue units, couri ⁇ r services and taxis; and/or (b) vehid ⁇ s whose primary purpose is to verify loc sigs; e.g., location signal calibration vehicles. Additionally, vehicles having both wireless transceivers and location verifying devices may provide the location center 142 with random loc sigs. Note, a repeatable loc sig may become a random loc sig if an MS 140 at the location associated with the loc sig becomes undetectable such as, for exampl ⁇ , wh ⁇ n the
- MS 140 is removed from its verifi ⁇ d location and therefore the loc sig forthe location can not be readily updated.
- such a first surface may be generated for the (forward) signals from the base station 122 to the targ ⁇ t MS 140 and a second such surface may be generated for (or alternatively, the first surface may be enhanc ⁇ d by increasing its dimensionality with) the signals from the MS 140 to the base station 122 (denoted the reverse signals).
- location hypothesis may include an estimated error as a measurement of perceived accuracy in addition to or as a substitute for the confidence field discussed hereinabove.
- location hypotheses may also include a text field for providing a reason for the values of one or more of the location hypothesis fields. For example, this text field may provide a reason as to why th ⁇ confidence value is low, or provide an indication that the wireless signal measurements used had a low signal to noise ratio.
- Loc sigs have the following fun ⁇ ions or obje ⁇ methods associated therewith: (26.1) A "normalization" method for normalizing loc sig data according to the associated MS 140 and/or BS 122 signal processing and generating chara ⁇ eristics.
- the signal processing subsystem 1220 one embodim ⁇ nt b ⁇ ing described in the PCT patent application titled, "Wireless Location Using A Plurality of Commercial Network Infrastru ⁇ ures," by F. W. LeBlanc and the present i ⁇ ventor(s), provid ⁇ s (m ⁇ thods for loc sig objects) for "normalizing" each toe sig so that variations in signal chara ⁇ eristi ⁇ resulting from variations in (for example) MS signal processing and generating characteristi of different types of MS's may be reduced.
- the normalization methods provided here transform the loc sig data so that it appears as though the loc sig was provided by a common hand set MS 140.
- other methods may also be provided to "normalize" a loc sig so that it may be compared with loc sigs obtained from oth ⁇ r types of MS's as well.
- normalization techniques include, for example, interpolating and extrapolating according to power levels so that loc sigs may b ⁇ nor aliz ⁇ d to th ⁇ same power level for, c.g., comparison purposes.
- Normalization for the BS 122 associated with a loc sig is similar to the normalization for MS signal processing and generating chara ⁇ eristics.
- the signal processing subsystem 1220 provides a loc sig method for "normalizing" loc sigs according to base station signal processing and generating characteristics.
- loc sigs stored in the location signature data base 1320 are NOT “normalized” according to either MS or BS signal processing and generating chara ⁇ eristics. That is, "raw" values of the wireless signal chara ⁇ eristics are stored with each loc sig in the location signature data base 1320. (262) A ⁇ thod for determining the "area type" corresponding to the signal transmission chara ⁇ eristi ⁇ of the area(s) between th ⁇ associated
- such an area type may be designated by, for exampl ⁇ , the techniques for determining transmission area types as described hereinabove.
- each composite io ⁇ tion obje ⁇ includes a bit string having a corresponding bit for each base station, wherein a "I" for such a bit indi ⁇ t ⁇ s that the corresponding base station was identified by the MS, and a "0" indicates that the base station was not identified.
- additional lo ⁇ tion signal m ⁇ asurements may also be included from other non-primary base stations.
- the target MS 140 may communicate with other base stations than it's primary base station.
- timing for the MS 140 is typically derived from it's primary base station and since timing synchronization between bas ⁇ stations is not exa ⁇ (e.g., in the cas ⁇ of CDMA, timing variations may be plus or minus I microsecond)at least some of the lo ⁇ tion signal measurements may be less reliable that the measurements from the primary base station, unless a forced hand-off technique is used to eliminate system timing errors among relevant base stations; (27.1.4) a completeness designation that indicates whether any loc sigs for the composite lo ⁇ tion obje ⁇ have be ⁇ n remov ⁇ d from (or invalidated in) the location signature data bas ⁇ 1320.
- a verified composite location obje ⁇ is designated as "incomplete” if a loc sig initially referenced by the verifi ⁇ d composite lo ⁇ tion obje ⁇ is deleted from the lo ⁇ tion signature data base 1320 ( ⁇ .g., be ⁇ use of a confidence that is too low). Further note that if ail loc sigs for a composite lo ⁇ tion obje ⁇ are d ⁇ leted, then the composite obje ⁇ is also deleted from the lo ⁇ tion signature data base 1320. Also not ⁇ that common fields between loc sigs referenced by the same composite lo ⁇ tio ⁇ obje ⁇ may be provided in the composite location obje ⁇ only (e.g., timesta p, etc.). Accordingly, a composite io ⁇ tion obje ⁇ that is complete (i.e., not incomplete) is a verified lo ⁇ tion signature cluster as described in
- Fig.5 presents a high level diagram of the lo ⁇ tio ⁇ center 142 and the lo ⁇ tion engine 139 in the context of the infrastru ⁇ ure for the entire io ⁇ tion system of th ⁇ present invention.
- the archite ⁇ ure for the lo ⁇ tion center 142 and the lo ⁇ tion engine 139 provided by the present invention is designed for extensibility and flexibility so that MS 140 lo ⁇ tion accuracy and reliability may b ⁇ ⁇ nha ⁇ ced as further lo ⁇ tion data become available and as enhanced MS lo ⁇ tion techniques become available.
- th ⁇ high level archite ⁇ ure for generating and processing MS lo ⁇ tio ⁇ estimates may be considered as divided into the following high lev ⁇ l functional groups described hereinbelow.
- a first fun ⁇ ional group of lo ⁇ tion engine 139 modules is for performing signal processing and filtering of MS lo ⁇ tion signal data received from a conventional wireless (e.g., CDMA) infrastru ⁇ ure, as discussed in the steps (23.1) and (232) above.
- This group is denot ⁇ d the signal processing subsystem 1220 herein.
- One embodiment of such a subsystem is described in the PQ patent appli ⁇ tion titled, "Wireless Lo ⁇ tion Using A Plurality of Commercial Network Infrastru ⁇ ures," by F. W. LeBlanc and the present i ⁇ ventor(s)..
- a second functional group of lo ⁇ tion engine 139 modules is for generating various target MS 140 lo ⁇ tion initial estimates, as described in step (233). Accordingly, the modules here use input provided by the signal processing subsystem 1220.
- This second fun ⁇ ional group includes one or more signal analysis modules or models, each hereinafter denoted as a first order model 1224 (FOM), for generating lo ⁇ tion hypotheses fora target MS 140 to be lo ⁇ ted. Note that it is int ⁇ nd ⁇ d that each such FOM 1224 use a different technique for determining a lo ⁇ tion area estimate for the target MS 140.
- a brief description of some types of first order models is provided immediately below. Note that Fig.8 illustrates another, more detail view of the lo ⁇ tion system for the present invention.
- this figure illustrates some of the FOMs 1224 contemplated by the present invention, and additionally illustrates the primary communi ⁇ tions with other modules of the lo ⁇ tion system for the present invention.
- the present invention is not limited to the FOMs 1224 shown and discussed herein. That is, it is a primary aspe ⁇ of the present invention to easily incorporate FOMs using oth ⁇ r signal processing and/or computational lo ⁇ tion estimating techniques than those presented herein.
- each FOM type may have a plurality of its models incorporated into an embodiment of the present invention.
- distance models 1224 may be based on a range or distance computation and/or on a base station signal reception angle determination between the target MS 1 0 from each of one or more base stations.
- distance models 1224 determine a lo ⁇ tion ⁇ stimate of the target MS 140 by determining a distance offset from each of one or more base stations 122, possibly in a particular dire ⁇ ion from each (some of) the base stations, so that an interse ⁇ ion of each area locus defined by the base station offsets may provide an estimate of the lo ⁇ tion of the target MS.
- Distance model FOMs 1224 may compute such offsets based on: (a) signal timing measurements between the target mobile station 140 and one or more base stations 122; e.g.., timing measurements such as time difference of arrival (TDOA), or time of arrival (TOA). Note that both forward and reverse signal path timing measurements may be utilized;
- TDOA time difference of arrival
- TOA time of arrival
- a distance model may utilize trianguiation or trilateration to compute a lo ⁇ tio ⁇ hypoth ⁇ sis having either an area lo ⁇ tion or a point lo ⁇ tion for an estimat ⁇ of the target MS 140.
- lo ⁇ tion hypothesis may include an estimated error
- FOM 1224 is a statistically bas ⁇ d first order model 1224, wherein a statistical technique, such as regression techniques (e.g., least squares, partial least squares, principle decomposition), or e.g., Bollenger Bands (e.g., for computing minimum and maximum base station offsets).
- models of this type output location hypotheses determined by performing one or more statistical techniques or comparisons between the verified io ⁇ tion signatures in io ⁇ tion signature data base 1320, and the wireless signal measurements from a target MS. Models of this type are also referred to hereinafter as a "stochastic signal (first order) model” or a “stochastic FOM” or a "statistical model.”
- Still another type of FOM 1224 is an adaptive learning model, such as an artificial neural net or a genetic algorithm, wherein the FOM may be trained to recognize or associate ⁇ ach of a plurality of locations with a corresponding set of signal chara ⁇ eristics for communi ⁇ tions between the target MS 140 (at the lo ⁇ tion) and the base stations 122. Moreover, typically such a FOM is expe ⁇ ed to accurately interpolate/extrapolate targ ⁇ t MS 140 lo ⁇ tion estimat ⁇ s from a set of signal chara ⁇ eristi ⁇ from an unknown target MS 1 0 lo ⁇ tion.
- an adaptive learning model such as an artificial neural net or a genetic algorithm
- Models of this typ ⁇ are also referred to hereinafter variously as “artificial n ⁇ ural net mod ⁇ ls” or “n ⁇ ural net models” or “trainable mod ⁇ ls” or “learning models.”
- FOM 1224 a related type of FOM 1224 is based on pattern recognition. These FOMs an recognize patterns in the signal chara ⁇ eristi ⁇ of communi ⁇ tions betw ⁇ en the target MS 140 (at the lo ⁇ tion) and the base stations 122 and thereby estimate a lo ⁇ tio ⁇ area of the target MS. However, such FOMs may not be trainable.
- FOM 1224 an be based on a colle ⁇ ion of dispersed low power, low cost fixed lo ⁇ tion wireless transceivers (also denoted “lo ⁇ tion base stations 152" hereinabove) that are provided for detecting a target MS 140 in areas where, e.g., there is insufficient base station 122 infrastru ⁇ ure coverage for providing a desired level of MS 140 lo ⁇ tion accuracy.
- a target MS 140 in areas where, e.g., there is insufficient base station 122 infrastru ⁇ ure coverage for providing a desired level of MS 140 lo ⁇ tion accuracy.
- lo ⁇ tion base stations 152 populating an area where the target MS 140 is presumed to be, then by a ⁇ ' ivating these lo ⁇ tion base stations 152, evidence may be obtained as to whether or not the target MS is a ⁇ ually in the area; e.g., if the target MS 140 is dete ⁇ ed by a location base station 152, then a corresponding lo ⁇ tion hypothesis having a lo ⁇ tion estimate corresponding to the coverage area of the lo ⁇ tion base station may have a very high confidence value.
- a corresponding lo ⁇ tion hypothesis having a lo ⁇ tion estimate corresponding to the coverage area of the lo ⁇ tion base station may have a very low confidence value. Models of this type are referred to hereinafter as "lo ⁇ tion base station models.”
- FOM 1224 n be based on input from a mobile base station 148, wherein lo ⁇ tio ⁇ hypotheses may be generated from target MS 140 lo ⁇ tion data received from the mobile base station 148. Still other types of FOM 1224 n be based on various techniques for recognizing wireless signal measurement patterns and associating particular patterns with lo ⁇ tio ⁇ s in the coverage area 120. For example, artificial n ⁇ ural networks or oth ⁇ r learning models n used as the basis for various FOMs.
- a novel aspe ⁇ of the present invention is the simultaneous use or a ⁇ ivation of a potentially large number of such first order models 1224, wherein such FOMs are not limited to those described herein.
- the present invention provides a framework for incorporating MS lo ⁇ tio ⁇ estimators to be subsequently provided as new FOMs in a straightforward manner.
- a FOM 1224 based on wireless signal time delay measurements from a distributed antenna system for wireless communication may b ⁇ incorporated into th ⁇ present invention for bating a target MS 140 in an enclosed area serviced by the distributed antenna system.
- the present invention may determine the floor of a multi-story building from which a targ ⁇ t MS is transmitting.
- MSs 140 an b ⁇ bated in three dimensions using such a distributed antenna FOM.
- n also b ⁇ used for bating a target MS 140.
- the device registers with a home location register of the public switched telephone network when there is a status change such as from not dete ⁇ ing the corresponding MS to det ⁇ i ⁇ g th ⁇ MS, or visa versa, as one skilled in the an will understand. Accordingly, by providing a FOM that accesses the MS status in the home lo ⁇ tion register, the location engine 139 an determine whether the MS is within signaling range of the home base station or not, and generate lo ⁇ tion hypotheses accordingly. Moreover, other FOMs based on, for example, chaos theory and/or fractal theory are also within the scope of the present invention.
- Each such first ord ⁇ r mod ⁇ l 1224 may b ⁇ relativ ⁇ ly ⁇ asily incorporat ⁇ d into and/or removed from the present invention.
- the signal processing subsystem 1220 provides uniform input to the FOMs, and there is a uniform FOM output interface, it is believed that a large majority (if not substantially all) viable MS lo ⁇ tio ⁇ estimation strategies may be accommodated.
- a large majority (if not substantially all) viable MS lo ⁇ tio ⁇ estimation strategies may be accommodated.
- Each such first order model 1224 may be relatively simple and still provide significant MS 140 bating functionality and predictability.
- the present invention is modular and extensible such that, for example, (and importantly) different first order models 1224 may be utilized depending on the signal transmission chara ⁇ eristics of the geographic region serviced by an e bodim ⁇ nt of the present invention.
- a simple configuration of the present invention may have a small number of FOMs 1224 for a simple wireless signal environment (e.g., flat terrain, no urban ⁇ nyons and low population density).
- a large number of FOMs 1224 may be simultaneously utilized for generating MS lo ⁇ tb ⁇ hypotheses.
- a third fun ⁇ ional group of b ⁇ tion engine 139 modules evaluates location hypothes ⁇ s output by the first order mod ⁇ ls 1224 and thereby provides a "most likely" target MS lo ⁇ tion estimat ⁇ .
- the modules for this fun ⁇ ional group are colle ⁇ irety denoted th ⁇ hypoth ⁇ sis evaluator 1228.
- a primary purpose of the hypothesis evaluator 1228 is to mitigate conflicts and ambiguities related to lo ⁇ tion hypotheses output by th ⁇ first order models 1224 and thereby output a "most likely" estimate of an MS for which th ⁇ re is a request for it to b ⁇ bated.
- the hypothesis evaluator there are various related embodiments of the hypothesis evaluator that are within th ⁇ scop ⁇ of th ⁇ pres ⁇ nt inv ⁇ ntion.
- Sinc ⁇ each lo ⁇ tion hypothesis includ ⁇ s both an MS lo ⁇ tio ⁇ area estimate and a corresponding confidence value indicating a perceived confidence or likelihood of the target MS being within the corresponding lo ⁇ tion area estimate, there is a monoton relationship between MS lo ⁇ tio ⁇ area estimates and confidence values.
- the corresponding confidence value may also be increased (in an extreme case, the lo ⁇ tion area estimate could be the entire coverag ⁇ area 120 and thus the confidenc ⁇ value may likely correspond to the high ⁇ st l ⁇ vel of certainty; i.e., + 1.0). Accordingly, given a target MS lo ⁇ tion area estimate (of a lo ⁇ tion hypothesis), an adjustment to its accuracy may be performed by adjusting the MS lo ⁇ tion area estimat ⁇ and/or the corresponding confidence value. Thus, if the confidence value is, for exampl ⁇ , excessively low then the area estimate may be increased as a technique for increasing the confidenc ⁇ valu ⁇ .
- the estimated area may be decreased and the confidence value also decreased.
- the location hypothesis is judged to be more (less) accurate than initially determined, then (i) the confidence value of th ⁇ lo ⁇ tb ⁇ hypoth ⁇ sis n be increased (decreased), and/or (ii) the MS lo ⁇ tio ⁇ area estimate an be decreased (increased).
- the hypothesis evaluator 1228 evaluates lo ⁇ tbn hypotheses and adjusts or modifies only their confidence values for MS lo ⁇ tion area estimates and subsequently uses these MS lo ⁇ tion estimat ⁇ s with the adjusted confidence values for determining a "most likely" MS b ⁇ tion estimate for outputting. Accordingly, the MS b ⁇ tion area estimates are not substantially modified.
- MS b ⁇ tion area in a second class of embodim ⁇ nts for th ⁇ hypothesis evaluator 1228, MS b ⁇ tion area ⁇ stimates an b ⁇ adjust ⁇ d whil ⁇ confid ⁇ nc ⁇ values remain substantially fixed.
- hybrids between the first two embodiments n also be provided. Note that the present embodiment provided herein adjusts both the areas and the confidence values.
- the hypothesis evaluator 1228 may perform any or most of the folbwing tasks: (30.1) it utilizes environmental information to improve and reconcile lo ⁇ tion hypotheses supplied by the first order models 1224.
- a basic premise in this context is that the accuracy of the individual first order models may be affe ⁇ ed by various environmental fa ⁇ ors such as, for example, the season of the year, the time of day, the weather conditions, the presence of buildings, base station failures, etc; (302) it enhances the accuracy of an initial lo ⁇ tion hypothesis generated by an FOM by using the initial lo ⁇ tion hypothesis as, essentially, a query or index into the location signature data base 1320 for obtaining a corresponding enhanced location hypothesis, wherein the enhanced location hypothesis has both an adjust ⁇ d target MS location area estimate and an adjusted confidence based on past performance of the FOM in the lo ⁇ tio ⁇ service surrounding the target MS location estimat ⁇ of the initial location hypothesis;
- verified be sigs (which were previously obtained from one or more verified locations of one or more MS's) are retrieved for an area corresponding to the b ⁇ tion area estimate of the b ⁇ tion hypothesis, and the signal chara ⁇ eristi ⁇ of these verifi ⁇ d be sigs are compared with the signal chara ⁇ eristi ⁇ used to generate the b ⁇ tion hypothesis for determining their similarities and subsequently an adjustment to the confidence of the lo ⁇ tion hypothesis (and/or the size of the lo ⁇ tion area estimate);
- the hypothesis evaluator 1228 determines if (or how well) such b ⁇ tbn hypotheses are consistent with well known physi ⁇ l constraints such as the laws of physics. For example, if the difference between a previous (most likely) lo ⁇ tion estimate of a target MS and a locatbn estimate by a current b ⁇ tbn hypothesis requires the MS to:
- the hypothesis evaluator 1228 determines consistencies and inconsistencies between b ⁇ tion hypotheses obtained from different first order models. For example, if two such lo ⁇ tion hypotheses, for substantially the same ti estamp, have estimated lo ⁇ tbn areas where the targ ⁇ t MS is likely to b ⁇ and th ⁇ s ⁇ areas substantially overlap, then the confidence in both such lo ⁇ tion hypotheses may be increased. Additionally, note that a velocity of an MS may b ⁇ determined (via d ⁇ ltas of successive b ⁇ tion hypotheses from one or more first order models) even when th ⁇ re is low confidence in the location estimates for the MS, since such deltas may, in some cases, be more reliable than the a ⁇ ual target MS lo ⁇ tion estimates;
- the hypothesis evaluator 1228 determines new (more accurate) location hypotheses from other locatbn hypotheses. For example, this module may generate new hypotheses from currently a ⁇ ive ones by decomposing a bcation hypothesis having a target MS location estimat ⁇ intersecting two radically different area types. Additionally, this module may generate location hypothes ⁇ s indi ⁇ ting areas of poor r ⁇ c ⁇ ption; and (30.7) th ⁇ hypothesis evaluator 1228 determines and outputs a most likely lo ⁇ tion hypoth ⁇ sis for a targ ⁇ t MS.
- hypothesis evaluator may a ⁇ omplish th ⁇ abov ⁇ tasks, (30.1 ) - (30.7), by empbying various data processing tools including, but not limited to, fuzzy mathe ati ⁇ , genetic algorithms, neural networks, expert systems and/or blackboard systems.
- the hypothesis evaluator 1228 includes the following four high l ⁇ vel modules for processing output lo ⁇ tion hypotheses from the first order models 1224: a context adjuster 1326, a hypothesis analyzer 1332, an MS status repository 1338 and a most likelihood estimator 1334. These four modules are briefly described hereinbelow.
- the context adjuster 1326 module ⁇ nhanc ⁇ s both th ⁇ comparability and predictability of th ⁇ lo ⁇ tbn hypotheses output by the first order models 1224.
- this module modifies lo ⁇ tion hypotheses received from the FOMs 1224 so that th ⁇ resulting lo ⁇ tio ⁇ hypothes ⁇ s output by the context adjuster 1326 may be further processed uniformly and substantially without concern as to differences in accuracy between the first order models from which lo ⁇ tion hypotheses originate.
- the context adjuster 1326 may adjust or modify various fields of the input b ⁇ tion hypotheses. In particular, fields giving target MS 140 lo ⁇ tbn estimates and/or confidence values for such estimates may be modified by the context adjuster 1326.
- this module may determine those fa ⁇ ors that are perceived to impa ⁇ the perceived accuracy ( ⁇ .g., confidence) of the b ⁇ tbn hypotheses: (a) differently between FOMs, and/or (b) with substantial effect
- environmental chara ⁇ eristi ⁇ may be taken into account here, such as time of day, season, month, weather, geographi ⁇ l area ⁇ t ⁇ gorizations (e.g., dense urban, urban, suburban, rural, mountain, etc.), area sub ⁇ tegorizatbns (e.g., heavily treed, hilly, high traffic area, etc).
- geographi ⁇ l area ⁇ t ⁇ gorizations e.g., dense urban, urban, suburban, rural, mountain, etc.
- area sub ⁇ tegorizatbns e.g., heavily treed, hilly, high traffic area, etc.
