CA2138184C - Phase noise measurement system and method - Google Patents
Phase noise measurement system and methodInfo
- Publication number
- CA2138184C CA2138184C CA002138184A CA2138184A CA2138184C CA 2138184 C CA2138184 C CA 2138184C CA 002138184 A CA002138184 A CA 002138184A CA 2138184 A CA2138184 A CA 2138184A CA 2138184 C CA2138184 C CA 2138184C
- Authority
- CA
- Canada
- Prior art keywords
- signals
- signal
- power spectral
- phase noise
- difference signals
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R29/00—Arrangements for measuring or indicating electric quantities not covered by groups G01R19/00 - G01R27/00
- G01R29/26—Measuring noise figure; Measuring signal-to-noise ratio
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Measuring Phase Differences (AREA)
- Measurement Of Resistance Or Impedance (AREA)
- Radar Systems Or Details Thereof (AREA)
- Testing Relating To Insulation (AREA)
Abstract
Three independent signal sources (14, 15, 20) are used to statistically derive the power spectral density of the phase noise content of signals from each of them. This is accomplished by mixing (30) each of the signals two at a time (i.e., signal one with signal two, signal one with signal three, and signal two with signal three) and capturing the resultant difference signals, such as with a waveform recorder (11, 12, 13), for example. A servo electronics loop (25, 26) is used to remove the carrier and any long term signal drift from the resultant difference signals. Statistical analysis is then used to compute the composite power spectral densities of the the resultant difference signals, and to solve for the individual power spectral densities of the original signals. The present system (10) and method (40) uses the mathematical relationships between the three sources that have similar magnitudes of phase noise, to compute the power spectral density of the noise content of signals from each source (14, 15, 20). The present system (10) and method (40) requires a minimum of interconnect hardware and only three inexpensive waveform recorders (11, 12, 13), for example. Furthermore, the size, weight, and cost of producing the present phase noise test system (10) is relatively low.
Description
PHASE l~'OISlE ~IEASURE~IENT SYSTE~I A~D METHOD
BACKGROUI~D
The present invention relates generally to phase noise measurement systems, and more particularly, to a phase noise measurement system that uses three independent signal sources to statistically derive the power spectral density of the phase noise content of each of them.
Typical phase noise measurement test sets available in the commercial market fall into two primary categories: a two oscillator systems (or phase detector system) and a delay line discrirninator system. To clarify these systems, each is discussed separate-ly. The two oscillator system uses two sources (a unit under test and a reference source) that are set to the same frequency and that are in quadrature with respect to each other. These signals are input to a double balanced mixer that is used as a phase detec-tor, and the resultant output is passed through a low pass filter. The remaining signal is a low voltage DC signal that is summed with an AC signal. The AC fluctuations are proportional to the combined phase noise of the original two signal sources. This AC
signal is then fed into a spectrum analyzer and the power spectrum is displayed to a user. The primary limiting factor of this approach is the requirement that the reference source have phase noise characteristics that are at least 10 dB better than the source that is measured. In the case of phase noise measurements for radar signal sources, these sources have very low phase noise, and finding a reference that is superior can be very difficult or impossi~le. Therefore, this method is primarily used for measuring signal sources of higher noise content or for measuring phase noise close to the carrier.
The delay line discriminator system does not require additional reference sources. It uses the signal from the unit under test and splits it into two sigrials. The signal in one pats is input into a delay line whose output feeds a phase detector. The other signal is fed directly to the phase detector. Phase detecting the delayed and non-delayed signals together creates a discrimination effect which produces a frequency modulated signal 5 proportional to the signal's inherent noise content. This FM noise signal is then integrated and measured by a baseband spectrum analyzer. This system has several limitations. Its sensitivity is proportional to the delay time and the larger the delay time the greater the insertion loss. This acts as a practical limitation for the sensitivity of the system. Furthermore, the 0 sensitivity degrades as 1/f2 as the carrier is approached. Therefore, this technique is not useful measuring very stable sources close to the carrier frequency.
Accordingly, it is an objective of the present invention to provide for an improved phase noise measurement system that overcomes the 5 limitations of conventional systems.
SUMMARY OF THE INVENTION
The present system and method is used to determine the phase noise of a primary signal source. The present system uses three 2 o independent signal sources to statistically derive the power spectral density of the phase noise content of signals from each of them. This is accomplished by mixing each of the signals two at a time (i.e., signal one with signal two, signal one with signal three, and signal two with signal three) and measuring the resultant difference signals, such as with a 2 5 waveform recorder. A servo electronics subsystem is used to remove the residual low frequency products and any long term signal drift from the difference signals, and the difference signals are then captured via three channels of waveform recorders. Statistical analysis routines are then used to compute the composite power spectral densities of the difference 3 o signals, and to ultimately solve for the individual power spectral densities of the original signals. Thus, the phase noise of the primary signal source is determined.
Various aspects of the invention are as follows:
A phase noise measurement system characterized by:
a primary signal source for providing a first signal whose phase noise is to be measured;
second and third signal sources for providing second and third signals that each have substantially the same frequency and a similar noise content as said first signal provided by said primary signal source;
mixing means for mixing each of said first, second, and third signals two at a time to generate three respective difference signals;
servo means for processing said three difference signals to remove carrier signals and long term signal drift therefrom;
o waveform recorder means coupled to said mixing means for capturing said respective three difference signals;
processing means coupled to said waveform recorder means for statistical analyzing said three difference signals to compute composite power spectral densities therefor, and for computing respective individual power spectral densities of said first, second and third signals from the composite power spectral densities, and hence determining the phase noise of said primary signal source.