- th ⁇ embodiment described herein is simplified for illustration purposes such that only the geographi ⁇ l area categorizations are utilized in adjusting (i.e., modifying) lo ⁇ tion hypotheses. But, it is an important aspe ⁇ of the pres ⁇ nt invention that various categorizations, such as those mentioned immediately above, may be us ⁇ d for adjusting the b ⁇ tion hypotheses. That is, categories such as, for example:
- the present invention is not limited to the fa ⁇ ors explicitly mentioned here. That is, it is an asp ⁇ of the present invention to be extensible so that other environmental fa ⁇ ors of the coverage area 120 affecting the accuracy of lo ⁇ tion hypotheses may also be incorporated into the context adjuster 1326.
- the methods for adjusting b ⁇ tion hypotheses provided in this module may be generalized and thereby also utilized with multiple hypothesis computational archite ⁇ ures related to various applications wherein a terrain, surface, volume or other "geometric" interpretation (e.g., a metric space of statistical samples) may be placed on a large body of stored application data for relating hypothesized data to verifi ⁇ d data.
- a terrain, surface, volume or other "geometric" interpretation e.g., a metric space of statistical samples
- varbus techniques for "visualizing data” may provide such a geometric interpretation.
- the methods herein may be utilized in applications such as: (a) sonar, radar, x-ray or infrared identification of objects such as occurs in robotic navigation, medial image analysis, geobgi ⁇ l, and radar imaging. More generally, the novel computational paradigm of the context adjuster 1326 may be utilized in a number of applications wherein there is a large body of archived information providing verifi ⁇ d or a ⁇ ual appli ⁇ tion process data related to the past performance of the application process.
- the computational paradigm used in the context adjuster 1326 is a hybrid of a hypothesis adjuster and a data base query mechanism.
- the context adjuster 1326 us ⁇ s an input (lo ⁇ tbn) hypoth ⁇ sis both as an hypothesis and as a data base query or index into the lo ⁇ tbn signature data base 1320 for constru ⁇ ing a related but more accurate lo ⁇ tion hypothesis. Accordingly, substantial advantages are provided by this hybrid archite ⁇ ure, such as the following two advantages.
- the context adjuster 1326 reduces the likelihood that a feedback mechanism is necessary to the initial hypothesis generators (i.e., FOMs 1224) for periodically adjusting default evaluations of the goodness or confidence in the hypotheses generated. That is, since ⁇ ach hypoth ⁇ sis g ⁇ n ⁇ rat ⁇ d is, in effect, an index into a data base or archive of v ⁇ rified appli ⁇ tion ( ⁇ .g., lo ⁇ tion) data, th ⁇ context adjuster 1326, in turn, generates new corresponding hypotheses based on the a ⁇ ual or verified data retrieved from an archival data base.
- this archite ⁇ ure tends to separate the computations of the initial hypoth ⁇ sis generators (e.g., the FOMs 1224 in the present MS b ⁇ tion appli ⁇ tion) from any further processing and thereby provide a more modular, maintainable and flexible computational system.
- the initial hypoth ⁇ sis generators e.g., the FOMs 1224 in the present MS b ⁇ tion appli ⁇ tion
- the context adjuster 1326 tends to create hypotheses that are more accurate than the hypotheses generated by the initial hypotheses generators. That is, for each hypoth ⁇ sis, H, provid ⁇ d by on ⁇ of the initial hypothesis generators, G (e.g., a FOM 1224), a corresponding enhanc ⁇ d hypothesis, provided by the context adjuster 1326, is generated by mapping the past performance of G into the archived verifi ⁇ d appli ⁇ tion data (as will b ⁇ discuss ⁇ d in detail hereinbelow). In particular, the context adjuster hypothesis generation is based on the archived verifi ⁇ d (or known) performance application data that is related to both G and H.
- G e.g., a FOM 1224
- the context adjuster 1326 n For ⁇ xampl ⁇ , in the present wireless lo ⁇ tio ⁇ appli ⁇ tbn, if a FOM 1224, G, substantially consistently generates, in a particular geographical area, location hypotheses that are biased approximately 1000 feet north of the a ⁇ ual verified MS 140 lo ⁇ tion, then the context adjuster 1326 n generate corresponding hypotheses without this bias. Thus, the context adjuster 1326 tends to filter out inaccuracies in the initially generated hypotheses.
- this hybrid paradigm appli ⁇ s to oth ⁇ r domains that are not geographically based.
- this hybrid paradigm applies to many predi ⁇ ion and/or diagnostic applications for which:
- the application data and the appli ⁇ tion are dependent on a number of parameters whose values chara ⁇ erize the range of outputs for the appli ⁇ tion. That is, there is a set of parameters, p,, p j , p 3 , ... , p N from which a parameter space p. x p 2 x p 3 x ... x p N is derived whose points chara ⁇ erize the a ⁇ ual and estimated (or predi ⁇ ed) outcomes.
- such historical data may associate the predi ⁇ ed outcomes of the application with corresponding a ⁇ ual outcomes; (c) there is a metric or distance-like evaluation f un ⁇ bn that an be applied to th ⁇ parameter spac ⁇ for indicating relative closeness or accuracy of points in the parameter space, wherein the evaluation fun ⁇ ion provides a measurement of closeness that is related to the actual performance of the appli ⁇ tion.
- the MS status repository 1338 is a run-time storage manager for storing b ⁇ tbn hypothes ⁇ s from previous a ⁇ ivations of th ⁇ b ⁇ tion ⁇ ngin ⁇ 139 (as w ⁇ ll as for storing th ⁇ output "most likely" targ ⁇ t MS lo ⁇ tbn estimate(s)) so that a targ ⁇ t MS 140 may be tracked using target MS lo ⁇ tion hypothes ⁇ s from prevbus lo ⁇ tio ⁇ ⁇ gin ⁇ 139 a ⁇ ivations to determine, for exampl ⁇ , a movement of the target MS 140 between evaluations of the target MS lo ⁇ tion.
- the b ⁇ tion hypothesis analyzer 1332 adjusts confidence values of the lo ⁇ tion hypotheses, according to: (a) heuristics and/or statistical methods related to how well the signal chara ⁇ eristics for the generated target MS b ⁇ tio ⁇ hypothesis matches with previously obtained signal chara ⁇ eristi ⁇ for verified MS locations. (b) heuristics related to how consistent th ⁇ b ⁇ tion hypothesis is with physi ⁇ l laws, and/or highly probable reasonableness conditions relating to the b ⁇ tion of the target MS and its movement chara ⁇ eristics.
- such heuristics may utilize knowledge of the geographical terrain in which the MS is estimated to be, and/or, for instance, the MS velocity, acceleration or extrapolation of an MS position, velocity, or acceleration, (c) generation of additional lo ⁇ tbn hypotheses whose MS locations are consistent with, for exampl ⁇ , previous estimated b ⁇ tions for the target MS.
- the hypothesis analyzer 1332 module receives (potentially) modified b ⁇ tion hypothes ⁇ s from th ⁇ context adjuster 1326 and performs additional lo ⁇ tion hypothesis processing that is likely to be common and generic in analyzing most lo ⁇ tbn hypotheses. More specifically, the hypothesis analyzer 1332 may adjust eith ⁇ r or both of the target MS 140 estimated b ⁇ tion and/or the confidence of a lo ⁇ tion hypothesis.
- the hypothesis analyzer 1332 rec ⁇ ives target MS 140 bcation hypothes ⁇ s from the context analyzer 1336, and depending on the time stamps of newly received lo ⁇ tbn hypotheses and any previous (i.e., old ⁇ r) target MS locatbn hypotheses that may still be currently available to the hypothesis analyzer 1332, the hypothesis analyzer may: (a) update some of the older hypotheses by an extrapolation module, (b) utilize some of the old hypotheses as prevbus target MS estimates for us ⁇ in tracking the target MS 140, and/or
- th ⁇ processing for example, in th ⁇ hypoth ⁇ sis analyzer 1332 (as with the context adjuster 1326) is intended to compensate, when necessary, for this straightforwardness by providing substantially generic MS b ⁇ tion processing ⁇ pabilities that an require a greater breadth of appli ⁇ tbn understanding related to wireless signal chara ⁇ eristi ⁇ of the coverage area 120.
- the hypothesis analyzer 1332 may apply varbus heuristics that, for example, change the confidence in a lo ⁇ tion hypothesis depending on how w ⁇ ll the lo ⁇ tion hypothesis (and/or a series of lo ⁇ tion hypotheses from e.g., the same FOM 1224): (a) conforms with the laws of physics, (b) conforms with known chara ⁇ eristi ⁇ of location signature clusters in an area of the bcation hypothesis MS 140 estimat ⁇ , and (c) conforms with highly likely heuristic constraint knowledge, in particular, as illustrated best in Fig.7, the b ⁇ tion hypothesis analyzer 1332 may utilize at least one of a blackboard system and/or an expert system for applying various appli ⁇ tion specific heuristi ⁇ to the lo ⁇ tion hypotheses output by the context adjuster 1326.
- the lo ⁇ tion hypothesis analyzer 1332 includes, in one embodiment, a blackboard manager for managing processes and data of a blackboard system. Additionally, note that in a second embodiment, where an expert system is utilized instead of a blackboard system, the location hypoth ⁇ sis analyzer provides an expert system inference engine for the expert system. Note that additional detail on these aspens of the inventbn are provided hereinbelow. Additionally, note that the hypothesis analyzer 1332 may a ⁇ ivate one or more extrapolation procedures to extrapolate target MS
- the hypothesis analyzer may invoke an extrapolation module (i.e., lo ⁇ tion extrapolator 1432, Fig.7) for adjusting any previous bcation hypotheses for the same target MS 140 that are still being used by the lo ⁇ tb ⁇ hypothesis analyzer so that all target MS b ⁇ tion hypotheses (for the same target MS) being concurrently analyzed are presumed to be for substantially the same time.
- an extrapolation module i.e., lo ⁇ tion extrapolator 1432, Fig.7
- such a prevbus bcation hypothesis that is, for example, 15 seconds older than a newfy supplied lo ⁇ tbn hypothesis (from perhaps a different FOM 1224) may have both: (a) an MS b ⁇ tion estimate changed (e.g, to account for a movem ⁇ nt of the target MS), and (b) its confidence changed (e.g., to refle ⁇ a reduced confidence in the accuracy of the lo ⁇ tbn hypothesis).
- the archite ⁇ ure of the present inventbn is such that the hypothesis analyzer 1332 has an ext ⁇ nsibl ⁇ architecture. That is, additional lo ⁇ tion hypoth ⁇ sis analysis modules may b ⁇ ⁇ asily integrated into the hypothesis analyzer 1332 as further understanding regarding the behavior of wireless signals within the service area 120 becomes available. Conversely, some analysis modules may not be required in areas having relatively predictable signal patterns. Thus, in such service areas, such unnecessary modules may be ⁇ asily removed or not even developed..
- the most likelihood estimator 1344 is a module for determining a "most likely" lo ⁇ tion estimat ⁇ for a targ ⁇ t MS being bated by the loation e ⁇ gin ⁇ 139.
- the most likelihood estimator 1344 receives a colle ⁇ ion of a ⁇ ive or relevant b ⁇ tion hypotheses from the hypothesis analyzer 1332 and uses thes ⁇ b ⁇ tion hypotheses to determine one or more most likely estimates for the target MS 140. Still referring to the hypoth ⁇ sis evaluator 1228, it is important to note that not all the above mentioned modul ⁇ s are required in all embodiments of the present inventbn.
- th ⁇ hypoth ⁇ sis analyzer 1332 may be unnecessary. Accordingly, in such an embodiment, the enhanced lo ⁇ tion hypothesis output by the context adjuster 1326 are provid ⁇ d directly to the most likelihood estimator 1344.
- a fourth fun ⁇ ional group of lo ⁇ tio ⁇ ⁇ ngin ⁇ 139 modules is th ⁇ control and output gating modul ⁇ s which includes the b ⁇ tbn center control subsystem 1350, and the output gateway 1356.
- the lo ⁇ tion control subsystem 1350 provides the highest l ⁇ v ⁇ l of control and monitoring of the data processing performed by the b ⁇ tion center 142.
- this subsystem performs the folbwing f un ⁇ bns:
- this subsystem may receive (via, e.g, th ⁇ public telephone switching network and Internet 1362) such environmental information as increased signal noise in a particular service are due to increase traffic, a change in weather conditions, a bas ⁇ station 122 (or oth ⁇ r infrastru ⁇ ure provisioning), cha ⁇ g ⁇ in operation status (e.g., operational to ina ⁇ ive);
- (c) receives and directs lo ⁇ tion processing requests from oth ⁇ r lo ⁇ tion centers 142 (via, e.g., the Internet);
- (e) interacts with lo ⁇ tion center operators by, for example, receiving operator commands and providing output indicative of processing resources being utilized and malfunctions;
- this module routes target MS 140 b ⁇ tion estimates to the appropriate lo ⁇ tbn application(s). For instance, upon receiving a lo ⁇ tion estimat ⁇ from the most likelihood estimator 1344, the output gateway 1356 may determine that th ⁇ lo ⁇ tion estimate is for an automobile being tracked by the police and therefore must be provided must be provided according to the particular protocol.
- System Tuning and Adaptation The Adaptation Engin ⁇
- a fifth fu ⁇ ional group of lo ⁇ tion engine 139 modules provides the ability to enhance the MS bating reliability and/or accuracy of the present invention by providing it with the capability to adapt to particular operating configurations, operating conditions and wireless signaling environments without performing intensive manual analysis of the performance of various embodim ⁇ nts of th ⁇ lo ⁇ tion engine 139. That is, this f un ⁇ bnal group automati ⁇ lly enhances the performance of the lo ⁇ tion engine for bating MSs 140 within a particular coverage area 120 using at least one wireless network infrastru ⁇ ure therein. More precisely, this fun ⁇ ional group allows the present invention to adapt by tuning or optimizing certain system paramet ⁇ rs according to lo ⁇ tio ⁇ engine 1 9 b ⁇ tion estimate accuracy and reliability.
- Th ⁇ re are a numb ⁇ r lo ⁇ tion engine 139 system parameters whose values affe ⁇ b ⁇ tio ⁇ estimation, and it is an aspe ⁇ of the present inventbn that the MS lo ⁇ tion processing performed should become increasingly better at bating a target MS 140 not only through building an increasingly more detailed model of th ⁇ signal chara ⁇ eristi ⁇ of lo ⁇ tion in the coverage area 120 such as discussed above regarding the lo ⁇ tio ⁇ signature data base 1320, but also by providing automated capabilities for the lo ⁇ tion center processing to adapt by adjusting or "tuning" the values of such lo ⁇ tion center system parameters.
- the present invention includes a module, denoted herein as an "adaptation engine” 1382, that performs an optimization procedure on the lo ⁇ tion center 142 system param ⁇ ters either periodically or concurrently with th ⁇ operation of the io ⁇ tion center in estimating MS locations. That is, the adaptation engin ⁇ 1382 dire ⁇ s the modifications of the system parameters so that the lo ⁇ tbn engine 139 increases in overall accuracy in bating target MSs 140.
- the adaptation engine 1382 includes an embodiment of a genetic algorithm as the mechanism for modifying the system param ⁇ ters. Genetic algorithms are basically search algorithms based on the mechanics of natural g ⁇ n ⁇ ti ⁇ . Th ⁇ genetic algorithm utilized herein is included in the form of pseudo code in APPENDIX B.
- distanc ⁇ mod ⁇ ls determine a presumed dire ⁇ ion and/or distance that a target MS 140 is from one or more base stations 122.
- the target MS bcation estimate(s) generated are obtained using radio signal analysis techniques that are quite general and therefore are not capable of taking into account the peculiarities of the topography of a particular radio coverage area.
- substantially ail radio signal analysis techniques using conventional procedures (or formulas) are based on "signal characteristic measurements" such as:
- each base station (BS) 122 is required to ⁇ mit a constant signal-strength pilot channel pseudo-nois ⁇ (PN) sequence on the forward linkcha ⁇ n ⁇ l identifi ⁇ d uniquely in the network by a pilot seque ⁇ c ⁇ offs ⁇ t and frequ ⁇ ncy assignment. It is possibl ⁇ to us ⁇ th ⁇ pilot chann ⁇ ls of th ⁇ a ⁇ iv ⁇ , candidate, neighboring and remaining sets, maintained in the target MS, for obtaining signal chara ⁇ eristic measurements (e.g., TOA and/or TDOA measurements) between the target MS 140 and the base stations in one or more of these sets.
- PN pseudo-nois ⁇
- signal chara ⁇ eristic ranges or range differences related to the lo ⁇ tio ⁇ of the targ ⁇ t MS 140 ⁇ be calculated.
- thes ⁇ ranges can then be input to either the radius-radius multilateration or the time difference multilateration algorithms aiong with the known positions of the corresponding base stations 122 to thereby obtain one or more location estimat ⁇ s of th ⁇ targ ⁇ t MS 140.
- the target MS 140 may cooperate with each of the base stations in this set to provide signal arrival time measurements.
- each of the resulting four sets of three of thes ⁇ bas ⁇ stations 122 may b ⁇ us ⁇ d to provid ⁇ an ⁇ sti ate of the target MS 140 as one skilled in the art will understand.
- locatbn can be determined in eith ⁇ r entity.
- Sinc ⁇ many of the signal measurements utilized by embodiments of distance models are subject to signal attenuation and multipath due to a particular area topography. Many of th ⁇ s ⁇ ts of bas ⁇ stations from which targ ⁇ t MS bcation estimates are desired may result in either no location estimate, or an inaccurate bcation ⁇ stimate.
- some embodim ⁇ nts of distanc ⁇ FOMs may attempt to mitigate such ambiguity or inaccuracies by, e.g., identifying discrepancies (or consistencies) between arrival time measurements and other measurem ⁇ ts ( ⁇ .g., signal strength), these discrepancies (or consistencies) may be used to filter out at least those signal m ⁇ asurements and/or generated location estimates that appear less accurate.
- identifying may filtering can be performed by, for example, an expert system residing in the distance FOM.
- a second approach for mitigating such ambiguity or confli ⁇ ing MS location estimates is particularly novel in that each of the target MS location estimat ⁇ s is used to generate a location hypothesis regardless of its apparent accuracy. Accordingly, thes ⁇ bcation hypotheses are input to an alternative embodiment of the context adjuster 1326 that is substantially (but not identical to) the context adjuster as described in detail in APPENDIX D so that each location hypothesis may be adjusted to enhanc ⁇ its accuracy.
- this alternative embodiment adjusts each of the location hypotheses generated by a distance first order model according to a past performance of the model as applied to signal chara ⁇ eristic measurements from the same set of base stations 122 as were used in generating the bcation hypothesis.
- the retrieval retrieves only the archived bcation estimat ⁇ s that are, in addition, derived from the signal characteristics measur ⁇ m ⁇ nt obtained from the same colle ⁇ ion of base stations 122 as was used in generating the location hypothesis.
- the adjustment performed by this embodiment of the context adjuster 1326 adjusts according to the past performance of the distance model and the collection of base stations 122 used.
- Radio coverage area of individual base stations 1 2 may be used to generate location ⁇ stimates of the target MS 140.
- a first order model 1224 based on this notion may be less accurate than other techniques, if a reasonably accurate RF coverag ⁇ area is known for each (or most) of the base stations 122, then such a FOM (denoted hereinafter as a "coverage area first order model” or simply “coverage area model”) may be very reliable.
- RF location coverage areas with respect to BSs 122, antennas and/or sector coverag ⁇ areas, for a given class (or classes) of ( ⁇ .g., CDMA or TDMA) mobii ⁇ statb ⁇ (s) 140, bcation cov ⁇ rage should be based on an MS's ability to adequately detect the pilot channel, as opposed to adequate signal quality for purposes of carrying user-acceptable traffic in the voice channel.
- class (or classes) of ( ⁇ .g., CDMA or TDMA) mobii ⁇ statb ⁇ (s) 140, bcation cov ⁇ rage should be based on an MS's ability to adequately detect the pilot channel, as opposed to adequate signal quality for purposes of carrying user-acceptable traffic in the voice channel.
- the "Location Coverage Area” will generally be a larger area than that of a typical "Voice Coverag ⁇ Area", although industry vertex ⁇ s hav ⁇ found some occurrences of "no-coverage" areas within a larger covered area.
- An example of a cov ⁇ rag ⁇ area including both a "dead zone”, i.e., area of no coverag ⁇ , and a "notch" (of also no coverage) is shown in Fig. 15.
- the approximate maximum RF coverage area for a given se ⁇ or of (more generally angular range about) a base station 122 may be represented as a set of points repres ⁇ ting a polygonal area (potentially with, e.g., holes therein to account for dead zones and/or notch ⁇ s). Not ⁇ that if such polygonal RF cov ⁇ rag ⁇ area representations can be reliably determined and maintained over time (for one or more BS signal power level settings), then such representations can be used in providing a s ⁇ t theoretic or Venn diagram approach to estimating the location of a target MS 140. Coverage area first order models utilize such an approach.
- One e bodim ⁇ t, a cov ⁇ rag ⁇ area mod ⁇ l utiliz ⁇ s both the d ⁇ te ⁇ io ⁇ and non-det ⁇ io ⁇ of bas ⁇ stations 122 by th ⁇ targ ⁇ t MS 140 (conversely, of the MS by one or more base stations 122) to define an area wh ⁇ re th ⁇ targ ⁇ t MS 140 may lik ⁇ ly b ⁇ .
- a relatively straightforward application of this technique is to:
- the new areas may be used to generate bcation hypotheses.
- LBS location base station
- FAM 1224 a database is access ⁇ d which contains ⁇ lettrical, radio propagation and coverage area chara ⁇ eristics of each of the bcation base stations in the radio coverag ⁇ area.
- the LBS model is an a ⁇ ive model, in that it can prob ⁇ or ⁇ xcit ⁇ one or more particular LBSs 152 in an area for which th ⁇ targ ⁇ t MS 140 to b ⁇ located is suspe ⁇ ed to be placed.
- the LBS model may rec ⁇ ive as input a most likely targ ⁇ t MS 140 location estimate previously output by the lo ⁇ tion engi ⁇ 139 of th ⁇ present inv ⁇ ntion, and use this lo ⁇ tion ⁇ stimat ⁇ to determine which (if any) LBSs 152 to activate and/or dea ⁇ ivate for enhancing a subsequent location estimat ⁇ of the target MS.
- the feedback from the activated LBSs 152 may be provided to other FOMs 1224, as appropriate, as well as to the LBS model.