A method of determining the phase noise of signals provided by a primary signal source characterized by the steps of:
2 o providing a first signal from a primary signal source whose phase noise is to be measured;
providing second and third signals from second and third signal sources that each have substantially the same frequency and a similar noise content as said first signal provided by said primary signal source;
2 5 mixing each of said first, second, and third signals two at a time to generate three respective difference signals;
processing said three difference signals to remove residual carrier and drift theref~o~l;
statistically analyzing said three difference signals to compute 3 o composite power spectral densities therefor; and computing respective individual power spectral densities of said first, second and third signals from the composite power spectral densities to determine the phase noise of said primary signal source.
The present system and method thus uses mathematical 3 5 relationships between three signal sources, with similar magnitudes of 3a phase noise, to compute the power spectral density of the noise content of each source. Where traditional techniques typically require ultrastable r~f~ ce sources or expensive calibrated delay lines, the present system requires a minimum of interconnect hardware and only three inexpensive waveform recorder channels, for example. Furthermore, the size, weight, and cost of producing the present phase noise test system is substantially lower than present commercially available units.
Traditional phase noise measurement techniques typically address two problem areas: phase noise close to the carrier and phase noise far o from the carrier. Furthermore, because these problems are diverse, it takes one commercial test set to address each area. However, the present technique addresses the entire problem with one system configuration.
This has not been done by any commercial system vendor, and is therefore hel~eLof~le unavailable.
Future factory and field testers are expected to be down-sized for greater portability and lower cost. The present system relies on virtual instrument concepts to achieve this goal. Virtual instruments are presently unavailable in the commercial market place. The present system is adapted to replace existing phase noise measurement systems 2 o currently used for near-in and far-from the carrier phase measurements.
There is also a need for a system to test devices such as crystal oscillators, synthesizers, atomic clocks and standards, and low noise and ultra-low noise sources. The present system makes these tests more affordable while requiring less space.
BRIEF DESCRIPTION OF THE DRAWINGS
The various features and advantages of the present invention rnay be rnore readily understood with reference to the following detailed description taken inconjunction with the accompanying drawings, wherein like reference numerals designate like structural elements, and in which:
Fig. 1 is a system block diagram of a phase noise measurement system in accordance with the principles of the present invention; and Fig. 2 shows a software flow diagram employed in the phase noise measurement system of Fi~. 1.
DETAILED DESCRIPTIO~
Referring to the drawing figures, Fig. 1 is a system block diagram of a phase noise measurement system in accordance with the principles of the present invention.
the phase noise measurement system is comprised of two low noise reference sources 14, 15 that are both programmable and phase lockable. These reference sources 14, 15 are used in conjunction with a unit under test (WT) 20 that comprises a third source.
The reference sources 14, 15 are phase locked to the unit under test 20 by means of first and second phase locked loops 25, 26. The phase locked loops 25, 26 are adapted to minirnize cancellation of phase noise that is close to the carrier.
Outputs from the three sources (the two reference sources 14, IS and the WT
20) are applied to three splitters 16, 17, 18. Theoutputs from the splitters 16, 17, 18 are fed both to three mixers 27, 28, 29 and to three programmable phase shifters 24, 25, 26. This produces three sets of signals at the mixers 27, 28, 29 that are inquadrature. However, if the outputs of the mixers 27, 28, 29 produce a resultant signal with a residual carrier, servo electronics units 21, 22, 23 sweep the phase shifters 24, 25, 26 to produce a signal with a 0 Hz carrier (null carrier). The outputs of the three mixers 27, 28, 29 are fed into their respective low pass filters 31, 32, 33. These filters 31, 32, 33 reject high frequency rnixer products and also limit the noise bandwidth, in that they act as an anti-aliasing filter. The outputs of the filters 31, 32, 33 are applied to three waveforrn recorders 11, 12, 13. The waveform recorders 11, 12, 13 digitize the noise signals and couple them to a controVprocess computer 23 for analysis.
Fig.2 is a block diagram of software 40 employed in the controVprocess computer 23 of the phase noise measurement system 10. The software 40 is comprised 213818q s of eight functional software routines thal process the measured data and produce the desired spectral results. The first major section of the software 40 comprises a data acquisition module 41. The data acquisition module 41 provides for hardware control of the components of the system 10 and is comprised of two instrument handlers, a waveform recorder handler 42 for the waveform recorders 11, 12, 13 and a low noise source handler 43 for the low noise sources 14, lS, 16. The functions of these handlers 42, 43 include setup of the waveform recorders 11, 12, 13 and low noisesources 14, 15, 16, and programming of the phase shifters 24, 25, 26.
Once the data is digitized and acquired by the data acquisition module 41, control is passed to a phase analysis engine module 44. In the phase analysis engine module 44, the mixed phase noise data is analyzed using digital signal processing techniques, including an autocorrelation generator routine 45, averaging routine 46, noise extraction routine 48, and a fast Fourier transform routine 47. The autocorrela-tion generator routine 45 generates autocorrelation functions for the three mL~ed and lS downconverted noise signals (R13, R23, R12). To minimize the random variance and quantization noise introduced into the calculations, the autocorrelation functions for the combined noise signals (R13, R23, R12) are averaged using the averaging routine 46.