- the LBS model when it receives such feedback, it may output lo ⁇ tio ⁇ hypotheses having relatively small target MS 140 location area estimates about th ⁇ active LBSs 152 and each such location hypothesis also has a high confidenc ⁇ value indicative of the target MS 140 positively being in the corresponding bcation area estimate (e.g., a confidence value of .9 to + 1), or having a high confidence value indicative of the target MS 140 not being in the corresponding bcation area ⁇ stimate (i.e., a confidence value of -0.9 to -I).
- a high confidenc ⁇ value indicative of the target MS 140 positively being in the corresponding bcation area estimate e.g., a confidence value of .9 to + 1
- a confidence value indicative of the target MS 140 not being in the corresponding bcation area ⁇ stimate i.e., a confidence value of -0.9 to -I.
- th ⁇ s ⁇ ⁇ mbodim ⁇ nts may hav ⁇ functionality similar to that of the coverag ⁇ area first order model describ ⁇ d above. Further note that for LBSs within a neighborhood of the target MS wherein there is a reasonable chance that with movement of the target MS may be detected by th ⁇ s ⁇ LBSs, such LBSs may be requested to periodically a ⁇ ivate. (Note, that it is not assumed that such LBSs have an on-line external power source; e.g., some may be solar powered).
- an LBS 152 includes sufficient el ⁇ ttronics to carry voic ⁇ communication with th ⁇ target MS 140 and is the primary BS for the target MS (or alternatively, in the a ⁇ ive or candidate set), then the LBS model will not dea ⁇ ivate this particular LBS during its procedure of a ⁇ ivating and deactivating various LBSs 152.
- the stochastic first order models may use statistical predi ⁇ ion techniques such as principle decomposition, partial least squares, partial least squares, or other regression techniques for predicting, for example, expe ⁇ ed minimum and maximum distances of the target MS from one or more base stations 122, e.g, Boll ⁇ ng ⁇ r Bands. Additionally, some embodiments may use Markov processes and Random Walks (predi ⁇ ed incremental MS movement) for determining an expe ⁇ ed area within which the target MS 140 is likely to be. That is, such a process measures the incremental time differences of each pilot as the MS moves for predicting a siz ⁇ of a location area estimate using past MS estimates such as the verifi ⁇ d location signatures in th ⁇ bcation signature data bas ⁇ 1320.
- statistical predi ⁇ ion techniques such as principle decomposition, partial least squares, partial least squares, or other regression techniques for predicting, for example, expe ⁇ ed minimum and maximum distances of the target MS from one or more base stations 122, e
- FOMs 1224 using pattern recognition or associativity techniques th ⁇ re are many such techniques available.
- FOMs 1224 using pattern recognition or associativity techniques th ⁇ re are many such techniques available.
- there are statistically bas ⁇ d systems such as "CART" (anacronym for Classification and Regression Trees) by ANGOSS Software International Limited of Toronto, Canada that may be us ⁇ d for automati ⁇ lly for d ⁇ te ⁇ ing or recognizing patterns in data that were u ⁇ provid ⁇ d (and likely previously unknown).
- the verified location signature clusters within the cells of each area type may be analyzed for signal chara ⁇ eristic patterns. If such patterns are found, then they can be used to identify at least a likely area type in which a target MS is likely to be bated. That is, on ⁇ or more bcation hypotheses may be generated having target MS 140 location estimat ⁇ s that cov ⁇ r an area having the likely area typ ⁇ wherein the target MS 140 is located. Further note that such statistically based pattern recognition systems as "CART" i ⁇ clud ⁇ software code generators for generating expert system software embodiments for recognizing the patterns dete ⁇ ed within a training set ( ⁇ .g., the verified location signature clusters).
- an embodim ⁇ nt of a FOM as d ⁇ scrib ⁇ d here may not be exc ⁇ di ⁇ gly accurate, it may be very reliable.
- a fundamental aspect of the present invention is to use a plurality MS location techniques for generating location estimates and to analyze the generated estimates (likely after being adjusted) to dete ⁇ patterns of convergence or clustering among the estimates, even large MS location area estimates are useful. For example, it can be the case that four different and relatively large MS location estimat ⁇ s, ⁇ ach having very high reliability, have an area of interse ⁇ ion that is acceptably precise and inherits the very high reliability from each of the large MS location estimates from which the intersection area was derived.
- a similar statistically based FOM 1224 to the one abov ⁇ may be provided wherein the radio coverage area is d ⁇ composed substantially as above, but addition to using the signal chara ⁇ eristics for dete ⁇ ing useful signal patterns, the specific identifications of the base station 122 providing the signal chara ⁇ eristics may also be used.
- an expert system may be gen ⁇ rated that outputs a targ ⁇ t MS 140 bcation ⁇ stimate that may provide both a reliable and accurate location estimat ⁇ of a targ ⁇ t MS 140.
- Th ⁇ t ⁇ r adaptiv ⁇ is used to describ ⁇ a data proc ⁇ ssing component that can modify its data processing behavior in r ⁇ spons ⁇ to certain inputs that are used to change how subsequent inputs are process ⁇ d by the component.
- a data processing component may be "explicitly adaptive” by modifying its behavior according to the input of explicit instructions or control data that is input for changing the component's subsequent behavior in ways that are predictable and exp ⁇ cted. That is, the input encodes explicit instructions that are known by a us ⁇ r of the component.
- a data processing component may be "implicitly adaptive" in that its behavior is modified by other than instructions or control data whose meaning is known by a user of the component.
- such implicitly adaptive data processors may learn by training on ⁇ xampl ⁇ s, by substantially u ⁇ guid ⁇ d exploration of a solution space, or other data driven adaptive strategies such as statistically generated decision tre ⁇ s. Accordingly, it is an aspe ⁇ of the present invention to utilize not only explicitly adaptive MS bcation estimators within FOMs 1224, but also implicitly adaptive MS location estimators.
- artificial neural networks also denoted neural nets and ANNs herein
- Artificial neural networks may b ⁇ particularly us ⁇ ful in developing one or more first order models 1224 for bating an MS 140, si ⁇ c ⁇ , for example, ANNs can be trained for classifying and/or associativeiy pattern matching of various RF signal measurements such as the bcation signatures. That is, by training one or more artificial neural nets using RF signal measurements from verified lo ⁇ tions so that RF signal transmissions chara ⁇ eristics indicative of particular locations are associated with their corresponding lo ⁇ tions, such trained artificial n ⁇ ural nets can be used to provide additional target MS 140 location hypotheses. Moreover, it is an aspe ⁇ of the present invention that the training of such artificial neural net based FOMs (ANN FOMs) is provided without manual intervention as will be discussed hereinbelow. Artificial Neural Networks That Converge on Near Optimal Solutions
- an adaptive neural network architecture which has the ability to ⁇ xplore the parameter or matrix weight space corresponding to a ANN for determining new configurations of weights that reduce an objective or error function indicating the error in the output of the ANN over some aggregate set of input data ensembles.
- a g ⁇ netic algorithm is used to provide such an adaptation ⁇ pability.
- other adaptive techniques such as, for exampl ⁇ , simulated a ⁇ n ⁇ aling, cas ⁇ d ⁇ correlation with multistarts, gradient descent with multistarts, and trun ⁇ ted Newton's method with multistarts, as one skilled in the art of neural network computing will understand.
- a learning paradigm i.e., does the ANN require supervised training (i.e., being provided with indications of corrett and incorre ⁇ performance), unsupervised training, or a hybrid of both (sometimes referred to as reinforcement);
- supervised training i.e., being provided with indications of corrett and incorre ⁇ performance
- unsupervised training or a hybrid of both (sometimes referred to as reinforcement)
- a colle ⁇ ion of learning rules for indicating how to update the ANN a colle ⁇ ion of learning rules for indicating how to update the ANN
- the architecture of the present invention allows substantial flexibility in the implem ⁇ ntation of ANN for FOMs 1224.
- th ⁇ r ⁇ is no n ⁇ d to choose only one artificial neural net archite ⁇ ure and/or implementation in that a plurality of ANNs may be accommodated by the archite ⁇ ure of the location engine 139.
- one embodiment of the present invention may have a plurality of moderately well trained ANNs having different neural net archite ⁇ ures such as: multilayer perceptions, adaptive resonance theory models, and radial basis fun ⁇ ion networks.
- ANN archite ⁇ ure and implem ⁇ ntation d ⁇ cisio ⁇ s can be addressed substantially automatically by various commercial artificial neural net devebpm ⁇ nt systems such as: “NEUROGENETIC OPTIMIZER” by BioComp Systems, wherein gen ⁇ tic algorithms are used to optimize and configure ANNs, and artificial n ⁇ ural network hardware and software produtts by Accurate Automation Corporation of Chattanooga, T ⁇ nn ⁇ ssee, such as "ACCURATE AUTOMATION NEURAL NETWORK TOOLS.
- the signal proc ⁇ ssing subsystem 1220 may provide various RF signal m ⁇ asurements as input to an ANN (such as the RF signal measurements derived from verified bcation signatures in th ⁇ bcation signature data bas ⁇ 1320).
- an ANN such as the RF signal measurements derived from verified bcation signatures in th ⁇ bcation signature data bas ⁇ 1320.
- a representation of a histogram of the frequency of occurrence of CDMA fingers in a time delay vs. signal strength 2-dimensional domain may be provided as input to such an ANN.
- a 2-dimensional grid of signal strength versus time delay bins may be provided so that received signal measurem ⁇ nts are slotted into an appropriate bin of the grid.
- a grid is a six by six array of bins such as illustrated in the left portion of Fig. 1 . That is, each of the signal strength and tim ⁇ d ⁇ lay axises are partitioned into six ranges so that both th ⁇ signal strength and the time delay of RF signal measurements can be slotted into an appropriate range, thus determining the bin.
- RF signal measurement data i.e., location signatures
- a counter for the bin can be incremented.
- the RF measurements for each bin can be represented pi ⁇ orially as a histogram.
- various filters may be applied for filtering outliers and noise prior to inputting bin values to an ANN.
- varbus amounts of data from such a grid may be provided to an ANN.
- th ⁇ tally from ⁇ ach bin is provid ⁇ d to an ANN.
- 108 valu ⁇ s could b ⁇ input to the ANN (two values defining each bin, and a tally for the bin).
- other representations are also possible. For instance, by ordering the bin tallies linearly, only 36 ne ⁇ d b ⁇ provid ⁇ d as ANN input. Alternatively, only representations of bins having the high ⁇ st tallies may be provided as ANN input.
- the signal processing subsystem 1220 may also obtain th ⁇ identifications of oth ⁇ r bas ⁇ statbns 122 (152) for which their pilot channels can be d ⁇ t ⁇ d by th ⁇ target MS 140 (i.e., the forward path), or for which the base stations can dete ⁇ a signal from the target MS (i.e., the rev ⁇ rse path).
- a technique is provided wherein a plurality of ANNs may be a ⁇ ivated using various portions of an ⁇ nse ble of lo ⁇ tion signature data obtained. How ⁇ v ⁇ r, before describing this t ⁇ chniqu ⁇ , it is worthwhile to note that a naive strategy of providing input to a single ANN for locating target MSs throughout an area having a large number of base stations (e.g., 300) is likely to be undesirable.
- th ⁇ ANN would have to be extremely large and therefore may require inordinate training and retraining.
- sinc ⁇ th ⁇ re may be approximately 30 to 60 ANN inputs per bcation signature
- an ANN for an area having even twenty base stations 122 can require at least 600 input neurons, and potentially as many as 1,420 (i.e., 20 base stations with 70 inputs per base station and one input for every one of possibly 20 additional surrounding base stations in the radio coverage area 120 that might be able to dete ⁇ , or be detetted by, a target MS 140 in the area corresponding to the ANN).
- the technique described herein limits the numb ⁇ r of input neurons in each ANN constru ⁇ ed and generates a larger number of these smaller ANNs. That is, ⁇ ach ANN is train ⁇ d on lo ⁇ tion signature data (or, more precisely, portions of locatbn signature clusters) in an area A ANN (hereinafter also denoted the "net area"), wh ⁇ rein each input neuron receives a unique input from either:
- (Al) location signature data (e.g., signal strength/time delay bin tallies) corresponding to transmissions between an MS 140 and a relativ ⁇ ly small number of bas ⁇ stations 122 in th ⁇ area A ANN
- bcation signature data obtained from, for example, four base stations 122 (or antenna se ⁇ ors) in th ⁇ area A NN .
- ⁇ ach bcation signature data cluster includes fields describing the wireless communication devic ⁇ s used; e.g., (i) the make and model of the target MS; (ii) the current and maximum transmission power; (iii) the MS battery power (instantaneous or current); (iv) the base station (se ⁇ or) current pow ⁇ r level; (v) the base station make and model and revision level; (vi) the air interface type and revision level (of, e.g., CDMA, TDMA or AMPS).
- each such input h ⁇ r indicates whether the corresponding base station (se ⁇ or): (i) is on-line (i.e., capable of wireless communication with MSs) and at least its pilot channel signal is detected by the target MS 140, but the base station (sector) does not dete ⁇ the target MS; (ii) is on-line and the base station (se ⁇ or) det ⁇ s a wireless transmission from the target MS, but the target MS does not dete ⁇ the base station (sector) pilot channel signal; (iii) is on-line and the base station (se ⁇ or) detects the target MS and the base station (se ⁇ or) is dete ⁇ ed by the target MS; (iv) is on-line and the base station (se ⁇ or) does not dete ⁇ the target MS, the base station is not dete ⁇ ed by the target MS;
- a local environmental change in the wireless signal chara ⁇ eristics of one net area is unlikely to affe ⁇ more than a small number of adjacent or overlapping net areas. Accordingly, such local environmental changes can be reflected in that only the ANNs having net areas affected by the local change need to be retrained. Additionally, note that in cases where RF measurements from a target MS 140 are received across multiple net areas, multiple ANNs may be a ⁇ ivated, thus providing multiple MS location estimat ⁇ s.
- multiple ANNs may be a ⁇ ivated when a location signature cluster is rec ⁇ ived for a target MS 140 and location signature cluster includes location signature data corresponding to wireless transmissions between the MS and, e.g., more base statbns (antenna se ⁇ ors) than needed for the colle ⁇ ion B described in the previous s ⁇ ion. That is, if ⁇ ach collection B identifies four base stations 122 (antenna sectors), and a received bcation signature cluster includes location signature data corresponding to five base statbns (antenna sectors), then there may be up to five ANNs a ⁇ ivated to each generate a lo ⁇ tion estimate.
- the ⁇ umb ⁇ r of input n ⁇ urons is on th ⁇ order of 330; (i.e., 70 inputs per each of four bcation signatures ( i.e., 35 inputs for the forward wireless communications and 35 for the reverse wireless communications), plus 40 additional discrete inputs for an appropriate area surrounding m , plus 10 inputs related typ ⁇ of MS, power levels, etc.
- the number of base stations (or antenna se ⁇ ors 130) having corresponding location signature data to be provided to such an ANN may vary.
- location signature data from five or more base stations may be used, whereas in other subareas three (or less) may be used.
- th ⁇ re are also numerous options.
- two values corresponding to the latitude and longitude of the target MS are estimated.
- such ANN output may be in the form of a row value and a column value of a particular mesh cell (and its corresponding area) wh ⁇ re the target MS is estimated to be. Note that the cell sizes of the mesh need not be of a particular shape nor of uniform size.
- the following are steps provide one embodim ⁇ nt for training a location estimating ANN according to the pres ⁇ nt inv ⁇ ntion.
- the colle ⁇ ion C is determined by interrogating the lo ⁇ tion signature data base 1320 for verifi ⁇ d bcation signature clusters stored therein having such a common set B of base stations (antenna se ⁇ ors).
- the colle ⁇ ion C may be determined from (i) the existing engineering and planning data from service providers who are planning wireless cell sites, or (ii) service provider test data obtain ⁇ d using mobil ⁇ test s ⁇ ts, acc ⁇ ss probes or other RF field measuring devices. Note that such a colle ⁇ ion B of base stations (antenna se ⁇ ors) should only be created when th ⁇ set C of verified location signature clusters is of a sufficient size so that it is expected that the ANN can be effectively trained.
- (b) Determine a colle ⁇ ion of base stations (or antenna se ⁇ ors 130), B', from the common set B, wherein B' is small (e.g., four or five).
- (c) Determine the area, A NN , to be associated with colle ⁇ ion B' of bas ⁇ stations (antenna se ⁇ ors). In one embodiment, this area is sele ⁇ ed by determining an area containing the set L of locations of all verified location signature clusters determin ⁇ d in step (a) having location signature data from each of the base stations (antenna se ⁇ ors) in the colle ⁇ ion B'.
- th ⁇ area, A ANN may be determined by providing a covering of the locations of L, such as, e.g., by cells of a mesh of appropriately fine mesh size so that each cell is of a size not substantially larger than the maximum MS lo ⁇ tion accuracy desired.
- train the ANN on input including: (i) data from verifi ⁇ d bcation signatures from each of the base stations (antenna se ⁇ ors) in the colle ⁇ ion B', wherein each location signature is part of a cluster in the colle ⁇ ion C; (ii) a collection of discrete values corresponding to other base stations (antenna se ⁇ ors) in the area b containing the area, A ANN .
- an ANN may require configuring parameters related to, for example, input data scaling, test/training set classification, dete ⁇ i ⁇ g and removing unnecessary input variable sele ⁇ ion. How ⁇ ver, th ⁇ present inventbn reduces this tedium. That is, the present invention uses mechanisms such as gen ⁇ tic algorithms or other mechanisms for avoiding non-optimal but locally appealing (i.e., local minimum) solutions, and locating near-optimal solutions instead, in particular, such mechanism may be used to adjust the matrix of weights for the ANNs so that very good, near optimal ANN configurations may be found efficiently.
- mechanisms such as gen ⁇ tic algorithms or other mechanisms for avoiding non-optimal but locally appealing (i.e., local minimum) solutions, and locating near-optimal solutions instead, in particular, such mechanism may be used to adjust the matrix of weights for the ANNs so that very good, near optimal ANN configurations may be found efficiently.
- the signal processing system 1220 uses various types of signal processing filters for filtering the RF measurem ⁇ nts received from transmissions between an MS 140 and one or more bas ⁇ stations (antenna sectors 130), such mechanisms for finding near-optimal solutions may b ⁇ applied to sel ⁇ cti ⁇ g appropriate filters as well. Accordingly, in o ⁇ embodiment of the pres ⁇ nt invention, such filters are paired with particular ANNs so that the location signature data suppli ⁇ d to ⁇ ach ANN is filtered according to a corresponding "filter description" for the ANN, wherein the filter description specifies the filters to be used on location signature data prior to inputting this data to the ANN.
- the filter description can define a pip ⁇ lin ⁇ of filters having a sequence of filters wherein for each two consecutive filters, f ⁇ and f 2 (f, preceding f 2 ), in a filter description, the output of f, flows as input to .
- a filter description tog ⁇ ther with its corresponding ANN so that the encoding can be provided to a near optimal solution finding mechanism such as a genetic algorithm, it is believed that enhanced ANN locating performance can be obtained. That is, the combined genetic codes of the filter description and the ANN are manipulated by the gen ⁇ tic algorithm in a s ⁇ arch for a satisfa ⁇ ory solution (i.e., location ⁇ rror estimates within a desired range).
- This process and system provides a mechanism for optimizing not only the artificial neural network archite ⁇ ure, but also identifying a near optimal match between the ANN and one or more signal processing filters. Accordingly, th ⁇ following filters may be used in a filter pipeline of a filter description: Sobel, median, mean, histogram normalization, input cropping, neighbor, Gaussion, Weiner filters.
- filters may be used in a filter pipeline of a filter description: Sobel, median, mean, histogram normalization, input cropping, neighbor, Gaussion, Weiner filters.
- One embodiment for implementing the genetic evolving of filter description and ANN pairs is provided by th ⁇ following st ⁇ ps that may automati ⁇ lly performed without substantial manual effort:
- Creat ⁇ an initial population of concatenated genotypes, or genetic representations for each pair of an artificial neural networks and corresponding filter description pair. Also, provide seed parameters which guide the scope and chara ⁇ erization of the artificial neural network archite ⁇ ures, filter sele ⁇ ion and param ⁇ ters, genetic parameters and system control parameters.
- artificial neural network genotypes may be form ⁇ d by selecting various types of artificial neural network archite ⁇ ures suited to fun ⁇ ion approximation, such as fast back propagation, as well as chara ⁇ erizing several varieties of candidate transfer/a ⁇ ivation fun ⁇ ions, such as Tanh, logistic, linear, sigmoid and radial basis.
- ANNs having complex inputs may be sele ⁇ ed (as determined by a filter type in th ⁇ signal proc ⁇ ssing subsystem 1220) for th ⁇ genotypes.
- genetic parameters include: (a) maximum population size (typical default: 300), (b) generation limit (typical default: 50), (c) selection criteria, such as a certain percentage to survive (typical default: 0.5) or roulette wheel, (d) population refilling, such as random or cloning (default), (e) mating criteria, such as tail swapping (default) or two cut swapping, (f) rate for a choice of mutation criterion, such as random exchang ⁇ (d ⁇ fault: 0 5) or s ⁇ ion reversal, (g) population size of the co ⁇ tenated artificial neural network/ filter combinations, (h) use of statistical seeding on the initial population to bias the random initialization toward stronger first order relating variables, and (i) neural node influence fa ⁇ ors, e.g., input nodes and hidden nodes.
- selection criteria such as a certain percentage to survive (typical default: 0.5) or roulette wheel
- population refilling such as random or cloning (default)
- mating criteria such as tail swapping
- Such param ⁇ ters can be used as weighting fa ⁇ ors that influences the degree the system optimizes for accuracy versus network compactness.
- an input nod ⁇ fa ⁇ or greater than 0 provides a means to reward artificial neural networks constru ⁇ ed that use fewer input variables (nodes).
- a reasonable default value is 0.1 for both input and hidden nod ⁇ fa ⁇ ors.
- neural net/filter description system control parameters include: (a) accuracy of modeling parameters, such as relativ ⁇ accuracy, R-squared, mean squared error, root mean squared error or average absolute error (default), and (b) stopping criteria parameters, such as generations run, elapsed time, best accuracy found and population converg ⁇ nc ⁇ .
- accuracy of modeling parameters such as relativ ⁇ accuracy, R-squared, mean squared error, root mean squared error or average absolute error (default)
- stopping criteria parameters such as generations run, elapsed time, best accuracy found and population converg ⁇ nc ⁇ .
- th ⁇ artificial n ⁇ ural network b ⁇ provided with as much accurate RF signal measur ⁇ m ⁇ nt data regarding signal transmissions between th ⁇ target MS 140 and the base station infrastru ⁇ ure as possible.
- ANN inputs as described hereinabove, it is desirable to obtain the dete ⁇ ion states of as many surrounding base stations as possible.
- the location center 140 automatically transmits a request to the wirel ⁇ ss infrastructure to which the target MS is assigned for instructing the MS to raise its transmission pow ⁇ r to full power for a short period of time (e.g., 100 milliseconds in a base station infrastru ⁇ ure configuration an optimized for such requests to 2 seconds in a non-optimized configuration).
- the request for a change in the transmission power iev ⁇ l of the target MS has a further advantag ⁇ for bcation requests such as emergency 911 that are initiated from the MS itself in that a first ⁇ ns ⁇ mbl ⁇ of RF signal measurements can be provided to the bcation ⁇ gine 139 at the initial 911 calling power level and then a second ensemble of RF signal measurements can be provided at a second higher transmission power lev ⁇ l.
- an artificial neural network can be trained not only on the bcation signature cluster d ⁇ rived from either the initial wireless 911 transmissbns or the full power transmissions, but also on the differences between these two transmissions.
- the difference in the dete ⁇ ion states of the discrete ANN inputs betw ⁇ en the two transmission power levels may provide useful additional information for more accurately estimating a bcation of a target MS.
- the network should not be overburden ⁇ d with location related traffic. Accordingly, not ⁇ that n ⁇ twork bcation data requests for data particularly useful for ANN based FOMs is generally confined to the requests to the base stations in the immediate area of a target MS 140 whose location is desired.
- both collections of bas ⁇ stations B' and b discuss ⁇ d in the context of training an ANN are also the same collections of base stations from which MS locatbn data would be requested.
- the wireless network MS lo ⁇ tion data requests are data driven in that the base stations to queried for lo ⁇ tio ⁇ data (i.e., the collections B' and b) are determined by previous RF signal measurement characteristics recorded. Accordingly, the selection of the colle ⁇ ions ⁇ * and b are adaptable to changes in the wireless enviro ⁇ m ⁇ ntal chara ⁇ eristics of the coverage area 120.
- lo ⁇ tion signature data base 1320 stores MS lo ⁇ tion data from verifi ⁇ d and/or known lo ⁇ tions (optionally with additional known environmental chara ⁇ eristic values) for use in enhancing current targ ⁇ t MS lo ⁇ tbn hypotheses and for comparing archived location data with lo ⁇ tbn signal data obtained from a current target MS.
- the data base management system functionality incorporated into the lo ⁇ tion signature data base 1320 is an important aspe ⁇ of the present invention, and is therefore described in this se ⁇ ion.
- the data bas ⁇ management functionality described herein addresses a number of difficulties encountered in maintaining a large archive of signal processing data such as MS signal location data. Some of these difficulties on be described as follows:
- the siz ⁇ of the data in the archive makes it prohibitive for such a process to be performed manually, and there may be no simple or straightforward tech ⁇ iqu ⁇ s for automating such impa ⁇ redu ⁇ ion or filtering processes for inapplicable signal data; (b) it is sometimes difficult to determine the archived data to use in comparing with newly obtained signal processing application data; and (c) it is sometimes difficult to determine a useful technique for comparing archived data with newly obtained signal proc ⁇ ssing appli ⁇ tion data. It is an aspe ⁇ of the present invention that th ⁇ data base management functionality of th ⁇ lo ⁇ tion signature data base 1320 addresses each of the difficulties mentioned immediately above.
- the lo ⁇ tion signature data base is "self cleaning" in that by associating a confidence value with each loc sig in the data base and by reducing or increasing the confidences of archived verified be sigs according to how well their signal chara ⁇ eristic data compares with newly received verifi ⁇ d lo ⁇ tion signature data, the location signature data base 1320 maintains a consistency with newly verified loc sigs.
- the following data base management functional descriptions describe some of the more noteworthy fun ⁇ ions of the lo ⁇ tion signature data base 1320. Note that there are varbus ways that these fun ⁇ ions may be embodi ⁇ d. So as to not overburden the reader here, the d ⁇ tails for one embodim ⁇ nt is provid ⁇ d in APPENDIX C. Figs. 16a through 16c pres ⁇ nt a table providing a brief description of the attributes of the b ⁇ tion signature data type stored in the bcation signature data bas ⁇ 1320.
- Th ⁇ following program updates the random loc sigs in the location signature data base 1320.
- this program is invoked primarily by the Signal Processing Subsystem.
- This program updat ⁇ s loc sigs in th ⁇ lo ⁇ tion signature data bas ⁇ 1320. That is, this program updates, for example, at least the location information for verified random be sigs residing in this data base.
- the general strategy here is to use information (i.e., "new_loc_obj") received from a newly verified location (that may not yet be entered into the location signature data bas ⁇ ) to assist in determining if the previously stored random verified loc sigs are still reasonably valid to use for: (29.1) estimating a bcation fora given colle ⁇ ion (i.e., "bag") of wireless (e.g., CDMA) location related signal chara ⁇ eristics received from an MS, (192) training (for example) adaptive lo ⁇ tion estimators (and location hypothesizing models), and (293) comparing with wireless signal characteristics used in generating an MS location hypothesis by one of the MS bcation hypothesizing models (denoted first Order Models, or, FOMs).
- wireless e.g., CDMA
- comparisons are it ⁇ rativ ⁇ ly ad ⁇ h ⁇ re betwe ⁇ n ⁇ ach (targ ⁇ t) loc sig "near" "new_bc_obj” and a population of be sigs in th ⁇ locatbn signature data base 1320 (such population typically including the loc sig for "new_loc_obj) for.
- predetermined thresholds be substantially automatically adjustable by periodically testing various confidenc ⁇ fa ⁇ or thresholds in a sp ⁇ cifi ⁇ d geographic area to determine how w ⁇ ll the eligible data base e sigs (for different thresholds) perform in agreeing with a number of verified be sigs in a "loc sig test-bed", wherein the test bed may b ⁇ composed of, for exampl ⁇ , repeatable loc sigs and recent random verifi ⁇ d loc sigs.
- this program may be invoked with a (verified Anown) random and/or rep ⁇ atabl ⁇ loc sig as input.
- the target loc sigs to be updated may be sel ⁇ ct ⁇ d from a particular group of loc sigs such as the random loc sigs or the repeatable loc sigs, such sel ⁇ ion b ⁇ ing determined according to the input param ⁇ t ⁇ r, "sel ⁇ ion criteria" while the comparison population may be designated with the input parameter, "bc_sig_pop".
- the rep ⁇ atabl ⁇ loc sigs (from, e.g., stationary transceivers) in th ⁇ area hav ⁇ r ⁇ c ⁇ ntly b ⁇ en updated, then by successively providing "new_bc_obj" with a loc sig for ⁇ ach of th ⁇ s ⁇ r ⁇ p ⁇ atable loc sigs, the stored random e sigs can have their confidences adjusted.
- th ⁇ pres ⁇ nt fun ⁇ ion may b ⁇ us ⁇ d for determining when it is desirable to update rep ⁇ atabl ⁇ loc sigs in a particular area (instead of automatically and periodically updating such rep ⁇ atabl ⁇ loc sigs).
- rep ⁇ atabl ⁇ loc sigs may be updated.
- the random bcation signature data base verified location information may be effectively compared against the repeatable loc sigs in an area.
- sel ⁇ ion_crit ⁇ ria a data representation designating the be sigs to be sele ⁇ ed to have their confidences updated (may be defaulted).
- the folbwing groups of be sigs may be sel ⁇ ct ⁇ d: "USE_RAND0M_L0C_SIGS" (this is th ⁇ default), USEJEPEATABLE OC IGS", "USE_ALL_LOC_SIGS". Note that each of these selections has values for the following values associated with it (although the values may be defaulted): (a) a confidence redu ⁇ ion fa ⁇ or for reducing loc sig confidences,
- loc sig pop a data representation of the type of loc sig population to which th ⁇ loc sigs to b ⁇ updated are compared.
- the folbwing values may be provided: (a) "USE ALL LOC SIGS IN DB", (b) "USE ONLY REPEATABLE LOC SIGS" (this is the default),
- Th ⁇ following program r ⁇ duc ⁇ s th ⁇ confid ⁇ nce of verifi ⁇ d loc sigs in the location signature data base 1320 that are likely to b ⁇ no long ⁇ r accurate (i.e., in agreement with comparable loc sigs in the data base). If the confidence is reduced low enough, th ⁇ n such loc sigs are removed from the data base. Further, if for a bcation signature data bas ⁇ verified bcation composite ⁇ ntity (i. ⁇ ., a coli ⁇ ion of loc sigs for th ⁇ sam ⁇ cation and tim ⁇ ), this ⁇ ntity no long ⁇ r references any valid loc sigs, then it is also r ⁇ mov ⁇ d from th ⁇ data base. Note that this program is invoked by "Update_Loc_Sig_DB”. reduce_bad_DB Joc_sigs(loc_sig_bag , error_rec_set, big_error_threshold confidence_reduction_factor, recent_time)
- bc_sig_bag A collection or “bag” of loc sigs to be tested for determining if their confidences should be lowered and/or any of these loc sigs removed.
- error_rec_s ⁇ t A s ⁇ t of ⁇ rror records (obje ⁇ s), denot ⁇ d " ⁇ rror_recs”, providing information as to how much each loc sig in "bc_sig_bag” disagrees with comparable loc sigs in the data base. That is, there is a
- error_rec_s ⁇ t A s ⁇ t of ⁇ rror records (obje ⁇ s), denoted “error_recs”, providing information as to how much each loc sig in "bc_sig_bag” disagrees with comparable loc sigs in the bcation signature data base. That is, there is a " ⁇ rror rec” h ⁇ re for ⁇ ach loc sig in "loc sig bag”.
- small_error threshold The error threshold below which the errors are considered too small to ignore, confidence increase a ⁇ or: The factor by which to increase the confidenc ⁇ of loc sigs. recent_time: Tim ⁇ p ⁇ riod b ⁇ yond which loc sigs are no longer considered recent.
- the following program deter in ⁇ s th ⁇ consistency of location hypothes ⁇ s with verified location information in th ⁇ location signature data base 1320. Note that in the one embodiment of the pres ⁇ nt invention, this program is invoked primarily by a module denot ⁇ d th ⁇ historical locatbn reasoner 1424 described se ⁇ io ⁇ s hereinbelow. Moreover, the detailed description for this program is provid ⁇ d with the description of the historical bcation reasoner hereinbelow for completeness.
- This function determines how well the colle ⁇ ion of loc sigs in "measured_!oc_sig_bag” fit with the loc sigs in the bcation signature data base 1320 wherein th ⁇ data bas ⁇ loc sigs must satisfy th ⁇ criteria of th ⁇ input parameter "s ⁇ arch_criteria” and are relativ ⁇ ly close to the MS lo ⁇ tion estimate of the locatbn hypothesis, "hypothesis”.
- Input hypothesis: MS bcation hypothesis; measuredJoc_sig_bag: A collection of m ⁇ asur ⁇ d location signatures ("be sigs" for short) obtain ⁇ d from the MS
- the data stru ⁇ ure here is an aggregation such as an array or list. Note, it is assumed that there is at most one be sig here per Base Station in this colle ⁇ ion. Additionally, note that the input data stru ⁇ ure here may be a lo ⁇ tion signature cluster such as the "loc_sig_cluster" field of a location hypothesis (cf. Fig.9). Note that variations in input data structures may be accepted here by utilization of flag or tag bits as one skilled in the art will appreciate; search_crit ⁇ ria: Th ⁇ criteria for searching the verified bcation signature data base for various categories of loc sigs. The only limitation on the types of categories that may be provided here is that, to be useful, each category should have meaningful number of loc sigs in the location signature data base. Th ⁇ following cat ⁇ gori ⁇ s includ ⁇ d h ⁇ r ⁇ are illustrative, but others are contemplated:
- loc sigs close to the MS estimate of "hypothesis" contemplated are: all be sigs for the sam ⁇ s ⁇ ason and same time of day, all loc sigs during a specific weather condition (e.g., snowing) and at the same time of day, as well as other limitations for other environm ⁇ ntal conditions such as traffic patterns. Note, if this parameter is NIL, then (a) is assumed.
- An error obje ⁇ (data type: "error_object") having: (a) an "error” field with a measurement of the error in the fit of the bcation signatures from the MS with verifi ⁇ d bcation signatures in the location signature data base 1320; and (b) a "confidence” field with a value indicating the perceived confide ⁇ c ⁇ that is to be given to the "error” value.
- the following program compares: (al) loc sigs that are contain ⁇ d in (or d ⁇ riv ⁇ d from) the loc sigs in "target_bc_sig_bag” with (bl) loc sigs computed from verified loc sigs in the bcation signature data bas ⁇ 1320. That is, each loc sig from (al) is compared with a corresponding e sig from (b) to obtain a measurement of the discrepancy between the two loc sigs.
- this program determines how well the loc sigs in "target bc_sig_bag” fit with a computed or estimated loc sig for the location, "targetjoc” that is derived from the verifi ⁇ d loc sigs in th ⁇ bcation signature data base 1320.
- this program may be used: (a2) for determining how well th ⁇ loc sigs in the bcation signature cluster for a target MS
- target loc_sig_bag compares with loc sigs derived from verifi ⁇ d location signatures in th ⁇ location signature data base, and (b2) for determining how consistent a given colle ⁇ ion of be sigs (“targetJoc_sig_bag”) from the bcation signature data base is with other loc sigs in the bcation signature data base. Not ⁇ that in (b2) each of the one or more loc sigs in "target Joc_sig_bag” have an error computed here that can be used in determining if the be sig is becoming inapplicable for predicting target MS locations.
- targetjoc An MS location or a location hypothesis for an MS. Note, this can be any of the folbwing: (a) An MS bcation hypothesis, in which ⁇ se, if th ⁇ hypothesis is inaccurate, then the loc sigs in "target Joc_sig_bag" are the location signature cluster from which this location hypoth ⁇ sis was derived. Note that if this location is inaccurate, then
- target Joc_sig_bag is unlikely to be similar to the comparable loc sigs derived from the loc sigs of th ⁇ locatbn signature data base close “targetjoc”;
- target loc sig bag Measured bcation signatures ("loc sigs" for short) obtain ⁇ d from the MS (the data stru ⁇ ure here, bag, is an aggregation such as array or list). It is assumed that there is at least one be sig in the bag. Further, it is assumed that there is at most on ⁇ loc sig per Base Station; search area: The representation of the geographic area surrounding "targetjoc”.
- Th ⁇ criteria used in searching the bcation signature data base. Th ⁇ criteria may include the following:
- error_rec_bag A bag of error records or obje ⁇ s providing an indication of the similarity between each be sig in "target_loc_sig_bag” and an estimated loc sig computed for "targetjoc” from stored loc sigs in a surrounding area of "targetjoc".
- the following program receives a colle ⁇ ion of be sigs and computes a loc sig that is representative of th ⁇ loc sigs in th ⁇ coli ⁇ ion. That is, given a colle ⁇ ion of loc sigs, "loc sig bag", wherein each loc sig is associated with the same predetermined Base
- this program uses these loc sigs to compute a representative or estimated loc sig associated with the pr ⁇ d ⁇ termined Base Station and associated with a pred ⁇ t ⁇ rmi ⁇ ed MS bcation, "locjor_estimatbn".
- loc sigs in "lo sig bag” are from the verifi ⁇ d be sigs of the location signature data base such that each of these loc sigs also has its associated MS bcation relatively cbse to "loc Jor_estimatbn”
- this program can compute and return a reasonable approximation of what a measured loc sig between an MS at "loc Jor_estimation" and the predetermined Base Station ought to be.
- This program is invoked by "D ⁇ termin ⁇ _Location_Signature_Fit_Errors". estimateJoc_sig_from_DB(loc_for_estimation, loc_sig_bag)
- the following program deter in ⁇ s and r ⁇ turns a representation of a geographic area about a bcation, "loc", wherein: (a) the geographic area has associated MS locations for an acceptabl ⁇ numb ⁇ r (i.e., at least a determined minimal number) of verifi ⁇ d loc sigs from the location signature data base, and (b) the geographical area is not too big. However, if there are not enough loc sigs in even a larg ⁇ st acc ⁇ ptabl ⁇ s ⁇ arch area about "loc", then this largest search area is returned. "DB_Loc_Sig_Error_Fit" get_area_to_search(loc)
- This program compares two location signatures, "target_loc_sig” and “comparison_bc_sig”, both associated with the same pred ⁇ t ⁇ rmin ⁇ d Base Station and th ⁇ sam ⁇ predetermined MS bcation (or hypothesized bcation).
- This program determin ⁇ s a measure of the difference or error b ⁇ tw ⁇ en the two loc sigs relative to the variability of the verifi ⁇ d location signatures in a collection of loc sigs d ⁇ not ⁇ d the "comparison_bc_sig_bag” obtained from the bcation signature data base.
- This program is invoked by: the program, "Determine_Location_Signature_Fit Errors", described above. get_difference_measurement(targetJoc_sig, comparisonJoc_sig, comparison Joc_sig_bag, search_area, search_criteria) Input: targ ⁇ t _loc_sig: Th ⁇ loc sig to which the "error rec” determined here is to be associated. comparison Joc_sig: The loc sig to compare with the "target Jocjig”. Note, if “comparison Joc_sig” is NIL, then this parameter has a value that corresponds to a noise level of "target Jocjig".
- comparisonjOc sig bag The universe of loc sigs to use in det ⁇ rmining an ⁇ rror measurement between "targetjocjig” and “comparison loc jig” .
- the loc sigs in this aggregation include all loc sigs for the associated BS that are in the "search irea”.
- s ⁇ arch trea A representation of the geographi ⁇ l area surrounding the bcation for all input be sigs. This input is used for determining extra information about the search area in problematic circumstances, search riteria: The criteria us ⁇ d in searching the bcation signature data base. The criteria may include the following: (a) "USE ALL LOC SIGS IN DB",
- the context adjuster 1326 performs the first set of potentially many adjustments to at least the confidences of lo ⁇ tio ⁇ hypotheses, and in some important embodim ⁇ nts, both the confid ⁇ nc ⁇ s and th ⁇ targ ⁇ t MS lo ⁇ tion ⁇ stimat ⁇ s provided by FOMs 1224 may b ⁇ adjust ⁇ d according to prevbus performances of the FOMs. More particularly, as mentioned above, the context adjuster adjusts confidences so that, assuming there is a sufficient density verified lo ⁇ tion signature clusters captured in the location signature data base 1320, the resulting b ⁇ tion hypothes ⁇ s output by th ⁇ context adjuster 1326 may be further processed uniformly and substantially without concern as to differences in accuracy between the first order models from which lo ⁇ tion hypotheses originate.
- the context adjuster adjusts lo ⁇ tion hypotheses both to environmental fa ⁇ ors (e.g., terrain, traffic, time of day, etc, as described in 30.1 above), and to how predictable or consistent each first order model (FOM) has been at bating prevbus targ ⁇ t MS's whos ⁇ lo ⁇ tions w ⁇ re subsequently verified.
- environmental fa ⁇ ors e.g., terrain, traffic, time of day, etc, as described in 30.1 above
- FOM first order model
- the lo ⁇ tion signature data base 1320 stores previously ⁇ ptur ⁇ d MS b ⁇ tion data including:
- the cont ⁇ xt adjuster 1326 us ⁇ s newly created target MS lo ⁇ tion hypotheses output by the FOM's as indexes or pointers into the b ⁇ tion signature data base for identifying other geographi ⁇ l areas where the target MS 140 is likely to be bated based on the verified MS lo ⁇ tion data in the lo ⁇ tio ⁇ signature data base.
- Confidence values for bcation hypothes ⁇ s may b ⁇ adjusted to account for current environmental chara ⁇ eristics such as month, day (weekday or week ⁇ nd), tim ⁇ of day, area type (urban, rural, etc.), traffic and/or weather when comparing how accurate the first order models have previously been in determining an MS location according to such environmental chara ⁇ eristics.
- current environmental chara ⁇ eristics such as month, day (weekday or week ⁇ nd), tim ⁇ of day, area type (urban, rural, etc.), traffic and/or weather when comparing how accurate the first order models have previously been in determining an MS location according to such environmental chara ⁇ eristics.
- such environmental characteristics are accounted for by utilizing a transmission area type scheme (as discussed in s ⁇ ion 5.9 abov ⁇ ) wh ⁇ n adjusting confidence values of bcation hypothes ⁇ s.
- the context adjuster 1326 may us ⁇ heuristic (fuzzy logic) rules to adjust the confidence values of location hypotheses from the first order models. Additionally, the context adjuster may also satisfy the folbwing criteria:
- the context adjuster may adjust locatbn hypothesis confidences due to BS failure(s),
- the context adjuster may have a calibration mod ⁇ for at ieast on ⁇ of:
- a first embodiment of the context adjuster is discuss ⁇ d immediately hereinbelow and in APPENDIX D. Howev ⁇ r, the present invention also includes oth ⁇ r ⁇ mbodim ⁇ nts of th ⁇ context adjuster. A second embodiment is also described in Appendix D so as to not overburd ⁇ n the reader and thereby chance losing perspe ⁇ ive of th ⁇ overall invention.
- Cont ⁇ xt adj ust ⁇ r( be typjist) This fun ⁇ ion adjusts the lo ⁇ tion hypotheses on the list, "loc hyp list", so that the confidences of the bcation hypotheses are determined more by empirical data than default values from the First Order Models 1224. That is, for each input location hypothesis, its confidence (and an MS bcation area estimate) may be exclusively determined here if there are enough verified location signatures available withir and/or surrounding the location hypothesis estimate.
- This function creates a new list of location hypothes ⁇ s from th ⁇ input list, * *loc_hyp_list", wh ⁇ r ⁇ in th ⁇ bcation hypothes ⁇ s on the new list are modified versions of thos ⁇ on th ⁇ input list
- Such corresponding output bcation hypoth ⁇ s ⁇ s will differ from their associated input location hypothesis by one or more of the following: (a) the "image trea" field (se ⁇ Fig.
- the bcation hypoth ⁇ s ⁇ s on the input list may have no change to its confidence or the area to which the co ⁇ fid ⁇ nce applies.
- the returned bcation hypotheses on the list are "adjusted” versions of "loc_hyp” in that both their target MS 140 bcation estimates, and confidenc ⁇ plac ⁇ d in such ⁇ stimates may be adjusted according to archival MS bcation information in th ⁇ location signature data bas ⁇ 1320.