The averaging function involves acquiring multiple signal sets and computing a series of autocorrelation functions for the combined noise (R 13, R23, R 12). Then each auto-correlation function (i.e., R13) is averaged over the series. Following the averaging calculations for the combined noise, the autocorrelation functions (R13, R23, R12) are converted to power spectral density functions (P13, P23, P13) using the fast Fourier transforrnation routine 47. The conversion from autocorrelation functions to power spectral density functions is based on the Wiener-Khinchin theorem. At this point, the individual noise power spectral densities (P1, P2, P3) are then computed using the noise extraction routine 48. The power spectral densities (P1, P2, P3) are then formatted and passed to a human interface module 50. The human interface module 50 handles key~oard interactions, displaying functions, cornmand interpretations, plotting functions and calculations and the overall process flow of the software 40. This is accomplished using a process control/command interpreter routine S1 and a plotting routine 52.
The phase noise measurement system 10 is adapted to measure phase noise in active devices comprising the unit under test 20 by using sophisticated digital process-ing techniques. The novelty of this system 10 lies in the way the noise signal is han-dled and analyzed and the use of autocorrelation functions and spectral power density functions in the final analysis routines 45-48 of the phase analysis engine module 44.
The value of this technique lies in the fundamental trade-off between hardware and software, i.e. if hardware may be eliminated by using software analysis, it lowers the cost of the measurement system 10. Furtherrnore, reducing the hardware results in a lighter, smaller, and more reliable product. The present system 10 provides a means for addressing factory and bench testing applications and field testing applications requiring reduced weight, size, and cost.
The present measurement system 10 trades hardware complexity for software sophistication. It uses the mathematical relationships between discrete time measure-ments, autocorrelation functions, power spectral density conversions, and error mini-rnization theory to extract the noise content from a signal derived from the unit under test 20. To help clarify the principles and theory behind the present invention, a general mathematical description is presented below.
To measure the noise content of the signal from the unit under test 20 using thepresent system 10 requires the two additional reference sources 14, 15 having substan-tially the same frequency and similar noise content. That is, all three sources 14, 15, 20 have the same carrier frequency and have noise spectra within 10 dB of each other.
Let ~l(t), ~2(t), and 03(t) represent the phase noise content of each of the three signal sources 14, 15, 20 and assume that all three sources 14, 15, 20 have carriers in the RF/microwave region. The three signals produced by the three signal sources 14, 15, 20 are given by:
Vl(t) = Sin (WCt ffpl(t)) V2(t) = Sin (~ct +q'2(t)) V3(t) = Sin (Wct +<p3(t)) The next step is to mix the three signals together, two at a time, to create three new composite baseband signals. Assume that the mixing is performed with the signals in quadrature and the results are passed through low pass filters 31, 32, 33 each having a gain of two. The three sets of signals produced by the three signal sources 14, 15, 20 are given by:
V13(t) = Vl(t) x V3(t) x 2 V12(t) = Vl(t) x V2(t) x 2 V23(t) = V2(t) x V3(t) x 2.
Then, after filtering:
V13(t) = Sin (~I(t) - ~3(t)) V 12(t) = Sin (~1 (t) - t>2(t)) V23(t)= Sin (~2(t) - ~3(t)), which correspond to the outputs of the three mixers 27, 28, 29. The composite noise signals (i.e.: ~I(t) - ~3(t)) represent very small angles (<<.1 radians). Therefore, the following simplification is made to produce the outputs of the filters 31, 32, 33:
~138184 V13(t) = [~I(t) - 03(t)]
Vl2(~) ~ [~I(t) - ~2(t)]
V13(t) 2 [~2(t) - Q3(t)]
Since three independent signal sources 14, 15, 20 are provided, it is also reasonable to assume that the noise content for each source 14, 15, 20 is uncorrelated and that the three baseband composite noise signals are uncorrelated. In addition, it is assumed that the three sources 14, 15, 20 produce ergotic random sequences. That is, the statistics of these random sequences may be determined from a single collection of observations. If this is true, time averaging may be substituted for ensemble averaging.
Therefore, the auto correlation functions for the three composite signals may becomputed at follows. The following equations are implemented in the autocorrelation generator routine 45 of Fig. 2.
Rl3(t) = LimT~ T V13~) x ~-13(~ + t) d~
~ ,3T
Rl2(t) = LimT~ 1 V12(~) x V12(~ + t) dl3 R23(t) = LimT T V23(~) x V23(~ + t) d~
~0 Expanding one of the three autocorrelation functions reveals the following:
Rl3(t) = LimT~ T [~~ 3(~)] [Ql(~+t) - Q3(~+t)J d~
~ O
~k~ T [~ +tD ~ [~ h3(~+t)] - [~ +~h3(~)~ + [~ (~h (~ )~
~o R13(t) = rl(t) - rl3(t) - r31(t) + r3(t), 20 where rl, r3 are autocorrelation functions and rl3, r31 are cross correlation functions.
However, the phase noise of the three signals are assumed to be independent.
Therefore, all cross correlation functions approach zero and the autocorrelation function simplifies to:
R13(t) = rl(t) + r3(t).
Extrapolating these results, the autocorrelation functions of the remaining composite signals are computed in a similar manner.
R12(t) = rl(t) + r2(t), and R23(t) = r2(t) + r3(t) The last three equations represent the autocorrelation functions computed by theautocorrelation generator routine 45.
Z13~1Q~
The averaging routine 46 sums a plurality of sets of autocorrelation signals anddivides the resultant sum by the number of summed sets to produce an average value for the respective autorrelation functions. This averages out fluctuations in the signals.
Based on the Wiener-Khinchin Theorem, the Fourier transform of the S autocorrelation function is representative of the power spectral density. The following equations are implemented in the Fourier transforrn routine 48 of Fig. 2.