- th ⁇ steps herein are also provided in flowchart form in Figs.26a through 26c.
- loc_ hyp list This is a list of one or more bcation hypotheses related to the input "loc hyp”. Each location hypothesis on “loc hyp list” will typically be substantially the same as the input "bc_hyp” exc ⁇ pt that th ⁇ re may now b ⁇ a n ⁇ w targ ⁇ t
- MS estimate in the field, "image trea", and/or the confidence value may be changed to reflect information of verified lo ⁇ tion signature clusters in the location signature data bas ⁇ .
- the cluster set to be the set of all MS location point estimat ⁇ s (e.g., the values of the "pt est” field of the location hypothesis data type), for the present FOM, such that: (a) these estimates are within a predetermined corresponding area (e.g., the "loc hyp.pt coveri ⁇ g” being such a predetermined corresponding ar ⁇ a, or more generally, this pred ⁇ t ⁇ rmi ⁇ ed corresponding area is d ⁇ termin ⁇ d as a fun ⁇ ion of the distance from an initial bcation estimate, e.g., "bc hyp.pt est", from the FOM), and
- a predetermined corresponding area e.g., the "loc hyp.pt coveri ⁇ g" being such a predetermined corresponding ar ⁇ a, or more generally, this pred ⁇ t ⁇ rmi ⁇ ed corresponding area is d ⁇ termin ⁇ d as a fun ⁇ ion of the distance from an initial bcation estimate, e.g., "bc
- th ⁇ term image cluster set (for a given First Order Model identified by "bc_hyp.FOM _ID”) to mean the set of verified location signature clusters whose MS lo ⁇ tion point estimates are in "th ⁇ cluster set”.
- an area containing th ⁇ "imag ⁇ cluster set” will be denoted as the "image cluster set area” or simply th ⁇ “imag ⁇ area” in some contexts. Further note that th ⁇ "image cluster set area” will be a "small” area encompassing the "imag ⁇ cluster set”.
- the imag ⁇ cluster s ⁇ t ar ⁇ a will be the smallest covering of cells from the mesh for the pres ⁇ nt FOM that cov ⁇ rs th ⁇ convex hull of th ⁇ imag ⁇ cluster set Note that preferably, each cell of each mesh for each FOM is substantially contained within a single (transmission) area type.
- the present FOM provides the correspondences or mapping betw ⁇ n elements of the cluster set and elem ⁇ nts of th ⁇ imag ⁇ clust ⁇ r s ⁇ t.
- This function returns a confidenc ⁇ valu ⁇ indicative of the target MS 140 being in the area for "imag ⁇ _area”. Note that the steps for this fun ⁇ ion are provided in flowchart form in Figs.27a and 27b.
- RETURNS A confidenc ⁇ value. This is a valu ⁇ indicative of the target MS being bated in the area represented by "image trea" (when it is assumed that for th ⁇ related "loc_hyp,” the “cluster set area” is the “loc_hyp.pt overing” and "bc_hyp.FOM_ID” is "FOM ID").
- the fun ⁇ ion "co ⁇ fidenc ⁇ _adjust ⁇ r,” (and functions called by this fun ⁇ ion) presuppose a framework or paradigm that requires som ⁇ discussion as w ⁇ ll as th ⁇ defining of terms.
- mapped cluster density to be the number of the verifi ⁇ d location signature clusters in an "image cluster set” per unit of area in th ⁇ "imag ⁇ cluster set area”.
- the mapped cluster density beco ⁇ s an important factor in d ⁇ t ⁇ rmining a confid ⁇ nc ⁇ valu ⁇ for an estimated area of a target MS such as, for example, the area represented by "image irea”.
- the mapped cluster density value requires modification before it can be utilized in the confidenc ⁇ calculation.
- MCD is sufficiently high so that it correlates (at least at a predetermined likelihood threshold level) with the actual target MS locatbn being in the "image cluster set area" when a FOM target MS location estimate is in the corresponding "cluster s ⁇ t area";
- the prediction mapped cluster density will typically be dependent on one or more area types.
- an ar ⁇ a is within a singl ⁇ area type
- such a "relativized mapped cluster density" measurem ⁇ nt for the area may be obtained by dividing the mapped clust ⁇ r d ⁇ nsity by th ⁇ pr ⁇ di ⁇ ion mapped clust ⁇ r density and taking the smaller of: the resulting ratio and 1.0 as the value for the relativized mapped cluster d ⁇ nsity.
- an area e.g., an image cluster set area
- a "composite prediction mapp ⁇ d cluster density” may be computed, wherein, a weighted sum is computed of the predi ⁇ ion mapped cluster densities for the portions of the area that is in each of the area types. That is, the weighting, for each of the single area type prediction mapped cluster densiti ⁇ s, is the fra ⁇ ion of the total area that this area type is.
- a "relativized composit ⁇ mapped cluster density" for the area here may also be computed by dividing the mapped cluster density by the composite predi ⁇ ion mapp ⁇ d clust ⁇ r d ⁇ nsity and taking the smaller of: the resulting ratio and 1.0 as th ⁇ valu ⁇ for th ⁇ relativized composite mapped cluster density. Accordingly, note that as such a relativized (composite) mapped cluster density for an image cluster set area increases/decreases, it is assu ⁇ d that th ⁇ confid ⁇ nc ⁇ of th ⁇ targ ⁇ t MS b ⁇ ing in th ⁇ image cluster set area should increase/decrease, respe ⁇ ively.
- a "reliability” field giving an indication as to the reliability of the "value” field.
- the reliability field is in the range [0, 1] with 0 indicating that the "value” field is worthless and the larger the value the more assurance can be put in “value” with maximal assurance indicated when "reliability” is I.
- the pres ⁇ nt fun ⁇ ion d ⁇ t ⁇ rmin ⁇ s an approximation to a predi ⁇ ion mapped cluster density, D, for an area type such that if an image cluster set area has a mapped cluster density > D, then there is a high exp ⁇ ation that th ⁇ targ ⁇ t MS 140 is in the image cluster set area. Note that there are a number of ⁇ mbodiments that may b ⁇ utiliz ⁇ d for this fun ⁇ ion. The steps herein are also provided in flowchart form in Figs.29a through 29h.
- OUTPUT predi ⁇ ion napped luster iensity This is a value giving an approximation to the prediction mapped cluster density for th ⁇ First Order Mod ⁇ l having identity, "FOM JD", and for the area type represented by
- th ⁇ predi ⁇ ion mapped cluster density may be more intense than some other computations but the cluster densities computed here ne ⁇ d not b ⁇ performed in real time target MS location processing. That is, the st ⁇ ps of this function may be performed only periodically (e.g., once a we ⁇ k), for ⁇ ach FOM and ⁇ ach ar ⁇ a typ ⁇ th ⁇ reby precomputing th ⁇ output for this function. Accordingly, th ⁇ valu ⁇ s obtain ⁇ d here may be stored in a table that is accessed during real time target MS locatbn processing. However, for simplicity, only the periodically performed steps are presented here.
- this fun ⁇ ion may be performed in real-time.
- a particular area, A may be provid ⁇ d such as the image area for a cluster set area, or, the portion of such an image area in a particular area type.
- area Jype is us ⁇ d in a statement of the embodiment of this fun ⁇ ion below, a comparable statement with "A" can be provided.
- the control component is denoted the control module 1 00.
- this control module manages or controls access to the run time bcation hypothesis storag ⁇ area 1410.
- the control module 1400 and the run time lo ⁇ tion hypothesis storage area 1410 may b ⁇ implemented as a blackboard system and/or an expert system. Accordingly, in the blackboard embodiment, , and the control module 1400 determines when new lo ⁇ tion hypotheses may be entered onto the blackboard from other processes such as the context adjuster 1326 as well as when bcation hypotheses may be output to the most likelihood estimator 1344.
- the folbwing is a bri ⁇ f description of ⁇ ach submodule included in the b ⁇ tion hypothesis analyzer 1332.
- a run-time lo ⁇ tion hypothesis storage area 1410 for retaining lo ⁇ tbn hypotheses during their processing by th ⁇ lo ⁇ tion hypoth ⁇ s ⁇ s analyzer This on be, for example, an expert system fa ⁇ bas ⁇ or a blackboard. Not ⁇ that in some of th ⁇ discussion hereinbelow, for simplicity, this module is referred to as a "blackboard”. Howev ⁇ r, h is not intended that such notation be a limitation on the present inventbn; L ⁇ ., th ⁇ term "blackboard” hereinafter will denote a run-time data repository for a data processing paradigm wherein the flow of control is substantially data-driven.
- An analytical reasoner module 1416 for determining if (or how well) lo ⁇ tion hypotheses are consistent with well known physi ⁇ l or heuristic constraints as, e.g., mentioned in (30.4) above. Note that this module may be a daemon or expert system rule base. (35.4) An historical lo ⁇ tion reasoner module 1424 for adjusting b ⁇ tion hypotheses' confidences according to how well the lo ⁇ tion signature chara ⁇ eristics (i.e., loc sigs) associated with a lo ⁇ tion hypothesis compare with "nearby" be sigs in the lo ⁇ tion signature data base as i ⁇ di ⁇ ted in (302) above. Note that this module may also be a daemon or expert system rule base.
- initial lo ⁇ tion estimates generated by the FOMs using different wireless signal measurements, from different signal transmission time intervals may hav ⁇ their corresponding dependent lo ⁇ tion hypothes ⁇ s utiiiz ⁇ d simultaneously for determining a most likely targ ⁇ t MS lo ⁇ tion ⁇ stimate.
- this modul ⁇ may also be daemon or expert system rule bas ⁇ . (35.6) hypothesis gen ⁇ rating modul ⁇ 1428 for g ⁇ nerating additional ta ⁇ tion hypoth ⁇ ses a ⁇ ording to, for example, MS location information not adequately utilized or modeled.
- lo ⁇ tion hypotheses may also be decomposed here if, for exampl ⁇ it is determined that a locatbn hypothesis includes an MS area estimate that has subareas with radially different charaaeristics such as an MS area estimate that includes an uninhabited area and a densely populated area. Additionally, the hypothesis generating module 1428 may generate "poor reception" lo ⁇ tbn hypothes ⁇ s that specify MS location areas of known poor reception that are "near" or interse ⁇ currently a ⁇ ive b ⁇ tion hypotheses.
- these poor reception b ⁇ tion hypothes ⁇ s may b ⁇ specially tagged (e.g., with a distin ⁇ ive FOM JD value or specific tag field) so that regardless of substantially any other lo ⁇ tion hypothesis confidence value overlapping such a poor reception area, such an area will maintain a confidence value of "unknown" (i.e., zero).
- location hypotheses generated from mobile base statbns 148 may also b ⁇ da ⁇ mon or expert system rule bas ⁇ .
- a blackboard system is the mechanism by which the last adjustments are performed on lo ⁇ tio ⁇ hypothes ⁇ s and by which additional lo ⁇ tion hypotheses may be generated.
- a blackboard system an b ⁇ described as a particular class of software that typically includes at least three basic components.
- a data base called the "blackboard,” whose stored information is commonly available to a collection of programming elements known as “daemons”, wherein, in the pres ⁇ nt invention, the blackboard includes information concerning the current status of the lo ⁇ tbn hypothes ⁇ s b ⁇ ing ⁇ valuat ⁇ d to determine a "most likely” MS b ⁇ tion estimate.
- this data base is provided by the run time lo ⁇ tio ⁇ hypothesis storage area 1410; (162) one or more a ⁇ ive (and typically opportunistic) knowledge sources, denoted conventionally as “daemons,” that create and modify the contents of the blackboard.
- th ⁇ knowledge sources or daemons in th ⁇ hypoth ⁇ sis analyzer include the analytical reasoner module 1416, the hypothesis generating module 1428, and the historical b ⁇ tion reasoner modul ⁇ 1416; (363) a control modul ⁇ that enables the realization of the behavior in a serial computing environment
- the control element orchestrates the flow of control betw ⁇ en the varbus daemons. This control module is provided by the control module
- control module 1400 and the run-time b ⁇ tion hypothesis storage area 1410 may be implemented as an expert system or as a fuzzy rule inferenci ⁇ g system, wherein the control module 1400 a ⁇ ivates or "fires" rul ⁇ s related to th ⁇ knowledge domain (in the present case, rubs relating to the accuracy of MS lo ⁇ tio ⁇ hypothesis ⁇ stimat ⁇ s), and wherein the rules provide a computational embodiment of, for example, constraints and heuristics related to the accuracy of MS lo ⁇ tio ⁇ estimates.
- the control module 1400 for the present embodim ⁇ nt is also used for orchestrating, coordinating and controlling the a ⁇ ivity of the individual rule bases of the lo ⁇ tion hypothesis analyzer (e.g.
- the analytical reasoner module 1416 As shown in Fig.7, the analytical reasoner module 1416, the hypothesis generating module 1428 , the histori ⁇ l b ⁇ tion reasoner module 1424, and the lo ⁇ tion extrapolator module 1432).
- the folbwing reference is incorporated h ⁇ rein by reference: Waterman, D. A. (1970). A guide to expert systems. Reading, MA: Addison-Wesley Publishing Company. MS Status Repository Embodiment
- the MS status repository 1338 is a run-time storage manag ⁇ r for storing location hypotheses from previous activations of the lo ⁇ tion engine 139 (as well as the output target MS bcation estimat ⁇ (s)) so that a targ ⁇ t MS may be tracked using target MS lo ⁇ tion hypothes ⁇ s from prevbus bcation engine 139 a ⁇ ivations to determine, for example, a movement of the target MS betwe ⁇ n evaluations of the target MS bcation.
- target MS lo ⁇ tion hypothes ⁇ s from prevbus bcation engine 139 a ⁇ ivations to determine, for example, a movement of the target MS betwe ⁇ n evaluations of the target MS bcation.
- thes ⁇ hypotheses may be used to resolve conflicts between hypotheses in a current a ⁇ ivation for locating the target MS; e.g., MS paths may be stored here for use in extrapolating a new lo ⁇ tion
- the most likelihood estimator 1344 is a module for determining a "most likely" location estimate for a target MS 140 being bated (e.g., as in (30.7) above).
- the most likelihood estimator performs an integration or summing of all location hypothesis confidence values for any geographic region(s) of interest having at least one lo ⁇ tion hypothesis that has b ⁇ n provided to the most likelihood estimator, and wherein the lo ⁇ tion hypothesis has a relatively (or sufficiently) high confidence. That is, the most likelihood estimator 1344 determines the area(s) within each such region having high co ⁇ fid ⁇ nc ⁇ s (or confidences above a threshold) as the most likely target MS 140 lo ⁇ tion estimates.
- this module utiliz ⁇ s an area m ⁇ sh, M, ov ⁇ r which to integrate, wherein the mesh cells of M are preferably smaller than the greatest lo ⁇ tion accuracy desired. That is, each cell, c, of M is assigned a confidence value i ⁇ di ⁇ ting a likelihood that the target MS 140 is bated in c, wherein the confidence value for c is determined by the confidence valu ⁇ s of the targ ⁇ t MS b ⁇ tion estimates provided to the most likelihood estimator 1344.
- the following steps are performed:
- each corresponding MS lo ⁇ tion area estimate, LAE is provided with a smallest covering, C ⁇ , of cells c from M.
- each of the cells of C ⁇ have their confidence values adjusted by adding to it the confidence value for LAE. Accordingly, if th ⁇ confidence of LEA is positive, then the cells of C ⁇ have their confidences increased. Alternatively, if th ⁇ confidence of LEA is negative, then the cells of a have their confidences decreased.
- interval [-1.0, + I.OJ represents the range in confidence values, and that this range has been partitioned into intervals, Int, having lengths of, e.g., 0.05, foreach interval, Int, perform a cluster analysis fun ⁇ ion for clustering cells with confidences that are in Int.
- a topographi ⁇ l-type map may b ⁇ constructed from the resulting cell clusters, wherein higher confidence areas are analogous to representations of areas having higher elevations.
- the m ⁇ sh cells of a poor reception area may have their confidences set to zero unless, e.g., there is a lo ⁇ tbn hypothesis derived from target MS b ⁇ tion data provided by a mobile base station 148 that (a) is near the poor reception area, (b) able to dete ⁇ that the target MS 140 is in the poor reception area, and (c) can relay target MS lo ⁇ tion data to the lo ⁇ tion center 142.
- MBS b ⁇ tio ⁇ hypothesis may take precedence.
- Qa may also have their confidences adjusted according to how near the celts c are to the covering. That is, th ⁇ assigning of confid ⁇ nc ⁇ s to c ⁇ ll meshes may be "fuzzified" in the terms of fuzzy logic so that the confidence value of each lo ⁇ tion hypothesis utilized by th ⁇ most lik ⁇ lihood ⁇ stimator 1344 is provid ⁇ d with a weighting fa ⁇ or depending on its proxity to the targ ⁇ t MS lo ⁇ tion estimate of the bcation hypothesis.
- th ⁇ following st ⁇ ps may be performed: (i) Determine the centroid of A, denoted Cent(A). (ii) Determine the centroid of the cell c denoted Q.
- th ⁇ most likelihood estimator 1344 upon receiving on ⁇ or more b ⁇ tion hypotheses from the hypothesis analyzer 1332, also performs some or all of the folbwing tasks:
- lo ⁇ tion hypothes ⁇ s having confidence l o values in the range [-0.02, 0.02] may be filtered here;
- this area is a convex hull including each of the MS area estimat ⁇ s from the received lo ⁇ tbn hypothes ⁇ s (wh ⁇ rein such lo ⁇ tion hypotheses have not been remov ⁇ d from consideration by the filtering process of (37.1)); (373) Det ⁇ rmin ⁇ s, onc ⁇ the integration is performed, one or more colle ⁇ ions of contiguous area mesh cells that may be deemed a 15 "most likely" MS ta ⁇ tion estimate, wherein each such colle ⁇ ion includes one or more area mesh cells having a high confidenc ⁇ value.
- the analytical reasoner applies constraint or "sanity" checks to the target MS estimates of the ta ⁇ tio ⁇ hypothes ⁇ s residing in the Run-tim ⁇ 20 Location Hypoth ⁇ sis Storage Area for adjusting the associated confidence values accordingly.
- thes ⁇ sanity checks involve "path" information. That is, this modul ⁇ determi ⁇ s if (or how well) lo ⁇ tion hypothes ⁇ s are consistent with well known physi ⁇ l constraints such as the laws of physics, in an area in which the MS (associated with the b ⁇ tbn hypothesis) is estimated to be bated.
- the predi ⁇ ion is for an area for which there is Lo ⁇ tion Base Station coverage, and no Lo ⁇ tion Base Station covering the area subsequently reports communkating with the target MS, then the predi ⁇ ions are incorre ⁇ and any current lo ⁇ tbn hypoth ⁇ sis from th ⁇ same FOM should not b ⁇ decreased here if it is outside of this Lo ⁇ tbn
- Bas ⁇ Station coverag ⁇ area.
- the analytical reasoner can access b ⁇ tion hypoth ⁇ s ⁇ s currently posted on the Run-time Lo ⁇ tion Hypoth ⁇ sis Storage Area. Additionally, it int ⁇ ra ⁇ s with the Pathway Database which contains information concerning the lo ⁇ tion of natural transportation pathways in the region (highways, rivers, etc.) and the Area Characteristics Database which contains information concerning, for exampl ⁇ , reasonable velocities that an be expected in varbus regions (for instance, speeds of 80 mph would not be reasonably expe ⁇ ed in dense urban areas).
- the Pathway Database which contains information concerning the lo ⁇ tion of natural transportation pathways in the region (highways, rivers, etc.)
- the Area Characteristics Database which contains information concerning, for exampl ⁇ , reasonable velocities that an be expected in varbus regions (for instance, speeds of 80 mph would not be reasonably expe ⁇ ed in dense urban areas).
- both speed and direction n be important constraints; e.g., even though a speed might be appropriate for an area, such as 20 mph in a dense urban area, if the dire ⁇ ion indicated by a time series of related lo ⁇ tion hypotheses is dire ⁇ ty through an ⁇ xtensiv ⁇ building compl ⁇ x having no through traffic routes, then a redu ⁇ bn in th ⁇ confidence of one or more of the lo ⁇ tion hypothes ⁇ s may be appropriate.
- Analytical Reason ⁇ r illustrating how such constraints may be implemented is provided in the folbwing sectbn. Note, however, that this embodim ⁇ nt analyz ⁇ s only lo ⁇ tion hypotheses having a non-negative confid ⁇ nc ⁇ value. Modul ⁇ s of an embodiment of th ⁇ analytical reason ⁇ r module 1416 are provid ⁇ d hereinbelow.
- Th ⁇ path comparison module 1454 implements the folbwing strategy: the confidence of a particular b ⁇ tion hypothesis is be increased (decreased) if it is (not) predi ⁇ ing a path that lies along a known transportation pathway (and the speed of the target MS is sufficiently high). For instance, if a time s ⁇ ries of target MS lo ⁇ tion hypotheses for a given FOM is predi ⁇ i ⁇ g a path of th ⁇ target MS that lies along an interstate highway, the confidenc ⁇ of th ⁇ currently a ⁇ ive lo ⁇ tion hypothesis for this FOM should, in general, be increased. Thus, at a high level the folbwing st ⁇ ps may b ⁇ performed:
- Th ⁇ velocity/acceleration calculation module 1458 computes velocity and/or acceleration estimates for the targ ⁇ t MS 140 using currently a ⁇ ive lo ⁇ tion hypotheses and previous ta ⁇ tion hypothesis estimates of the target MS. In one ⁇ mbodiment, for each FOM 1224 having a currently a ⁇ iv ⁇ lo ⁇ tbn hypothesis (with positive confidences) and a sufficient number of previous (reasonably recent) target MS b ⁇ tb ⁇ hypotheses, a velocity and/or acceleration may be calculated.
- such a velocity and/or acceleration may b ⁇ calculated using th ⁇ currently a ⁇ ive lo ⁇ tion hypoth ⁇ s ⁇ s and on ⁇ or more recent "most lik ⁇ ly" b ⁇ tions of the target MS output by the lo ⁇ tbn engin ⁇ 139. If th ⁇ estimated velocity and/or acceleration corresponding to a currently a ⁇ iv ⁇ location hypoth ⁇ sis is reasonable for the region, then its confid ⁇ nc ⁇ value may be incremented; if not, then its confid ⁇ nc ⁇ may b ⁇ d ⁇ crem ⁇ nted.
- the algorithm may be summarized as folbws: (a) Approximate speed and/or acceleration ⁇ stimates for currently a ⁇ iv ⁇ targ ⁇ t MS bcation hypotheses may b ⁇ provided using path information related to the currently a ⁇ ive b ⁇ tio ⁇ hypotheses and prevbus target MS lo ⁇ tion estimates in a manner similar to the description of the path comparison module 1454. Accordingly, a single confidence adjustment value may b ⁇ d ⁇ t ⁇ rmin ⁇ d for each currently a ⁇ ive lo ⁇ tion hypothesis for indi ⁇ ting the extent to which ' its corresponding velocity and/or acceleration ⁇ kulations are reasonable for its particular target MS bcation estimat ⁇ .
- This calculation is performed by retrieving information from the area chara ⁇ eristi ⁇ data base 1450 (e.g, Figs.6 and 7). Since each lo ⁇ tbn hypothesis includes timestamp data indi ⁇ ting when th ⁇ MS b ⁇ tion signals w ⁇ re received from th ⁇ targ ⁇ t MS, the velocity and/or acceleration associated with a path for a currently a ⁇ iv ⁇ lo ⁇ tion hypothesis an be straightforwardly approximated.
- a confidenc ⁇ adjustm ⁇ nt valu ⁇ , v ⁇ l_ok(i), indicating a likelihood that the velocity calculated for the i* currently active b ⁇ tio ⁇ hypothesis (having adequate corresponding path information) may be appropriate is calculated using for the environmental chara ⁇ eristics of the lo ⁇ tion hypothesis' target MS lo ⁇ tion estimate.
- th ⁇ area chara ⁇ eristics data base 1450 may include expected maximum velocities and/or accelerations for each area type and/or cell of a cell mesh of the cov ⁇ rag ⁇ area 120.
- velocities and/or accelerations above such maximum values may be indicative of anomalies in the MS location estimating process.
- the most recent lo ⁇ tion hypotheses yielding such ⁇ xtre ⁇ velocities and/or accelerations may have their confidence values decreased.
- the target MS lo ⁇ tion estimate includes a portion of an interstate highway
- an appropriate velocity might correspond to a speed of up to 100 miles per hour
- th ⁇ target MS locatbn estimat ⁇ might correspond to a speed of up to 100 miles per hour
- th ⁇ n a likely speed might be no more than 30 miles per hour with an maximum spe ⁇ d of 60 miles per hour
- tliat a similar confid ⁇ nc ⁇ adjustm ⁇ nt value, acc ok(i), may be provid ⁇ d for currently a ⁇ ive lo ⁇ tion hypotheses, l o wherein th ⁇ confidence adjustm ⁇ nt is related to the appropriateness of the acceleration ⁇ stimat ⁇ of th ⁇ targ ⁇ t MS.
- Th ⁇ attribute comparison module 1462 compares attribute values for b ⁇ tion hypotheses generated from different FOMs, and determines if the confidence of certain of the currently a ⁇ ive lo ⁇ tion hypotheses should be increased due to a similarity in related values for the attribute.
- ] of one or more lo ⁇ tbn hypotheses generated by one FOM, FOM[l] is compared with anoth ⁇ r attribute value for A derived from a set S f0H2 of one or more lo ⁇ tb ⁇ hypotheses generated by a different FOM, F0M[2] for determining if these attribute values cluster (i.e., are sufficiently close to one another) so that a currently a ⁇ ive lo ⁇ tbn hypoth ⁇ sis in S f0M [
- the attribute may b ⁇ a "target MS path data" attribute, wherein a value for the attribute is an estimated target MS path derived from b ⁇ tion hypothes ⁇ s generated
- the attribute might be, for example, one of a velocity and/or acceleration, wherein a value for the attribute is a vebcity and/or acceleration derived from lo ⁇ tion hypotheses generated by a fixed FOM over some (recent) time period.
- the attribute comparison module 1462 operates according to the folbwing premise: (38.1) for each of two or more currently a ⁇ iv ⁇ lo ⁇ tion hypoth ⁇ s ⁇ s (with, ⁇ .g., positive confidences) if:
- the attribute value might be an MS path ⁇ stimate, or alternatively an MS estimated vebcity, or an MS estimated acceleration
- the attribute value is deriv ⁇ d without using a FOM different from FOM K , and;
- each of these currently a ⁇ iv ⁇ b ⁇ tion hypoth ⁇ s ⁇ s, H, will hav ⁇ th ⁇ ir corresponding confidences increased. That is, thes ⁇ confidences will be increased by a confidence adjustment value or delta.
- cluster sufficiently well may have a numb ⁇ r of technical ⁇ mbodim ⁇ nts, including performing various clust ⁇ r analysis techniques wherein any clusters (according to some statistic) must satisfy a system set threshold for the members of the cluster being cbse ⁇ nough to on ⁇ anoth ⁇ r.
- Furth ⁇ r, upon d ⁇ t ⁇ rmining th ⁇ (any) b ⁇ tion hypoth ⁇ s ⁇ s satisfying (38.1), th ⁇ re are various techniques that may be us ⁇ d in d ⁇ termining a change or delta in confidences to b ⁇ appli ⁇ d.
- the confidenc ⁇ deltas for thes ⁇ examples are: [(I -cf)/(l + cf)] 2 (an additive d ⁇ lta), and, [1.0 + d n ] (a multiplicative delta), and a constan
- such an adaptive mechanism may repeatedly perform the folbwing steps: (a) modify such system parameters; (b) consequently a ⁇ ivate an instantiation of the lo ⁇ tbn engine 139 (having the modified system parameters) to process, as input a series of MS signal b ⁇ tion data that has be ⁇ n archived together with data corresponding to a verified MS bcation from which signal lo ⁇ tion data was transmitted (e.g., such data as is stored in th ⁇ lo ⁇ tbn signature data bas ⁇ 1320); and (c) th ⁇ determine if the modifications to the system parameters enhanc ⁇ d b ⁇ tion engine 139 performance in comparison to previous performanc ⁇ s.
- the computation for this module may b ⁇ summariz ⁇ d in the following steps: (a) Determine if any of the currently a ⁇ ive b ⁇ tion hypotheses satisfy the premise (38.1) for the attribute. Note that in making this determination, av ⁇ rag ⁇ distances and average standard deviations for the paths (velocities and/or accelerations) corresponding to currently a ⁇ ive location hypothes ⁇ s may be computed..
- this controller activates, for each such input lo ⁇ tbn hypothesis, the other submodules of th ⁇ analytical reason ⁇ r module 1416 (d ⁇ noted hereinafter as "adjustment submodules") with this lo ⁇ tion hypothesis.
- the analytical reasoner controller 1466 receives an output confidence adjustment valu ⁇ computed by each adjustment submodule for adjusting the confidence of this lo ⁇ tion hypoth ⁇ sis.
- the adjustment submodule computes a non-zero confidenc ⁇ adjustm ⁇ nt valu ⁇ that is returned to the analytical reasoner controller.
- the controller uses th ⁇ output from th ⁇ adjustm ⁇ nt submodul ⁇ s to compute an aggregate confidenc ⁇ adjustm ⁇ nt for th ⁇ corresponding lo ⁇ tion hypothesis.
- values for the eight types of confidenc ⁇ adjustment values are output to th ⁇ present controller for computing an aggregate confidence adjustment value for adjusting th ⁇ confid ⁇ nc ⁇ of th ⁇ currently a ⁇ ive lo ⁇ tion hypothesis presemly being analyzed by the analytical reasoner module 1416.
- vel_ok(i) I if th ⁇ velocity calculated for the i lh currently a ⁇ ive b ⁇ tion hypoth ⁇ sis (assuming ad ⁇ quat ⁇ corresponding path information) is typical for the area (and th ⁇ current environmental chara ⁇ eristics) of this lo ⁇ tbn hypothesis' target MS b ⁇ tion estimate; 02 if the vebcity calculated for the i' currently a ⁇ ive lo ⁇ tion hypothesis is near a maximum for the area (and th ⁇ current environmental chara ⁇ eristics) of this b ⁇ tion hypothesis' target MS lo ⁇ tion estimate;. 0 if th ⁇ velocity calculated is above the maximum.
- a ⁇ _ok(i) I if the acceleration calculated for the , 1 currently a ⁇ ive location hypothesis (assuming adequate corresponding path information) is typical for the area (and th ⁇ current environmental chara ⁇ eristics) of this bcation hypothesis' target MS lo ⁇ tion estimate; 02 if th ⁇ acceleration calculated for the t currently a ⁇ ive b ⁇ tion hypoth ⁇ sis is n ⁇ ar a maximum for the area (and the current environmental charatteristics) of this lo ⁇ tion hypoth ⁇ sis' targ ⁇ t MS lo ⁇ tio ⁇ ⁇ stimate;. 0 if th ⁇ acceleration calculated is above the maximum.
- simiiar_path(i) I if the lo ⁇ tion hypothesis "i" satisfies (38.1) for the target MS path data attribute; 0 otherwise.
- velocity_similar(i) I if th ⁇ lo ⁇ tio ⁇ hypoth ⁇ sis "i” satisfies (38.1) for the target MS velocity attribute; 0 otherwise.
- acce te ratio n_si miiar (i) I if th ⁇ b ⁇ tion hypoth ⁇ sis "i” satisHes (38.1) for th ⁇ target MS acceleration attribute; 0 otherwise.
- ⁇ xtrapolation_chk(i) I if the lo ⁇ tion hypothesis "i” is "near" a previously predi ⁇ ed MS b ⁇ tion for the target MS; 0 otherwise.
- th ⁇ re is a corresponding locatbn e ⁇ gin ⁇ 139 system s ⁇ tabl ⁇ parameter whose value may be determined by repeated activation of the adaptation engin ⁇ 1382. Accordingly, for ⁇ ach of the confidence adjustm ⁇ nt typ ⁇ s, T, above, there is a corresponding system s ⁇ table parameter, "alphaJT, that is tunable by th ⁇ adaptation ⁇ ngi ⁇ 1382. Accordingly, the folbwing high level program segment illustrates the aggregate confidenc ⁇ adjustm ⁇ nt valu ⁇ comput ⁇ d by th ⁇ Analytical Reasoner Controller.
- target_MSJoc_hyps ⁇ — g ⁇ t al) currently a ⁇ ' rv ⁇ lo ⁇ tbn hypoth ⁇ s ⁇ s, H, identifying the present target ; foreach currently a ⁇ ive locatbn hypothesis, hyp(i), from target_MS_loc_hyps do
- the historical lo ⁇ tion reasoner modul ⁇ 1424 may b ⁇ , for example, a daemon or expert system rule base.
- the module adjusts the confidences of currently a ⁇ iv ⁇ location hypoth ⁇ s ⁇ s by using (from location signature data base 1320) historical signal data correlated with: (a) verified MS locations (e.g. tacations verified when emergency personnel co-locate with a target MS location), and (b) various environmental fa ⁇ ors to evaluate how consistent the location signature cluster for an input location hypothesis agrees with such historical signal data.
- This reason ⁇ r will increase/decrease th ⁇ confidence of a currently active location hypothesis depending on how well its associated loc sigs correlate with the loc sigs obtained from data in the location signature data base. Not ⁇ that th ⁇ ⁇ mbodim ⁇ nt hereinbelow is but one of many ⁇ mbodim ⁇ nts that may adjust th ⁇ confid ⁇ nce of currently a ⁇ ive location hypothes ⁇ s appropriately. Accordingly, it is important to note other embodiments of the historical location reason ⁇ r functionality are within the scope of the present invention as one skilled in the art will appreciate upon examining the techniques utilized within this specification.
- calculations of a confid ⁇ nce adjustm ⁇ nt factor may be det ⁇ rmin ⁇ d using Monte Carlo techniqu ⁇ s as in the context adjuster 1326.
- Each such embodim ⁇ t generates a measurement of at least one of the similarity and th ⁇ discrepancy between the signal chara ⁇ eristics of the verified lo ⁇ tion signature clusters in th ⁇ location signature data bas ⁇ and th ⁇ bcation signature clust ⁇ r for an input currently a ⁇ iv ⁇ bcation hypothesis, "loc_hyp”.
- the embodiment hereinbelow provides one exampl ⁇ of the fun ⁇ ionality that an be provid ⁇ d by th ⁇ historical location r ⁇ ason ⁇ r 1424 ( ⁇ ith ⁇ r by a ⁇ ivating th ⁇ following programs as a daemon or by transforming various program segments into the consequ ⁇ ts of expert system rules).
- the pr ⁇ s ⁇ nt ⁇ mbodim ⁇ nt g ⁇ n ⁇ rat ⁇ s such a confid ⁇ nc ⁇ adjustm ⁇ nt by th ⁇ folbwing st ⁇ ps:
- the lo ⁇ tion extrapolator 1432 works on the folbwing premise: if for a currently a ⁇ ive locatbn hypothesis there is sufficient previous related information regarding estimates of the targ ⁇ t MS (e.g., from the same FOM or from using a "most likely" prevbus target MS estimat ⁇ output by the lo ⁇ tion engine 139), then an extrapolation may be performed for predicting future targ ⁇ t MS lo ⁇ tions that an be compared with new lo ⁇ tion hypothes ⁇ s provided to the blackboard.
- interpolation routines e.g., conventional algorithms such as Lagrange or Newton polynomials
- n b ⁇ determined according to how well if the lo ⁇ tion hypothesis "i" . That is, this confidenc ⁇ adjustment value will be larger as the new MS estimate and th ⁇ predkt ⁇ d estimate becom ⁇ closer together.
- such predi ⁇ ions are bas ⁇ d solely on previous target MS b ⁇ tion estimat ⁇ s output by lo ⁇ tion engin ⁇ 139.
- substantially every currently a ⁇ ive location hypothesis an be provided with a confid ⁇ nc ⁇ adjustment value by this module once a sufficient number of prevbus targ ⁇ t MS bcation estimates have been output
- a value, extrapolatio ⁇ _chk(i) that represents how accurately th ⁇ new currently a ⁇ ive lo ⁇ tion hypothesis (identified here by "i") matches the predi ⁇ ed bcation is determined.
- the hypothesis generating module 1428 is used for gen ⁇ rating additional lo ⁇ tbn hypoth ⁇ s ⁇ s according to, for ⁇ xampl ⁇ , MS lo ⁇ tbn information not ad ⁇ quately utiliz ⁇ d or mod ⁇ l ⁇ d.
- lo ⁇ tion hypoth ⁇ ses may also b ⁇ decomposed here if, for ⁇ xampl ⁇ it is determined that a lo ⁇ tbn hypoth ⁇ sis includes an MS area estimate that has subareas with radially different characteristics such as an ar ⁇ a that includes an uninhabited area and a densely populated area.
- the hypothesis gen ⁇ rating module 1428 may generate "poor reception" lo ⁇ tion hypothes ⁇ s that specify MS b ⁇ tion areas of known poor reception that are "near" or i ⁇ t ⁇ rs ⁇ currently a ⁇ ive bcation hypotheses.
- these poor reception location hypoth ⁇ s ⁇ s may b ⁇ specially tagged (e.g., with a distin ⁇ ive FOMJD value or specific tag field) so that regardless of substantially any other lo ⁇ tbn hypothesis confidence value overlapping such a poor reception area, such an area will maintain a confidenc ⁇ value of "unknown" (i.e., zero).
- substantially the only exc ⁇ ption to this constraint is location hypoth ⁇ ses gen ⁇ rated from mobil ⁇ base stations 148.
- Any collection of mobile ele ⁇ ronics (denoted mobile bcation unit) that is able to both estimat ⁇ a location of a targ ⁇ t MS 140 and communicate with th ⁇ base station network may be utilized by th ⁇ pr ⁇ s ⁇ nt inv ⁇ ntion to more accurately locate the target MS.
- Such mobile location units may provid ⁇ greater targ ⁇ t MS lo ⁇ tion accuracy by, for example, homing in on the target MS and by transmitting additional MS lo ⁇ tion information to the bcation cent ⁇ r 142.
- Th ⁇ re are a numb ⁇ r of embodiments for such a mobile location unit contemplated by the present invention.
- such the ele ⁇ ronics of the mobile location unit may be little more than an onboard MS 140, a s ⁇ ctored/dire ⁇ ional antenna and a controller for communicating between th ⁇ .
- the onboard MS is used to communicate with the location cent ⁇ r 142 and possibly the target MS 140, while the antenna monitors signals for homing in on the target MS 140.
- a GPS rec ⁇ iv ⁇ r may also b ⁇ incorporated so that th ⁇ location of th ⁇ mobil ⁇ location unit may b ⁇ d ⁇ t ⁇ r in ⁇ d and consequently an estimat ⁇ of th ⁇ location of th ⁇ targ ⁇ t MS may also b ⁇ d ⁇ t ⁇ rmin ⁇ d.
- th ⁇ location of th ⁇ mobil ⁇ location unit may b ⁇ d ⁇ t ⁇ r in ⁇ d and consequently an estimat ⁇ of th ⁇ location of th ⁇ targ ⁇ t MS may also b ⁇ d ⁇ t ⁇ rmin ⁇ d.
- How ⁇ v ⁇ r, such a mobil ⁇ location unit is unlikely to be able to determin ⁇ substantially more than a dire ⁇ ion of the target MS 140 via the se ⁇ ored/dir ⁇ ctional antenna without furth ⁇ r bas ⁇ station infrastru ⁇ ure cooperation in, for example, determining the transmission power l ⁇ vel of the target MS or varying this power iev ⁇ l.
- th ⁇ targ ⁇ t MS or th ⁇ mobil ⁇ location unit l ⁇ av ⁇ s the coverag ⁇ ar ⁇ a 120 or resid ⁇ s in a poor communication ar ⁇ a it may b ⁇ difficult to accurately determine where th ⁇ target MS is located.
- Non ⁇ -th ⁇ -l ⁇ ss, such mobile bcation units may be sufficient for many situations, and in fact the present invention contemplates th ⁇ ir us ⁇ .
- th ⁇ present invention includes a mobile location unit that is also a scaled down version of a base station 122.
- a mobile bas ⁇ station or MBS 148 includes at least an onboard MS 140, a se ⁇ ored/dir ⁇ ionai antenna, a GPS rec ⁇ iv ⁇ r, a scal ⁇ d down base station 122 and sufficient components (including a controller) for integrating th ⁇ capabilities of thes ⁇ d ⁇ vic ⁇ s
- an ⁇ ha ⁇ c ⁇ d autonomous MS mobil ⁇ location syst ⁇ can be provid ⁇ d that can b ⁇ effectively used in, for exampl ⁇ , emergency vehicles, air planes and boats.
- th ⁇ description that follows below describes an embodiment of an MBS 148 having the above m ⁇ ntioned components and capabilities for use in a vehicle.
- th ⁇ MBS 148 As a consequ ⁇ nc ⁇ of th ⁇ MBS 148 being mobile, there are fundam ⁇ ntal diff ⁇ r ⁇ nces in the operation of an MBS in comparison to other types of BS's 122 (152).
- other types of base stations have fix ⁇ d locations that are precisely d ⁇ t ⁇ rmin ⁇ d and known by the lo ⁇ tion center, whereas a locatbn of an MBS 148 may b ⁇ known only approximately and thus may require r ⁇ p ⁇ ated and frequent re-estimating.
- MBS's may be used in areas (such as wilderness areas) where there may be no other means for reliably and cost effectively locating a target MS 140 (i.e., there may be insufficient fixed location BS's coverage in an area).
- an MBS may include components for communicating with the fixed location BS network infrastructure and th ⁇ location c ⁇ nt ⁇ r 142 via an on-board transceiver 1512 that is effectively an MS 140 integrated into the bcation subsystem 1508.
- the MBS 148 travels through an area having poor infrastru ⁇ ure signal cov ⁇ rag ⁇ , th ⁇ n th ⁇ MBS may not b ⁇ abl ⁇ to communicate reliably with the bcation center 142 (e.g., in rural or mountainous areas having reduced wirel ⁇ ss telephony cov ⁇ rag ⁇ ).
- th ⁇ MBS 148 must b ⁇ capable of functioning substantially autonomously from the location center. In one embodiment, this implies that each MBS 148 must be capable of estimating both its own bcation as well as th ⁇ bcation of a targ ⁇ t MS 140.
- the MBS 148 since the MBS 148 , includes a scaled down version of a BS 122 (denot ⁇ d 1522 in Fig. 11), it is capable of performing most typical BS 122 tasks, albeit on a reduc ⁇ d scale.
- th ⁇ bas ⁇ station portion of the MBS 148 can: (a) raise lower its pilot chann ⁇ l signal strength,
- the MBS 148 can, if it becom ⁇ s the primary base statbn communicating with the MS 140, request the MS to raise lower its power or, more generally, control the communication with the MS (via the base station components 1522).
- the pilot chann ⁇ l for the MBS is preferably a nonstandard pilot chann ⁇ l in that it should not be identiii ⁇ d as a conventional t ⁇ l ⁇ phony traffic b ⁇ aring BS 122 by MS's s ⁇ eking normal telephony communication.
- a target MS 140 requesting to be located may, depending on its ⁇ pabiiities, either automatically configure itself to scan for certain predetermined MBS pilot channels, or be instru ⁇ ed via th ⁇ fix ⁇ d bcation base station n ⁇ twork (equivalently BS infrastru ⁇ ur ⁇ ) to scan for a c ⁇ rtain pred ⁇ t ⁇ rmined MBS pilot chann ⁇ l.
- the MBS 148 has an additional advantage in that it can substantially increas ⁇ the reliability of communication with a target MS 140 in comparison to the base statbn infrastru ⁇ ure by being able to move toward or track th ⁇ target MS 140 ev ⁇ n if this MS is in (or mov ⁇ s into) a r ⁇ duc ⁇ d infrastru ⁇ ure bas ⁇ station n ⁇ twork cov ⁇ rage area.
- an MBS 148 may preferably use a dire ⁇ ional or smart antenna 1526 to more accurately locate a dire ⁇ ion of signals from a target MS 140.
- the swe ⁇ ping of such a smart antenna 1526 provid ⁇ s dire ⁇ ional information regarding signals rec ⁇ ived from the target MS 140.
- such dire ⁇ ional information is determined by the signal propagation delay of signals from the target MS 140 to the angular se ⁇ ors of one of more dire ⁇ ional antennas 1526 on-board the MBS 148.
- an exampl ⁇ of the operation of an MBS 148 in the context of responding to a 911 emergency call is given.
- this figure illustrates the primary state transitions between thes ⁇ MBS 148 states, wherein the solid state transitions are indicativ ⁇ of a typical "ideal" progression wh ⁇ locating or tracking a targ ⁇ t MS 140, and th ⁇ dash ⁇ d state transitions are the primary state reversions due, for ⁇ xa ple, to difficulties in locating the target MS 140.