F~R13(t)) = lsl3(f)} -~ < f S +
F(Rl2(t)) = 1S12(f)) ~~ < f < +
F(R23(t)) = 1S23(f)) ~~ < f < + ~
For this process, a user would be more interested in the power contained in a frequency interval from 0 to + ~ and would not want to distinguish between positive and negative values of frequency. Therefore, a one sided power spectral density function may be defined as follows:
P13(f) -- lS13(f)l + IS13(-f)l 0 < f <
Pl2(f) -- lS12(f)l + lSI2~ 0 < f ~ ~
P23(f) - 1s23(f)l + lS23(-f)l 0 < f < oo From above, Rl3(t) = rl(t) + r3(t) and the Fourier transform of R13(t) may be represented as follows:
F(Rl3(t)} = Flrl(t)} + F(r3(t)}, or IS13(f)1 = ISl(f)l + IS3(f)1 _OO < f S ~.
Converting the double sided power spectral density to a single sided representation yields:
Pl3(f) = Pl(f) + P3(f) 0 < f < ~.
Therefore, extrapolating these results for all three power spectral densities results in the following equations:
Pl3(f) = Pl(f) + P3(f) 0 S f S x, Pl2(f) = Pl(f) + P2(f) 0 S f S ~
P23(f) = P2(f) + P3(f) 0 S f s OO.
With these three equations, it is possible to solve for the individual power spectral densities of the three sources 14, 15, 20 which are represented by Pl(f), P2(f), and P3(f) as a function of the measured composite power spectral densities represented by Pl3(f), Pl2(f), and P23(f). This is implemented in the noise extraction routine 48.
Pl(f) = [Pl3(f) + Pl2(f) - P23(f)] /2 P2(f) = [Pl2(f) + P23(f) - Pl3(f)] /2 P3(f) = [pl3(f) + P23(f) - Pl2(f)] /2.
~138i~
The mathematical description presented above is for a continuous systems case.
However, to implement a real world instrument to perform this process requires discretization and complex digital signal processing. Therefore, it is important to validate the process for systems that are characterized by discrete data samples.
S Let ~l(n~t), ~2(n~t), and ~3(n~t) represent the discretely sampled phase noise content of the three discrete signal sources 14, 15, 20. These signal sources 14, 15, 20 (and their noise content) were originally continuous signals that were mixed down to 0 Hz baseband and then digitized. The rnixing process is the same as described above.
V13(n~t) = Sin [~I(n~t) - 03(n~t)] 0 S n S N-1 V12(n~t) = Sin [~I(n~t) - ~2(n~t)] 0 5 n S N-l V23(n~t) = Sin [~2(n~t) - ~3(n~t)] 0 5 n 5 N- 1 The composite noise signals (i.e.,: ~I(nat) - ~3(n~t)) represent very small angles (~<.1 radians). Therefore, the following simplification is made:
V13(n~t) = [Ol(n~t) - o3(n~t)] 0 5 n 5 N-l V12(n~t) = ~Ol(n~t) - ~(n~t)~ 0 S n S N- I
V23(n~t) = [~2(n~t) - Q3(n~t)] 0 5 n 5 N- l Since these are three independent signal sources 14, 15, 20, it is also reasonable to assume that their noise content are also uncorrelated. It is also assumed that the three sources 14, 15, 20 produce a discrete ergotic random sequence. Therefore, the discrete autocorrelation functions for the three composite signals may be computed as follows:
N Iml - I
Rl3(m~t) = N ~ 3(n~t) 0l3([n+m]~t) 0 5 m 5 N-1 N -Iml -I
Rl2(m~t) = N ~, ~l2(n~t) ~l2([n+m]~t) O S m S N-1 n = O
N -Iml -I
R23(m~t) = N ~, ~23(n~t) 023~n+m]~t) 0 5 m 5 N- l .
n = O
Expanding the discrete autocorrelation functions and evaluating them results in: N -Iml -I
Rl3(m~t~ = N ~ [~I(n~t) - Q3(n~t)] [~ n+m]~t) - ~3([n+m]~t)]
n=O
N -Iml -I
Rl3(m~t)= 1 ~, [~l(n~thl([n+m]~t)] +~3(n~ 3([n+m]~t) n = O
- ~3(n~thl([n+m]~ l(n~th3([n+m]~
If the signals ~I(n~t), ~2(n~t), and ~3(n~t) are independent and uncorrelated, then their discrete cross-correlation functions should approach zero. Therefore: R13(m~t) = rl(m~t) + r3(m~t) R23(m~t) = r2(m~t) + r3(m~t) 213~18~
R12(rn~t) = rl(mat) + r2(m~t).
Based on the Wiener-Khinchin Theorem, the discrete Fourier transform of the discrete autocorrelation function is representative of the power spectral density.
N - I
FIRl3(m~t))= 1 ~, R13(m~t)e-i~ Sl3(fm~
m = -N-l S F( Rl2(rn~t) } = IS 12(fm) F{R23(rn~t) 3 = 1S23(fm) and S13(fm) = ISI(fm)l + IS3(fm)l S12(fm) = ISI(fm)l + IS2(fm)l S23(fm) = ls2(fm)l + IS3(fm)1.
However, it should also be noted that an alternative method for calculating discrete power spectral density functions is to use a periodogram method. The period-ogram method is implemented by taking a discrete Fourier transform of the discrete time domain signal and then computing the power spectral density. This is performed in the fast Fourier transfonn routine 47.
N - I
V(fm) = ~, V(m~t) e- j2J~fm m = O
S(fm) = 1/N IV(fm)12 The discrete power spectral density functions are then converted to a one-sided power spectrum in the same manner as described above.