- the MBS 148 may b ⁇ in an ina ⁇ ' rve state 1700, wherein the MBS bcation subsystem 1508 is eff ⁇ ively available for voice or data communication with the fix ⁇ d location bas ⁇ station n ⁇ twork, but th ⁇ MS 140 locating capabilities of th ⁇ MBS are not a ⁇ ive.
- the MBS e.g., a police or rescue vehicl ⁇
- the MBS may ⁇ nt ⁇ r an a ⁇ iv ⁇ state 1704 once an MBS operator has logged onto th ⁇ MBS bcation subsystem of th ⁇ MBS, such logging b ⁇ ing for authentication, verification and journaling of MBS 148 ev ⁇ nts.
- the MBS may be listed by a 911 emergency center and/or the location cent ⁇ r 142 as eligible for service in responding to a 911 requ ⁇ st. From this state, the MBS 148 may transition to a ready state 1708 signifying that the MBS is ready for us ⁇ in locating and/or int ⁇ rc ⁇ pting a targ ⁇ t MS 140. That is, th ⁇ MBS 148 may transition to th ⁇ ready state 1708 by performing the folbwing steps: (la) Synchronizing the timing of the bcation subsystem 1508 with that of th ⁇ base station n ⁇ twork infrastructure.
- the MBS 148 will be at a predetermined or well known bcation so that the MBS time synchronization may adjust for a known amount of signal propagation delay in the synchronization signal, (lb) Establishing the location of the MBS 148. In one ⁇ mbodim ⁇ nt, this may b ⁇ accomplished by, for example, an MBS operator identifying th ⁇ predetermined or well known bcation at which the MBS 148 is located.
- th ⁇ ready state 1708 As the MBS 148 moves, it has its location rep ⁇ atedly (re)-estimat ⁇ d via, for example, GPS signals, location center I42S location estimates from the base stations 122 (and 152), and an on-board deadreckoning subsystem 1527 having an MBS location ⁇ stimator according to th ⁇ programs d ⁇ scrib ⁇ d hereinbelow. Howev ⁇ r, not ⁇ that th ⁇ accuracy of th ⁇ base station time synchronization (via th ⁇ ribidiu oscillator 1520) and the accuracy of the MBS 148 b ⁇ tion may ne ⁇ d to both b ⁇ periodically recalibrated according to (la) and (lb) abov ⁇ .
- a 911 signal is transmitted by a targ ⁇ t MS 140
- this signal is transmitted, via th ⁇ fixed bcation base station infrastructure, to th ⁇ 911 emerg ⁇ ncy center and the locatbn cent ⁇ r 142, and assuming th ⁇ MBS 148 is in th ⁇ ready state 1708, if a corresponding 911 ⁇ m ⁇ rg ⁇ ncy r ⁇ qu ⁇ st is transmitted to th ⁇ MBS (via the base station infrastru ⁇ ure) from the 911 emerg ⁇ ncy c ⁇ nt ⁇ r or th ⁇ location cent ⁇ r, th ⁇ n th ⁇ MBS may transition to a s ⁇ k state 1712 by performing th ⁇ following steps:
- (2a) Communicating with, for exampl ⁇ , th ⁇ 911 ⁇ m ⁇ rg ⁇ cy response center via the fixed bcation base station n ⁇ twork to rec ⁇ iv ⁇ th ⁇ PN code for the target MS to be located (wherein this communication is performed using the MS-like transceiver 1512 and/or the MBS operator telephony interface 1524).
- (2b) Obtaining a most r ⁇ c ⁇ nt target MS location estimate from eith ⁇ r the 911 emergency cent ⁇ r or the bcation cent ⁇ r 142.
- th ⁇ MBS may comm ⁇ c ⁇ toward th ⁇ targ ⁇ t MS location estimate provided. Note that it is likely that the MBS is not initially in dire ⁇ signal conta ⁇ with the target MS. Accordingly, in the seek state 1712 the folbwing steps may be, for exampl ⁇ , performed:
- the lo ⁇ tion cent ⁇ r 142 or th ⁇ 911 emerg ⁇ ncy response center may inform the target MS, via the fixed bcation base station network, to lower its threshold for soft hand-off and at least periodically boost its bcation signal strength. Additionally, th ⁇ targ ⁇ t MS may be informed to s ⁇ n for the pilot channel of th ⁇ MBS 148.
- the BS 148 repeatedly attempts to d ⁇ t ⁇ a signal from th ⁇ targ ⁇ t MS using th ⁇ PN cod ⁇ for the target MS. (3e) The MBS 148 repeatedly estimates its own location (as in other states as well), and receives MBS location estimat ⁇ s from th ⁇ location c ⁇ nter.
- Th ⁇ MBS 148 Since the MBS 148 is at least in soft hand-off with the targ ⁇ t MS 140, th ⁇ MBS an estimate the dire ⁇ ion and distance of th ⁇ targ ⁇ t MS itself using, for ⁇ xampl ⁇ , dete ⁇ ed target MS signal strength and TOA as well as using any rec ⁇ nt location c ⁇ nt ⁇ r targ ⁇ t MS bcation ⁇ stimat ⁇ s. (4d) Th ⁇ MBS 148 repeatedly provides the MBS operator with new target MS bcation estimates provided using MS location estimates provid ⁇ d by th ⁇ MBS its ⁇ lf and by th ⁇ location center via the fixed bcation base station network.
- the target MS 140 may switch to using the MBS 148 as its primary base station.
- the MBS enters a control state 1720, wherein the following steps are, for example, performed: (5a) The MBS 148 repeatedly estimat ⁇ s its own lo ⁇ tion.
- the location center 142 provides new target MS and MBS location estimat ⁇ s to the MBS 148 via the network of base stations 122 (152).
- the MBS 148 estimates the dire ⁇ ion and distanc ⁇ of th ⁇ targ ⁇ t MS 140 itself using, for example, dete ⁇ ed target MS signal strength and TOA as w ⁇ ll as using any rec ⁇ nt bcation c ⁇ nter target MS location estimates.
- the MBS 148 repeatedly provid ⁇ s th ⁇ MBS operator with new target MS io ⁇ tion estimates provid ⁇ d using MS location ⁇ stimat ⁇ s provid ⁇ d by th ⁇ MBS its ⁇ lf and by th ⁇ locatbn center 142 via the fix ⁇ d location base station network.
- the MBS 148 becomes the primary base statbn for the targ ⁇ t MS 140 and therefore controls at least the signal strength output by the target MS. Note, there can be more than one MBS 148 tracking or locating an MS 140. Th ⁇ re can also b ⁇ more than on ⁇ targ ⁇ t MS 140 to b ⁇ tracked concurrently and each targ ⁇ t MS b ⁇ ing tracked may b ⁇ stationary or moving.
- An MBS 148 uses MS signal characteristic data for locating th ⁇ MS 140. Th ⁇ MBS 148 may us ⁇ such signal chara ⁇ eristic data to facilitate determining wh ⁇ th ⁇ r a given signal from the MS is a "dire ⁇ shot" or an multipath signal. That is, in o ⁇ ⁇ mbodim ⁇ nt, th ⁇ MBS 148 attempts to determine or detect whether an MS signal transmission is received dire ⁇ ly, or wh ⁇ th ⁇ r th ⁇ transmission has been reflected or deflected. For example, the MBS may determine whether the expe ⁇ ed signal strength, and TOA agr ⁇ in distanc ⁇ estimates for th ⁇ MS signal transmissions.
- oth ⁇ r signal characteristics may also be used, if there are sufficient ele ⁇ ronics and processing available to the MBS 148; i.e., d ⁇ termining signal phas ⁇ and/or polarity as oth ⁇ r indications of receiving a "dire ⁇ shot" from an MS 140.
- the MBS 148 (Fig. II) includes an MBS controller 1533 for controlling th ⁇ bcation capabilities of the MBS
- th ⁇ MBS controller 1533 initiates and controls the MBS state changes as described in Fig. 12 above.
- the MBS controller 1533 also communi ⁇ tes with the locatbn controller 1535, wherein this latter controller controls MBS a ⁇ ivities related to MBS location and target MS location; e.g., this performs the program, "mobile_bas ⁇ _station_controller" described in APPENDIX A hereinbelow.
- the bcation controller 1535 rec ⁇ iv ⁇ s data input from an event generator 1537 for generating event records to be provided to the lo ⁇ tion controller 1535.
- records may b ⁇ generated from data input received from: (a) the vehicl ⁇ ov ⁇ m ⁇ nt d ⁇ t ⁇ or 1539 indi ⁇ ting that th ⁇ MBS 148 has mov ⁇ d at least a pred ⁇ t ⁇ rmin ⁇ d amount and/or has chang ⁇ d dire ⁇ ion by at i ⁇ ast a predetermined a ⁇ gl ⁇ , or (b) the MBS signal processing subsystem 1541 indicating that the additional signal measurem ⁇ nt data has b ⁇ n received from eith ⁇ r th ⁇ bcation c ⁇ nt ⁇ r 142 or the target MS 140.
- the MBS signal processing subsystem 1541 is similar to the signal processing subsystem 1220 of the location center 142. may hav ⁇ multipl ⁇ command sch ⁇ dulers.
- a scheduler 1528 for commands related to communicating with the bcation center 142 a schedul ⁇ r 1530 for commands related to GPS communication (via GPS receiver 1531), a schedul ⁇ r 1529 for commands related to th ⁇ frequency and granularity of the reporting of MBS changes in dire ⁇ ion and/or position via the MBS dead reckoning subsystem 1527 (note that this schedul ⁇ r is potentially optional and that such commands may be provided dire ⁇ ly to the deadr ⁇ ckoning ⁇ stimator 1544), and a scheduler 1532 for communicating with the targ ⁇ t MS(s) 140 being located.
- ⁇ ach MBS 148 has a plurality of MBS location ⁇ stimators (or h ⁇ reinafter also simply referred to as location estimators) for determining the bcation of the MBS.
- Each such bcation estimator computes MBS locatbn information such as MBS lo ⁇ tion estimates, changes to MBS bcation estimates, or, an MBS location estimator may be an interface for buffering and/or translating a previously computed MBS lo ⁇ tion estimate into an appropriate format.
- the MBS bcation module 1536 which determines the location of the MBS, may include th ⁇ following MBS bcation ⁇ stimators 1540 (also d ⁇ not ⁇ d baseline location estimators):
- an MBS operator locatbn estimator 1540c (not individually shown) for buffering and/or translating manual MBS location entri ⁇ s received from an MBS location operator
- an LBS location ⁇ stimator I540d (not individually shown) for th ⁇ activating and d ⁇ activating of LBS's 152.
- LBS low cost location base statbns 152
- the MBS 148 may be able to quickly us ⁇ th ⁇ locatbn information relating to the bcation base stations for determining its location by using signal chara ⁇ eristics obtained from the LBSs 152.
- each of the MBS baseline location ⁇ stimators 1540 such as thos ⁇ abov ⁇ , provid ⁇ an actual MBS locatbn rather than, for example, a change in an MBS bcation.
- additional MBS baselin ⁇ location estimators 1540 may be easily integrated into the MBS bcation subsystem 1508 as such baseline bcation estimators become available.
- a baseline bcation estimator that receives MBS lo ⁇ tion estimates from reflective codes provid ⁇ d, for exampl ⁇ , on streets or street signs can b ⁇ straightforwardly incorporated into the MBS locatbn subsystem 1508.
- GPS technologi ⁇ s may b ⁇ sufficiently accurate; how ⁇ v ⁇ r, GPS technologies: (a) may require a relatively longtime to provide an initial location estimat ⁇ ( ⁇ .g., greater than 2 minutes); (b) wh ⁇ n GPS communication is disturbed, it may require an equally long time to provide a new location estimate; (c) clouds, buildings and/or mountains can prevent bcation estimates from being obtained; (d) in some cases signal reflections can substantially skew a location estimate.
- an MBS 148 may be able to use triangulation or trilateraiization technologies to obtain a location estimate; howev ⁇ r, this assumes that there is sufficient (fixed location) infrastructure BS coverage in the area the MBS is located.
- an MBS is provided with a plurality of lo ⁇ tion technologies, each supplying an MBS location estimat ⁇ .
- much of the archite ⁇ ure of the location engine 139 could be incorporated into an MBS 148.
- th ⁇ following FOMs 1224 may hav ⁇ similar location mod ⁇ ls incorporat ⁇ d into the MBS:
- a variation of the artificial neural net based FOMs 1224 may be used to provide MBS location estimat ⁇ s via, for ⁇ xampl ⁇ , learned associations between fixed location
- an LBS bcation FOM 1224 for providing an MBS with the ability to activate and dea ⁇ ivat ⁇ LBS's to provid ⁇ (positive)
- MBS bcation ⁇ stimates as w ⁇ ll as n ⁇ gativ ⁇ MBS bcation regions (i. ⁇ ., regions where the MBS is unlikely to be since one or more LBS's are not dete ⁇ ed by the MBS transceiver); (d) one or more MBS location reasoning agents and/or a locatbn estimate heuristic agents for resolving MBS bcation estimat ⁇ conflicts and providing greater MBS bcation estimat ⁇ accuracy.
- an alternative embodiment is to rely on the bcation center 142 to perform the computations for at least som ⁇ of these MBS FOM models. That is, since each of the MBS location models mention ⁇ d immediately above require communication with the network of fixed location BS's 122 (152), it may be advantageous to transmit MBS bcation estimating data to the location center 142 as if the MBS were another MS 140 for the location center to locate, and thereby rely on the location estimation capabilities at the location center rather than duplicate such models in the MBS 148.
- the advantages of this approach are that:
- an MBS is likely to be able to use less expensive processing power and software than that of the location c ⁇ nt ⁇ r;
- an BS is likely to require substantially less memory, particularly for data bases, than that of the location cent ⁇ r.
- th ⁇ r ⁇ are confid ⁇ nc ⁇ valu ⁇ s assigned to the locations output by the various location ⁇ stimators 1540.
- the confidence for a manual entry of location data by an MBS operator may be rated the highest and followed by the confidence for (any) GPS bcation data, followed by the confidenc ⁇ for (any) location center location 1 2 estimates, followed by the confid ⁇ nc ⁇ for (any) location ⁇ stimat ⁇ s using signal characteristic data from LBSs.
- prioritization may vary dep ⁇ nding on, for instanc ⁇ , the radio coverag ⁇ area 120.
- MBS bcation data received from the GPS and locatbn center their confidences may vary according to the area in which the MBS 148 resides. That is, if it is known that for a given area, there is a reasonable probability that a GPS signal may suffer multipath distortions and that the location center has in the past provided reliable location estimates, then the confidences for these two location sources may be reversed.
- MBS operators may be requested to occasionally manually enter the location of the MBS 148 when the MBS is stationary for determining and/or calibrating the accuracy of various MBS lo ⁇ tion ⁇ stimators.
- th ⁇ MBS 148 may us ⁇ deadreckoning information provided by a deadreckoning MBS bcation estimator 1544 whereby the MBS may obtain MBS deadreckoning location change estimates.
- the deadreckoning MBS bcation estimator 1544 may use, for example, an onboard gyroscope 1550, a wheel rotation measurement devic ⁇ ( ⁇ .g., odometer) 1554, and optionally an accel ⁇ rom ⁇ t ⁇ r (not shown).
- an onboard gyroscope 1550 a wheel rotation measurement devic ⁇ ( ⁇ .g., odometer) 1554
- an accel ⁇ rom ⁇ t ⁇ r not shown.
- a d ⁇ adr ⁇ cko ⁇ ing MBS location ⁇ sti ator 1544 periodically provid ⁇ s at l ⁇ ast MBS distance and dire ⁇ ional data related to MBS move ⁇ nts from a most recent MBS bcation ⁇ stimate.
- th ⁇ d ⁇ adr ⁇ ckoning MBS bcation estimator 1544 outputs a seri ⁇ s of m ⁇ asurements, wherein each such measurement is an estimated change (or delta) in the position of the MBS 148 between a request input timestamp and a closest time prior to the timestamp, wherein a previous deadreckoning terminated.
- ⁇ ach d ⁇ adr ⁇ ckoning bcation chang ⁇ estimate includes the folbwing fields:
- the "latest timestamp” is the timestamp input with a request for deadreckoning bcation data
- th ⁇ "earliest timestamp” is the tim ⁇ stamp of th ⁇ clos ⁇ st tim ⁇ , T, prior to th ⁇ lat ⁇ st tim ⁇ stamp, wherein a previous d ⁇ adr ⁇ ckoni ⁇ g output has its a tim ⁇ stamp at a time ⁇ qual to T.
- the frequency of such measur m ⁇ nts provided by the deadreckoning subsystem 1527 may be adaptively provid ⁇ d depending on the velocity of the MBS 148 and/or the elapsed time since the most recent MBS locatbn update. Accordingly, the archite ⁇ ure of at least some embodim ⁇ nts of th ⁇ MBS bcation subsystem 1508 must be such that it can utilize such deadreckoning information for estimating the lo ⁇ tion of th ⁇ MBS 148.
- the outputs from the deadreckoning MBS location estimator 1544 are used to synchronize MBS lo ⁇ tion estimat ⁇ s from different MBS bas ⁇ line bcation estimators. That is, since such a deadreckoning output may be requested for substantially any time from the deadreckoning MBS lo ⁇ tion estimator, such an output can be requested for substantially the same point in time as the occurrence of the signals from which a new MBS baseline bcation estimat ⁇ is derived. Accordingly, such a deadreckoning output can be used to update oth ⁇ r MBS location estimates not using the new MBS baseline bcation estimate.
- the deadreckoning MBS location estimator is periodically reset so that the error accumulation in its outputs can be decreased.
- wh ⁇ n th ⁇ re is a high probability that the location of the MBS is known.
- the deadreckoning MBS location estimator may be res ⁇ t when an MBS operator manually enters an MBS location or verifies an MBS bcation, or a computed MBS bcation has sufficiently high confidence.
- a first ⁇ mbodim ⁇ nt of th ⁇ MBS location subsystem archite ⁇ ure is somewhat different from the location engine 139 archite ⁇ ure. That is, the archite ⁇ ure of this first embodiment is simpler than that of the archite ⁇ ure of the lo ⁇ tion ⁇ ngin ⁇ 139.
- the archit ⁇ ure of th ⁇ lo ⁇ tion ⁇ gin ⁇ 139 may also b ⁇ appli ⁇ d for providing a s ⁇ cond ⁇ mbodim ⁇ nt of th ⁇ MBS bcation subsystem 1508, as on ⁇ skill ⁇ d in th ⁇ art will appr ciat ⁇ after reflecting on the archite ⁇ ures and proc ⁇ ssing provid ⁇ d at an MBS 1 8.
- an MBS bcation subsystem 1508 archite ⁇ ure may be provided that has one or more first order models 1224 whose output is supplied to, for example, a blackboard or expert system for resolving MBS location estimat ⁇ confli ⁇ s, such an archite ⁇ ure being analogous to one embodiment of the location engin ⁇ 139 architecture.
- a blackboard or expert system for resolving MBS location estimat ⁇ confli ⁇ s such an archite ⁇ ure being analogous to one embodiment of the location engin ⁇ 139 architecture.
- ⁇ ach of th ⁇ first ord ⁇ r mod ⁇ ls 1224 may provid ⁇ its MS bcation hypoth ⁇ sis outputs to a corresponding "location track," analogous to th ⁇ MBS location tracks described hereinbelow, and subsequently, a most likely MS current bcation estimat ⁇ may b ⁇ developed in a "current location track” (also describ ⁇ d hereinbelow) using the most recent location estimates in other location tracks.
- th ⁇ id ⁇ as and m ⁇ thods discuss ⁇ d here relating to MBS location ⁇ stimators 1540 and MBS location tracks, and, th ⁇ related programs hereinbelow are sufficiently general so that thes ⁇ id ⁇ as and m ⁇ thods may b ⁇ appli ⁇ d in a numb ⁇ r of contexts related to d ⁇ termining the location of a devic ⁇ capabl ⁇ of mov ⁇ m ⁇ nt and wherein the location of the device must be maintained in real time.
- the present ideas and methods may be used by a robot in a very cluttered environment (e.g., a warehouse), wherein the robot has access: (a) to a plurality of "robot bcation estimators" that may provide the robot with sporadic location information, and (b) to a deadreckoning bcation estimator.
- Each MBS 148 additionally, has a lo ⁇ tion display (denot ⁇ d the MBS operator visual user interface 1558 in Fig. I I) where area maps that may be displayed together with location data.
- MS location data may be displayed on this display as a nested colle ⁇ ion of areas, each smaller ⁇ st ⁇ d ar ⁇ a b ⁇ ing the most likely area within (any) encompassing area for locating a targ ⁇ t MS 140.
- th ⁇ MBS controller algorithm below may b ⁇ adapted to receive location center 142 data for displaying the locations of other MBSs 148 as well as target MSs 140.
- the MBS 148 may constrain any bcation estimat ⁇ s to streets on a street map using the MBS bcation snap to street module 1562.
- an estimated MBS location not on a street may b ⁇ "snapped to" a nearest street location.
- a nearest stre ⁇ t location d ⁇ terminer may us ⁇ "normal" orientations of vehicles on streets as a constraint on the nearest street location.
- an MBS 148 is moving at typical rates of spe ⁇ d and acceleration, and without abrupt changes dire ⁇ ion.
- the deadreckoning MBS bcation estimator 1544 indicates that th ⁇ MBS 148 is moving in a northerly dire ⁇ ion, then the street snapped to should be a north-south running stre ⁇ t.
- the MBS bcation snap to street module 1562 may also be used to enhance target MS bcation ⁇ stimat ⁇ s wh ⁇ n, for ⁇ xampl ⁇ , it is known or susp ⁇ ed that the target MS 140 is in a vehicle and the vehicle is moving at typical rates of spe ⁇ d.
- the snap to stre ⁇ t bcation module 1562 may also b ⁇ used in enhancing the location of a target MS 140 by eith ⁇ r th ⁇ MBS 148 or by th ⁇ locatbn ⁇ ngin ⁇ 139.
- th ⁇ location ⁇ stimator 1344 or an additional modul ⁇ between th ⁇ location ⁇ stimator 1344 and th ⁇ output gateway 1356 may utilize an ⁇ mbodim ⁇ nt of th ⁇ snap to stre ⁇ t location module 1562 to enhance the accuracy of target MS 140 bcation estimat ⁇ s that are known to b ⁇ in v ⁇ hicl ⁇ s. Not ⁇ that this may be especially useful in locating stolen vehicl ⁇ s that have embedded wireless b ⁇ tion transceivers (MSs 140), wherein appropriate wireless signal measurements ⁇ be provided to th ⁇ location center 142.