Pl3(fm) = IS(fm)l + IS(-fm)l, and therefore:
P13(fm) = Pl(fm) + P3(fm) Pl2(fm) = Pl(fm) + P2(fm) P23(fm) = P2(fm) + P3(fm)-At this point, there are three equations in three unknowns and all of the individual power spectral densities can be solved for.
Pl(fm) = [P13(fm) + P12(fm) - P23(fm)] / 2 P2(fm) = [P12(fm) + P23(fm) - P23(fm)] / 2 P3(fm) = [P13(fm) + P22(fm) - P12(fm)] / 2.
Thus there has been described a new and improved phase noise measurement system that uses three independent signal sources to statistically derive the power spectral density of the phase noise content of each of them. It is to be understood that the above-described embodiment is merely illustrative of some of the many specific embodiments which represent applications of the principles of the present invention.
Clearly, numerous and other arrangements can be readily devised by those skilled in the art without departing from the scope of the invention.
BACKGROUI~D
The present invention relates generally to phase noise measurement systems, and more particularly, to a phase noise measurement system that uses three independent signal sources to statistically derive the power spectral density of the phase noise content of each of them.
Typical phase noise measurement test sets available in the commercial market fall into two primary categories: a two oscillator systems (or phase detector system) and a delay line discrirninator system. To clarify these systems, each is discussed separate-ly. The two oscillator system uses two sources (a unit under test and a reference source) that are set to the same frequency and that are in quadrature with respect to each other. These signals are input to a double balanced mixer that is used as a phase detec-tor, and the resultant output is passed through a low pass filter. The remaining signal is a low voltage DC signal that is summed with an AC signal. The AC fluctuations are proportional to the combined phase noise of the original two signal sources. This AC
signal is then fed into a spectrum analyzer and the power spectrum is displayed to a user. The primary limiting factor of this approach is the requirement that the reference source have phase noise characteristics that are at least 10 dB better than the source that is measured. In the case of phase noise measurements for radar signal sources, these sources have very low phase noise, and finding a reference that is superior can be very difficult or impossi~le. Therefore, this method is primarily used for measuring signal sources of higher noise content or for measuring phase noise close to the carrier.
The delay line discriminator system does not require additional reference sources. It uses the signal from the unit under test and splits it into two sigrials. The signal in one pats is input into a delay line whose output feeds a phase detector. The other signal is fed directly to the phase detector. Phase detecting the delayed and non-delayed signals together creates a discrimination effect which produces a frequency modulated signal 5 proportional to the signal's inherent noise content. This FM noise signal is then integrated and measured by a baseband spectrum analyzer. This system has several limitations. Its sensitivity is proportional to the delay time and the larger the delay time the greater the insertion loss. This acts as a practical limitation for the sensitivity of the system. Furthermore, the 0 sensitivity degrades as 1/f2 as the carrier is approached. Therefore, this technique is not useful measuring very stable sources close to the carrier frequency.
Accordingly, it is an objective of the present invention to provide for an improved phase noise measurement system that overcomes the 5 limitations of conventional systems.
SUMMARY OF THE INVENTION
The present system and method is used to determine the phase noise of a primary signal source. The present system uses three 2 o independent signal sources to statistically derive the power spectral density of the phase noise content of signals from each of them. This is accomplished by mixing each of the signals two at a time (i.e., signal one with signal two, signal one with signal three, and signal two with signal three) and measuring the resultant difference signals, such as with a 2 5 waveform recorder. A servo electronics subsystem is used to remove the residual low frequency products and any long term signal drift from the difference signals, and the difference signals are then captured via three channels of waveform recorders. Statistical analysis routines are then used to compute the composite power spectral densities of the difference 3 o signals, and to ultimately solve for the individual power spectral densities of the original signals. Thus, the phase noise of the primary signal source is determined.
Various aspects of the invention are as follows:
A phase noise measurement system characterized by:
a primary signal source for providing a first signal whose phase noise is to be measured;
second and third signal sources for providing second and third signals that each have substantially the same frequency and a similar noise content as said first signal provided by said primary signal source;
mixing means for mixing each of said first, second, and third signals two at a time to generate three respective difference signals;
servo means for processing said three difference signals to remove carrier signals and long term signal drift therefrom;
o waveform recorder means coupled to said mixing means for capturing said respective three difference signals;
processing means coupled to said waveform recorder means for statistical analyzing said three difference signals to compute composite power spectral densities therefor, and for computing respective individual power spectral densities of said first, second and third signals from the composite power spectral densities, and hence determining the phase noise of said primary signal source.
A method of determining the phase noise of signals provided by a primary signal source characterized by the steps of:
2 o providing a first signal from a primary signal source whose phase noise is to be measured;
providing second and third signals from second and third signal sources that each have substantially the same frequency and a similar noise content as said first signal provided by said primary signal source;
2 5 mixing each of said first, second, and third signals two at a time to generate three respective difference signals;
processing said three difference signals to remove residual carrier and drift theref~o~l;
statistically analyzing said three difference signals to compute 3 o composite power spectral densities therefor; and computing respective individual power spectral densities of said first, second and third signals from the composite power spectral densities to determine the phase noise of said primary signal source.
The present system and method thus uses mathematical 3 5 relationships between three signal sources, with similar magnitudes of 3a phase noise, to compute the power spectral density of the noise content of each source. Where traditional techniques typically require ultrastable r~f~ ce sources or expensive calibrated delay lines, the present system requires a minimum of interconnect hardware and only three inexpensive waveform recorder channels, for example. Furthermore, the size, weight, and cost of producing the present phase noise test system is substantially lower than present commercially available units.
Traditional phase noise measurement techniques typically address two problem areas: phase noise close to the carrier and phase noise far o from the carrier. Furthermore, because these problems are diverse, it takes one commercial test set to address each area. However, the present technique addresses the entire problem with one system configuration.
This has not been done by any commercial system vendor, and is therefore hel~eLof~le unavailable.
Future factory and field testers are expected to be down-sized for greater portability and lower cost. The present system relies on virtual instrument concepts to achieve this goal. Virtual instruments are presently unavailable in the commercial market place. The present system is adapted to replace existing phase noise measurement systems 2 o currently used for near-in and far-from the carrier phase measurements.
There is also a need for a system to test devices such as crystal oscillators, synthesizers, atomic clocks and standards, and low noise and ultra-low noise sources. The present system makes these tests more affordable while requiring less space.
BRIEF DESCRIPTION OF THE DRAWINGS
The various features and advantages of the present invention rnay be rnore readily understood with reference to the following detailed description taken inconjunction with the accompanying drawings, wherein like reference numerals designate like structural elements, and in which:
Fig. 1 is a system block diagram of a phase noise measurement system in accordance with the principles of the present invention; and Fig. 2 shows a software flow diagram employed in the phase noise measurement system of Fi~. 1.
DETAILED DESCRIPTIO~
Referring to the drawing figures, Fig. 1 is a system block diagram of a phase noise measurement system in accordance with the principles of the present invention.
the phase noise measurement system is comprised of two low noise reference sources 14, 15 that are both programmable and phase lockable. These reference sources 14, 15 are used in conjunction with a unit under test (WT) 20 that comprises a third source.
The reference sources 14, 15 are phase locked to the unit under test 20 by means of first and second phase locked loops 25, 26. The phase locked loops 25, 26 are adapted to minirnize cancellation of phase noise that is close to the carrier.
Outputs from the three sources (the two reference sources 14, IS and the WT
20) are applied to three splitters 16, 17, 18. Theoutputs from the splitters 16, 17, 18 are fed both to three mixers 27, 28, 29 and to three programmable phase shifters 24, 25, 26. This produces three sets of signals at the mixers 27, 28, 29 that are inquadrature. However, if the outputs of the mixers 27, 28, 29 produce a resultant signal with a residual carrier, servo electronics units 21, 22, 23 sweep the phase shifters 24, 25, 26 to produce a signal with a 0 Hz carrier (null carrier). The outputs of the three mixers 27, 28, 29 are fed into their respective low pass filters 31, 32, 33. These filters 31, 32, 33 reject high frequency rnixer products and also limit the noise bandwidth, in that they act as an anti-aliasing filter. The outputs of the filters 31, 32, 33 are applied to three waveforrn recorders 11, 12, 13. The waveform recorders 11, 12, 13 digitize the noise signals and couple them to a controVprocess computer 23 for analysis.
Fig.2 is a block diagram of software 40 employed in the controVprocess computer 23 of the phase noise measurement system 10. The software 40 is comprised 213818q s of eight functional software routines thal process the measured data and produce the desired spectral results. The first major section of the software 40 comprises a data acquisition module 41. The data acquisition module 41 provides for hardware control of the components of the system 10 and is comprised of two instrument handlers, a waveform recorder handler 42 for the waveform recorders 11, 12, 13 and a low noise source handler 43 for the low noise sources 14, lS, 16. The functions of these handlers 42, 43 include setup of the waveform recorders 11, 12, 13 and low noisesources 14, 15, 16, and programming of the phase shifters 24, 25, 26.
Once the data is digitized and acquired by the data acquisition module 41, control is passed to a phase analysis engine module 44. In the phase analysis engine module 44, the mixed phase noise data is analyzed using digital signal processing techniques, including an autocorrelation generator routine 45, averaging routine 46, noise extraction routine 48, and a fast Fourier transform routine 47. The autocorrela-tion generator routine 45 generates autocorrelation functions for the three mL~ed and lS downconverted noise signals (R13, R23, R12). To minimize the random variance and quantization noise introduced into the calculations, the autocorrelation functions for the combined noise signals (R13, R23, R12) are averaged using the averaging routine 46.
The averaging function involves acquiring multiple signal sets and computing a series of autocorrelation functions for the combined noise (R 13, R23, R 12). Then each auto-correlation function (i.e., R13) is averaged over the series. Following the averaging calculations for the combined noise, the autocorrelation functions (R13, R23, R12) are converted to power spectral density functions (P13, P23, P13) using the fast Fourier transforrnation routine 47. The conversion from autocorrelation functions to power spectral density functions is based on the Wiener-Khinchin theorem. At this point, the individual noise power spectral densities (P1, P2, P3) are then computed using the noise extraction routine 48. The power spectral densities (P1, P2, P3) are then formatted and passed to a human interface module 50. The human interface module 50 handles key~oard interactions, displaying functions, cornmand interpretations, plotting functions and calculations and the overall process flow of the software 40. This is accomplished using a process control/command interpreter routine S1 and a plotting routine 52.
The phase noise measurement system 10 is adapted to measure phase noise in active devices comprising the unit under test 20 by using sophisticated digital process-ing techniques. The novelty of this system 10 lies in the way the noise signal is han-dled and analyzed and the use of autocorrelation functions and spectral power density functions in the final analysis routines 45-48 of the phase analysis engine module 44.