- MSs 140 wireless b ⁇ tion transceivers
- th ⁇ discussion here refers substantially to the data stru ⁇ ures and their organization as illustrated in Fig. 13.
- the location estimates (or hypotheses) for an MBS 148 determining its own lo ⁇ tion each have an ⁇ rror or rang ⁇ ⁇ stimate associated with th ⁇ MBS location estimate. That is, each such MBS location estimat ⁇ includ ⁇ s a "most lik ⁇ ly MBS point locatbn" within a "most likely area”. The "most likely MBS point location” is assumed herein to be the centroid of the "most lik ⁇ ly area.” In one embodiment of the MBS location subsystem 1508, a n ⁇ st ⁇ d s ⁇ ri ⁇ s of "most lik ⁇ ly areas" may be provided about a most likely MBS point bcation.
- each MBS location estimate is assumed to have a single "most likely area”.
- One skilled in the art will understand how to provide such nested "most lik ⁇ ly ar ⁇ as” from th ⁇ d ⁇ scription h ⁇ r ⁇ in. Additionally, it is assum ⁇ d that such "most lik ⁇ ly ar ⁇ as" are not grossly oblong; i. ⁇ ., ar ⁇ a cross se ⁇ ioning lines through the centroid of the area do not have large differenc ⁇ s in th ⁇ ir lengths.
- no two such cross se ⁇ ioning lines of A may have lengths that vary by more than a factor of two.
- Each MBS location estimat ⁇ also has a confid ⁇ nc ⁇ associated therewith providing a measurement of the perc ⁇ iv ⁇ d accuracy of th ⁇ MBS being in the "most likely area" of the location estimate.
- a (MBS) "location track” is an data stru ⁇ ure (or obj ⁇ ct) having a qu ⁇ u ⁇ of a pr ⁇ determin ⁇ d l ⁇ ngth for maintaining a temporal
- each such MBS locatbn track entry is an estimat ⁇ of the bcation of the MBS at a particular corresponding time.
- MBS lo ⁇ tion track for storing MBS location entries obtained from MBS bcation estimation information from each of th ⁇ MBS bas ⁇ lin ⁇ lo ⁇ tion ⁇ stimators d ⁇ scrib ⁇ d abov ⁇ (i. ⁇ ., a GPS bcation track 1750 for storing MBS bcation ⁇ stimatio ⁇ s obtain ⁇ d from th ⁇ GPS location ⁇ stimator 1540, a locatbn c ⁇ nt ⁇ r location track 1754 for storing MBS bcation ⁇ stimatio ⁇ s obtained from the location estimator 1540 deriving its MBS bcation estimat ⁇ s from th ⁇ lo ⁇ tion c ⁇ nter 142, an LBS bcation track 1758 for storing MBS location estimations obtained from th ⁇ bcation ⁇ stimator 1540 d ⁇ riving its MBS bcation ⁇ stimat ⁇ s from bas ⁇ stations 122 and/or 152, and a manual locatbn track 1762 for M
- th ⁇ r ⁇ is one further bcation track, denot ⁇ d th ⁇ "current location track" 1766 whose bcation track entries may be deriv ⁇ d from th ⁇ entries in the other location tracks (describ ⁇ d further hereinbelow).
- a lo ⁇ tion track head that is the head of the queu ⁇ for th ⁇ location track. The location track head is the most rec ⁇ nt (and presumably the most accurate) MBS location estimate residing in the lo ⁇ tion track.
- the GPS bcation track 1750 has bcation track head 1770; the bcation center lo ⁇ tion track 1754 has location track head 1774; the LBS bcation track 1758 has lo ⁇ tio ⁇ track head 1778; the manual locatbn track 1762 has location track head 1782; and the current location track 1766 has location track head 1786.
- the time seri ⁇ s of previous MBS bcation ⁇ sti ations (i. ⁇ ., bcation track ⁇ tri ⁇ s) in the location track will herein be denoted the "path for the bcation track.”
- Such paths are typically th ⁇ l ⁇ ngth of th ⁇ location track queue containing the path. Note that the length of each such queu ⁇ may b ⁇ d ⁇ t ⁇ rmined using at least th ⁇ folbwing considerations:
- the location track entri ⁇ s are r mov ⁇ d from th ⁇ h ⁇ ad of the location track queu ⁇ s so that bcation adjustments may be made, in such a case, it may be advantageous for the length of such queu ⁇ s to be greater than the number of entri ⁇ s that are expe ⁇ d to b ⁇ r ⁇ mov ⁇ d;
- ⁇ ach locatbn track entry includes: (a) a "derived location estimat ⁇ " for th ⁇ MBS that is d ⁇ riv ⁇ d using at l ⁇ ast on ⁇ of:
- each output from an MBS bcation ⁇ stimator has a "type" field that is us ⁇ d for identifying th ⁇ MBS location estimator of the output.
- earliest timestamp latest timestamp only for so called “baseline entries” as defined hereinbelow. Further note that this attribute is the one used for maintaining the "temporal (timestamp) ordering" of location track entries.
- a "d ⁇ adreckoning distance” indicating the total distance ( ⁇ .g., wheel turns or odometer diff ⁇ r ⁇ nc ⁇ ) sinc ⁇ th ⁇ most recently prevbus baselin ⁇ ⁇ ntry for th ⁇ corresponding MBS location estimator for the location track to which the bcation track entry is assigned.
- MBS location track there are two categories of MBS location track entri ⁇ s that may be inserted into a MBS bcation track:
- each such baselin ⁇ ⁇ ntry includ ⁇ s (dep ⁇ ding on th ⁇ locatbn track) a bcation ⁇ stimat ⁇ for th ⁇ MBS 148 derived from: (i) a most recent previous output either from a corresponding MBS baseline bcation estimator, or (ii) from the baseii ⁇ entries of other location tracks (this latter case being th ⁇ for the "current" lo ⁇ tion track); (b) "extrapolation” entries, wherein each such entry includes an MBS location estimat ⁇ that has b ⁇ en extrapolated from the (most rec ⁇ t) bcation track head for the location track (i.e., based on the track head whose "latest tim ⁇ stamp” imm ⁇ diat ⁇ ly precedes the latest timestamp of the extrapolation entry).
- Each such extrapolation entry is computed by using data from a related deadreckoning bcation change estimat ⁇ output from the deadreckoning MBS bcation estimator 1544.
- Each such deadreckoning bcation change estimat ⁇ includ ⁇ s m ⁇ asur ⁇ m ⁇ nts related to changes or deltas in the bcation of the MBS 148.
- each extrapolation entry is det ⁇ rmin ⁇ d using: (i) a bas ⁇ lin ⁇ entry, and (ii) a s ⁇ t of on ⁇ or more (i.e., all later occurring) d ⁇ ad reckoning location cha ⁇ g ⁇ ⁇ stimat ⁇ s in increasing "latest tim ⁇ stamp" ord ⁇ r.
- this set of one or more deadreckoning bcation change estimates will be denot ⁇ d the "deadreckoning location change estimat ⁇ s ⁇ t" associat ⁇ d with the extrapolation entry resulting from this set.
- th ⁇ track h ⁇ ads of all location tracks include MBS location estimates that are for substantially the sam ⁇ (latest) tim ⁇ stamp. How ⁇ v ⁇ r, the MBS locatbn information from ⁇ ach MBS baseline bcation estimator is inherently substantially unpredi ⁇ able and u ⁇ synchronized.
- Consequ ⁇ ntly (referring to Fig.
- synchronization records 1790 may b ⁇ provid ⁇ d for updating ⁇ ach bcation track with a n ⁇ w MBS bcation estimate as a new track head.
- each synchronization record includes a deadreckoning bcation change estimat ⁇ to b ⁇ us ⁇ d in updating all but at most one of the location track heads with a new MBS locatbn estimate by using a d ⁇ adr ⁇ ckoning bcation chang ⁇ estimate in conjunction with each MBS location estimate from an MBS baseline location estimator, the bcation track heads may be synchronized according to timestamp.
- the present invention also substantially simultaneously queri ⁇ s the deadreckoning MBS location estimator for a corresponding most recent change in the bcation of the MBS 148.
- E and the retriev ⁇ d MBS deadreckoning location change estimate, C have substantially the same "latest timestamp".
- the bcation estimate E may be used to create a new baseline track head for the bcation track having the corresponding type for E, and C may be used to create a corresponding extrapolation entry as the head of each of the other location tracks.
- MBS-MS MBS on-board transceiver
- MS ordinary bcation device
- any bcation signal information between the MBS and the present target MS may be transmitted to the Location Center so that this information may also be used by the Location Center to provide better estimates of where the HBS is. furth ⁇ r, if th ⁇ MBS d ⁇ ter ines that it is immediately adjacent to th ⁇ target MS and also that its own location estimate is highly reliable (e.g., a GPS estimate), then the MBS may also communicate this information to the Location Cent ⁇ r so that th ⁇ Location C ⁇ nt ⁇ r can: (a) associate any target MS location signature cluster data with the fixed base statbn infrastru ⁇ ure with the location provided by the MBS, and (b) insert this associated data into the bcation signature data base of the
- this transmission pr ⁇ f ⁇ r ably continu ⁇ s (i. ⁇ ., repeats) for at least a predetermined length of time of sufficient length for the Signal Processing Subsystem to collect a sufficient signal chara ⁇ eristic sample size.
- MS_raw_signal_data is an obje ⁇ having substantially the unfilter ⁇ d signal characteristic values for communications between the MBS and the target MS as well as timestamp information. */ Construct a messag ⁇ for s ⁇ nding to th ⁇ Location C ⁇ nter, wherein the messag ⁇ includ ⁇ s at least
- MS_raw_signal_data and "MBS_curr_est” so that th ⁇ Location C ⁇ nt ⁇ r can also compute an estimated bcation for the target MS;
- th ⁇ MS signal data obtain ⁇ d above is, in one embodiment, "raw" signal data. Howev ⁇ r, in a s ⁇ cond ⁇ mbodim ⁇ nt, this data is filtered substantiaiiy as in th ⁇ Locatbn C ⁇ nter by the Signal Proc ⁇ ssing Subsystem. For simplicity of discussion here, it is assumed that each MBS includes at least a s ⁇ led down v ⁇ rsion of th ⁇ Signal Proc ⁇ ssing Subsyst ⁇ m (see FIG. 11). */ MS_new_est ⁇ »-- DETERMINE_MS_MOST_RECENT_ESTIMATE ⁇ ⁇ curr ⁇ st, MS curr ⁇ st,
- MS signaNata 5 /* May us ⁇ forward and reverse TOA, TDOA, signal power, signal strength, and signal quality indicators.
- MS_curr_est includes a timestamp of when the target MS signals w ⁇ re rec ⁇ ' rved.
- this MS locatbn estimat ⁇ is "temporary" in th ⁇ s ⁇ ns ⁇ that it will be replaced by a corresponding MS location estimate received from the Locatbn Cent ⁇ r that is based on the sam ⁇ targ ⁇ t MS raw signal data. That is, if the Location Cent ⁇ r responds with a corresponding target MS bcation estimate, E, while "MS_new_est” is a valu ⁇ in a "moving window" of targ ⁇ t MS 15 lo ⁇ tion ⁇ stimat ⁇ s (as d ⁇ scrib ⁇ d hereinbelow), then E will replace the value of "MS_new_ ⁇ st".
- th ⁇ moving window may dynamically vary in size according to, for exampl ⁇ , a perceived velocity of the target MS and/or the MBS. */ MS_moving_window ⁇ — get_MS_moving_ windo wwt);
- any giv ⁇ n singl ⁇ colle ⁇ ion of measurem ⁇ nts related to locating the target MS may be potentially misleading, a "moving window" of bcation ⁇ stimates are used to form a "composite bcation estimate" of the target MS.
- This composite location estimate is based on some number of the 25 most recent bcation estimat ⁇ s determin ⁇ d.
- Such a composite bcation ⁇ sti at ⁇ may b ⁇ , for ⁇ xample, analogous to a moving average or some other w ⁇ ighting of target MS location estimat ⁇ s.
- ⁇ ach bcation estimate i.e., at least on ⁇ MS bcation ar ⁇ a, a most lik ⁇ ly singl ⁇ location, and, a confidence estimat ⁇
- a centroid type calculation may be performed to provide the composite ta ⁇ tion estimate.
- the MBS display may use various colors to represent nested location areas overlay ⁇ d on an ar ⁇ a map wherein, for ⁇ xampl ⁇ , 3 n ⁇ st ⁇ d ar ⁇ as may be displayed on the map overlay: (a) a largest area having a relatively high probability that th ⁇ targ ⁇ t MS is in th ⁇ area ( ⁇ .g., > 95%); (b) a smaller nest ⁇ d area having a bw ⁇ r probability that th ⁇ targ ⁇ t MS is in this ar ⁇ a (e.g., > 80%); and (c) a smallest area having the lowest probability that the target MS is in this area (e.g., >70%).
- Furth ⁇ r a relativ ⁇ ly precise specific bcation is provided in the smallest area as the most lik ⁇ ly singl ⁇ location of the target MS.
- the colors for each region may dynamically change to provid ⁇ an indication as to how high th ⁇ ir reliability is; ⁇ .g., no colored areas shown for reliabilities below, say, 40%; 40-50% is purple; 50-60% is blue; 60-70% is green; 70-80% is amber; 80-90% is white; and red denotes the most likely single lo ⁇ tion of the target MS.
- the thre ⁇ n ⁇ st ⁇ d areas may collapse into one or two as the MBS gets closer to the target MS. Moreov ⁇ r, not ⁇ that th ⁇ collapsing of th ⁇ s ⁇ diff ⁇ rent areas may provide operators in the MBS with additional visual reassurance that the lo ⁇ tion of the target MS is being determined with better accuracy.
- target MS location data may be rec ⁇ ived from the Lo ⁇ tion Cent ⁇ r in th ⁇ seek state, conta ⁇ state and the control state. Such data may b ⁇ received in response to the HBS sending target MS bcation signal data to the Lo ⁇ tion Center (as may be the case in the conta ⁇ and control states), or such data may be rec ⁇ iv ⁇ d from th ⁇
- This information includes error or reliability estimat ⁇ s that may b ⁇ us ⁇ d in subs ⁇ qu ⁇ nt attempts to det ⁇ rmin ⁇ an MBS location ⁇ stimate when there is no communication with the LC and no exa ⁇ (GPS) lo ⁇ tion can be obtained. That is, if the reliability of the target HS's bcation is d ⁇ m ⁇ d highly reliable, th ⁇ n subs ⁇ qu ⁇ nt l ⁇ ss reliable locatbn estimates should be used only to the degree that more highly reliable estimates becom ⁇ l ⁇ ss r ⁇ l ⁇ vant du ⁇ to the MBS moving to other locations.
- CONFIGURE MBS to respond to any signals received from the new targ ⁇ t MS by requesting location data from the new target MS; INITIALIZE timer for communication from LC; /* A timer may be set per target MS on list. */
- MBS could be moving or stationary. If stationary, then the estimat ⁇ for the bcation of the MBS is given high reliability and a small range (e.g., 20 fe ⁇ t). If th ⁇ MBS is moving, then the estimat ⁇ for the location of the MBS is given high reliability but a wider range that may be dep ⁇ nd ⁇ t on the speed of the MBS.
- the estimat ⁇ may b ⁇ obtain ⁇ d, for ⁇ xampl ⁇ , using a light p ⁇ on a displayed map */ if (operator supplies a confidence indication for the input MBS lo ⁇ tion estimat ⁇ ) th ⁇ n
- each MBS bcation estimate includes: (a) a most likely area estimate surrounding a central bcation and (b) a confidenc ⁇ valu ⁇ of the MBS being in the bcation estimate.
- the confidence value for each MBS bcation estimate is a measurem ⁇ nt of th ⁇ lik ⁇ lihood of th ⁇ MBS bcation ⁇ stimate b ⁇ ing corre ⁇ . More precisely, a confidence value for a new MBS bcation estimate is a measurement that is adjusted according to the following criteria:
- the confidence value is an MBS location likelihood measurement which takes into account the history of previous MBS location estimates.
- MBS new est A new NBS baseline location estimate to use in determining the location of the NBS, but not a (deadreckoning) location change estimate deadreck est The deadreckoning location change estimate paired with "MBS_new_est”. */
- ⁇ els ⁇ /* th ⁇ r ⁇ is at least one non-empty location track in addition to the current locatbn track being nonempty*/
- MBS_curr_est add_location_entry(MBS_new_ ⁇ st,d ⁇ adreck_ ⁇ st);
- MBS_curr_est ⁇ add_location_entry(MBS_n ⁇ w_ ⁇ st, dcadreck ⁇ st);
- NBS_new_est.type location track is non-empty and "MBS_new_est” is not of type MANUAL_ENTRY */
- MBS curr est ⁇ — add_location_entry(MBS new est, deadreck est); ⁇ /* end "MBS new est” not filtered out */ else /* "MBS_new_ ⁇ st” is filtered out; do nothing */;
- MBS_new_est is NULL; thus only a d ⁇ adreckoning output is to b ⁇ add ⁇ d to location tracks */ ⁇ ⁇ xtrapolation_entry ⁇ --- create_a/ ⁇ _extrapo/atio ⁇ _e ⁇ ty_iro/ ⁇ (di ⁇ rt ⁇ _tity, insen nto_every ocatio ⁇ _tmcl ⁇ xt ⁇ ⁇ ]tt ⁇ )-, /* including the "current location track" */ MBS curr est ⁇ — et_ c ⁇ rr_ f ⁇ (MBS_new_est.MS_ID); /* from current location track */ ⁇ RETURN(MBS_curr_est);
- This fun ⁇ ion adds the bas ⁇ lin ⁇ entry, "MBS_n ⁇ w_est” and its paired d ⁇ adreckoning lo ⁇ tbn chang ⁇ estimate, "deadreck est” to the lo ⁇ tion tracks, including the "current location track”. Note, however, that this fun ⁇ ion will roll back and rearrange lo ⁇ tion entries, if necessary, so that the entries are in latest tim ⁇ stamp ord ⁇ r.
- MBS curr est */ ⁇ if (there is a time series of one or more dead reckoning extrapolation entries in the locatbn track of type "MBS_new_esttype" wherein the extrapolation entries have a "latest tim ⁇ stamp" more recent than the timestamp of "MBS_new_est") then ⁇ /* Note, this condition may occur in a number of ways; e.g., (a) an MBS location ⁇ stimat ⁇ rec ⁇ iv ⁇ d from th ⁇ Location C ⁇ nter could be delay ⁇ d long ⁇ nough (e.g., 1-4 sec) because of transmission and processing time; (b) the estimation records output from the MBS baseline bcation estimators are not guaranteed to b ⁇ always pres ⁇ nted to the bcation tracks in the temporal order they are created.
- deadreck est includes the values for the change in the MBS location substantially for the time period betw ⁇ n th ⁇ tim ⁇ stamp, T, of "MS_n ⁇ w_est” and th ⁇ tim ⁇ stamp of th ⁇ closest deadreckoning output just before T. Further note that if there are any extrapolation entries that were rolled back above, then there / ⁇ an extrapolation entry, E, previously in the location tracks and wherein E has an earliest timestamp equal to th ⁇ latest timestamp of B abov ⁇ .
- E.delta the HBS location change vector of E (denoted herein as E.delta) becomes E.delta - [location change ve ⁇ or of "d ⁇ adr ⁇ ck st"].
- E.delta location change vector of E.delta - [location change ve ⁇ or of "d ⁇ adr ⁇ ck st"].
- I* "stackjop” is either a bas ⁇ lin ⁇ location entry and a paired deadreckoning location change estimat ⁇ , or, an unpaired d ⁇ adreckoning bcation change estimate associated with a NULL for the baseline location entry */ deadreck ⁇ st MBS curr est ⁇ - DETERMINE J1BS_L0CATI0N_ESTIMATE(MBS_nxt_ ⁇ st, d ⁇ adr ⁇ ck_est);
Abstract
Description
Claims
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US09/194,367 US7764231B1 (en) | 1996-09-09 | 1997-09-08 | Wireless location using multiple mobile station location techniques |
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US10/262,413 US7298327B2 (en) | 1996-09-09 | 2002-09-30 | Geographic location using multiple location estimators |
US11/069,441 US7812766B2 (en) | 1996-09-09 | 2005-03-01 | Locating a mobile station and applications therefor |
US11/464,880 US7903029B2 (en) | 1996-09-09 | 2006-08-16 | Wireless location routing applications and architecture therefor |
US11/739,097 US9237543B2 (en) | 1996-09-09 | 2007-04-24 | Wireless location using signal fingerprinting and other location estimators |
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US12/786,429 US9060341B2 (en) | 1996-09-09 | 2010-05-24 | System and method for hybriding wireless location techniques |
US12/861,817 US8994591B2 (en) | 1996-09-09 | 2010-08-23 | Locating a mobile station and applications therefor |
US13/323,221 US9134398B2 (en) | 1996-09-09 | 2011-12-12 | Wireless location using network centric location estimators |
US13/831,674 US9277525B2 (en) | 1996-09-09 | 2013-03-15 | Wireless location using location estimators |
US14/854,025 US20160139242A1 (en) | 1996-09-09 | 2015-09-14 | Mobile unit location using mobile units in proximity |
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US20160309298A1 (en) | 2016-10-20 |
US20100234045A1 (en) | 2010-09-16 |
US9277525B2 (en) | 2016-03-01 |
US8032153B2 (en) | 2011-10-04 |
GB2337386A (en) | 1999-11-17 |
US7525484B2 (en) | 2009-04-28 |
US20080113672A1 (en) | 2008-05-15 |
GB2337386B (en) | 2001-04-04 |
GB9905311D0 (en) | 1999-04-28 |
CA2265875A1 (en) | 1998-03-12 |
US20010022558A1 (en) | 2001-09-20 |
US20080167049A1 (en) | 2008-07-10 |
US20130281115A1 (en) | 2013-10-24 |
US9237543B2 (en) | 2016-01-12 |
WO1998010307A8 (en) | 1999-04-08 |
US7298327B2 (en) | 2007-11-20 |
US9060341B2 (en) | 2015-06-16 |
US20030222820A1 (en) | 2003-12-04 |
CA2265875C (en) | 2007-01-16 |
US7764231B1 (en) | 2010-07-27 |
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