The value of this technique lies in the fundamental trade-off between hardware and software, i.e. if hardware may be eliminated by using software analysis, it lowers the cost of the measurement system 10. Furtherrnore, reducing the hardware results in a lighter, smaller, and more reliable product. The present system 10 provides a means for addressing factory and bench testing applications and field testing applications requiring reduced weight, size, and cost.
The present measurement system 10 trades hardware complexity for software sophistication. It uses the mathematical relationships between discrete time measure-ments, autocorrelation functions, power spectral density conversions, and error mini-rnization theory to extract the noise content from a signal derived from the unit under test 20. To help clarify the principles and theory behind the present invention, a general mathematical description is presented below.
To measure the noise content of the signal from the unit under test 20 using thepresent system 10 requires the two additional reference sources 14, 15 having substan-tially the same frequency and similar noise content. That is, all three sources 14, 15, 20 have the same carrier frequency and have noise spectra within 10 dB of each other.
Let ~l(t), ~2(t), and 03(t) represent the phase noise content of each of the three signal sources 14, 15, 20 and assume that all three sources 14, 15, 20 have carriers in the RF/microwave region. The three signals produced by the three signal sources 14, 15, 20 are given by:
Vl(t) = Sin (WCt ffpl(t)) V2(t) = Sin (~ct +q'2(t)) V3(t) = Sin (Wct +<p3(t)) The next step is to mix the three signals together, two at a time, to create three new composite baseband signals. Assume that the mixing is performed with the signals in quadrature and the results are passed through low pass filters 31, 32, 33 each having a gain of two. The three sets of signals produced by the three signal sources 14, 15, 20 are given by:
V13(t) = Vl(t) x V3(t) x 2 V12(t) = Vl(t) x V2(t) x 2 V23(t) = V2(t) x V3(t) x 2.
Then, after filtering:
V13(t) = Sin (~I(t) - ~3(t)) V 12(t) = Sin (~1 (t) - t>2(t)) V23(t)= Sin (~2(t) - ~3(t)), which correspond to the outputs of the three mixers 27, 28, 29. The composite noise signals (i.e.: ~I(t) - ~3(t)) represent very small angles (<<.1 radians). Therefore, the following simplification is made to produce the outputs of the filters 31, 32, 33:
~138184 V13(t) = [~I(t) - 03(t)]
Vl2(~) ~ [~I(t) - ~2(t)]
V13(t) 2 [~2(t) - Q3(t)]
Since three independent signal sources 14, 15, 20 are provided, it is also reasonable to assume that the noise content for each source 14, 15, 20 is uncorrelated and that the three baseband composite noise signals are uncorrelated. In addition, it is assumed that the three sources 14, 15, 20 produce ergotic random sequences. That is, the statistics of these random sequences may be determined from a single collection of observations. If this is true, time averaging may be substituted for ensemble averaging.
Therefore, the auto correlation functions for the three composite signals may becomputed at follows. The following equations are implemented in the autocorrelation generator routine 45 of Fig. 2.
Rl3(t) = LimT~ T V13~) x ~-13(~ + t) d~
~ ,3T
Rl2(t) = LimT~ 1 V12(~) x V12(~ + t) dl3 R23(t) = LimT T V23(~) x V23(~ + t) d~
~0 Expanding one of the three autocorrelation functions reveals the following:
Rl3(t) = LimT~ T [~~ 3(~)] [Ql(~+t) - Q3(~+t)J d~
~ O
~k~ T [~ +tD ~ [~ h3(~+t)] - [~ +~h3(~)~ + [~ (~h (~ )~
~o R13(t) = rl(t) - rl3(t) - r31(t) + r3(t), 20 where rl, r3 are autocorrelation functions and rl3, r31 are cross correlation functions.
However, the phase noise of the three signals are assumed to be independent.
Therefore, all cross correlation functions approach zero and the autocorrelation function simplifies to:
R13(t) = rl(t) + r3(t).
Extrapolating these results, the autocorrelation functions of the remaining composite signals are computed in a similar manner.
R12(t) = rl(t) + r2(t), and R23(t) = r2(t) + r3(t) The last three equations represent the autocorrelation functions computed by theautocorrelation generator routine 45.
Z13~1Q~
The averaging routine 46 sums a plurality of sets of autocorrelation signals anddivides the resultant sum by the number of summed sets to produce an average value for the respective autorrelation functions. This averages out fluctuations in the signals.
Based on the Wiener-Khinchin Theorem, the Fourier transform of the S autocorrelation function is representative of the power spectral density. The following equations are implemented in the Fourier transforrn routine 48 of Fig. 2.
F~R13(t)) = lsl3(f)} -~ < f S +
F(Rl2(t)) = 1S12(f)) ~~ < f < +
F(R23(t)) = 1S23(f)) ~~ < f < + ~
For this process, a user would be more interested in the power contained in a frequency interval from 0 to + ~ and would not want to distinguish between positive and negative values of frequency. Therefore, a one sided power spectral density function may be defined as follows:
P13(f) -- lS13(f)l + IS13(-f)l 0 < f <
Pl2(f) -- lS12(f)l + lSI2~ 0 < f ~ ~
P23(f) - 1s23(f)l + lS23(-f)l 0 < f < oo From above, Rl3(t) = rl(t) + r3(t) and the Fourier transform of R13(t) may be represented as follows:
F(Rl3(t)} = Flrl(t)} + F(r3(t)}, or IS13(f)1 = ISl(f)l + IS3(f)1 _OO < f S ~.
Converting the double sided power spectral density to a single sided representation yields:
Pl3(f) = Pl(f) + P3(f) 0 < f < ~.
Therefore, extrapolating these results for all three power spectral densities results in the following equations:
Pl3(f) = Pl(f) + P3(f) 0 S f S x, Pl2(f) = Pl(f) + P2(f) 0 S f S ~
P23(f) = P2(f) + P3(f) 0 S f s OO.
With these three equations, it is possible to solve for the individual power spectral densities of the three sources 14, 15, 20 which are represented by Pl(f), P2(f), and P3(f) as a function of the measured composite power spectral densities represented by Pl3(f), Pl2(f), and P23(f). This is implemented in the noise extraction routine 48.
Pl(f) = [Pl3(f) + Pl2(f) - P23(f)] /2 P2(f) = [Pl2(f) + P23(f) - Pl3(f)] /2 P3(f) = [pl3(f) + P23(f) - Pl2(f)] /2.
~138i~
The mathematical description presented above is for a continuous systems case.
However, to implement a real world instrument to perform this process requires discretization and complex digital signal processing. Therefore, it is important to validate the process for systems that are characterized by discrete data samples.
S Let ~l(n~t), ~2(n~t), and ~3(n~t) represent the discretely sampled phase noise content of the three discrete signal sources 14, 15, 20. These signal sources 14, 15, 20 (and their noise content) were originally continuous signals that were mixed down to 0 Hz baseband and then digitized. The rnixing process is the same as described above.
V13(n~t) = Sin [~I(n~t) - 03(n~t)] 0 S n S N-1 V12(n~t) = Sin [~I(n~t) - ~2(n~t)] 0 5 n S N-l V23(n~t) = Sin [~2(n~t) - ~3(n~t)] 0 5 n 5 N- 1 The composite noise signals (i.e.,: ~I(nat) - ~3(n~t)) represent very small angles (~<.1 radians). Therefore, the following simplification is made:
V13(n~t) = [Ol(n~t) - o3(n~t)] 0 5 n 5 N-l V12(n~t) = ~Ol(n~t) - ~(n~t)~ 0 S n S N- I
V23(n~t) = [~2(n~t) - Q3(n~t)] 0 5 n 5 N- l Since these are three independent signal sources 14, 15, 20, it is also reasonable to assume that their noise content are also uncorrelated. It is also assumed that the three sources 14, 15, 20 produce a discrete ergotic random sequence. Therefore, the discrete autocorrelation functions for the three composite signals may be computed as follows:
N Iml - I
Rl3(m~t) = N ~ 3(n~t) 0l3([n+m]~t) 0 5 m 5 N-1 N -Iml -I
Rl2(m~t) = N ~, ~l2(n~t) ~l2([n+m]~t) O S m S N-1 n = O
N -Iml -I
R23(m~t) = N ~, ~23(n~t) 023~n+m]~t) 0 5 m 5 N- l .
n = O
Expanding the discrete autocorrelation functions and evaluating them results in: N -Iml -I
Rl3(m~t~ = N ~ [~I(n~t) - Q3(n~t)] [~ n+m]~t) - ~3([n+m]~t)]
n=O
N -Iml -I
Rl3(m~t)= 1 ~, [~l(n~thl([n+m]~t)] +~3(n~ 3([n+m]~t) n = O
- ~3(n~thl([n+m]~ l(n~th3([n+m]~
If the signals ~I(n~t), ~2(n~t), and ~3(n~t) are independent and uncorrelated, then their discrete cross-correlation functions should approach zero. Therefore: R13(m~t) = rl(m~t) + r3(m~t) R23(m~t) = r2(m~t) + r3(m~t) 213~18~
R12(rn~t) = rl(mat) + r2(m~t).
Based on the Wiener-Khinchin Theorem, the discrete Fourier transform of the discrete autocorrelation function is representative of the power spectral density.
N - I
FIRl3(m~t))= 1 ~, R13(m~t)e-i~ Sl3(fm~
m = -N-l S F( Rl2(rn~t) } = IS 12(fm) F{R23(rn~t) 3 = 1S23(fm) and S13(fm) = ISI(fm)l + IS3(fm)l S12(fm) = ISI(fm)l + IS2(fm)l S23(fm) = ls2(fm)l + IS3(fm)1.
However, it should also be noted that an alternative method for calculating discrete power spectral density functions is to use a periodogram method. The period-ogram method is implemented by taking a discrete Fourier transform of the discrete time domain signal and then computing the power spectral density. This is performed in the fast Fourier transfonn routine 47.
N - I
V(fm) = ~, V(m~t) e- j2J~fm m = O
S(fm) = 1/N IV(fm)12 The discrete power spectral density functions are then converted to a one-sided power spectrum in the same manner as described above.
Pl3(fm) = IS(fm)l + IS(-fm)l, and therefore:
P13(fm) = Pl(fm) + P3(fm) Pl2(fm) = Pl(fm) + P2(fm) P23(fm) = P2(fm) + P3(fm)-At this point, there are three equations in three unknowns and all of the individual power spectral densities can be solved for.
Pl(fm) = [P13(fm) + P12(fm) - P23(fm)] / 2 P2(fm) = [P12(fm) + P23(fm) - P23(fm)] / 2 P3(fm) = [P13(fm) + P22(fm) - P12(fm)] / 2.
Thus there has been described a new and improved phase noise measurement system that uses three independent signal sources to statistically derive the power spectral density of the phase noise content of each of them. It is to be understood that the above-described embodiment is merely illustrative of some of the many specific embodiments which represent applications of the principles of the present invention.
Clearly, numerous and other arrangements can be readily devised by those skilled in the art without departing from the scope of the invention.
Claims (6)
1. A phase noise measurement system characterized by:
a primary signal source for providing a first signal whose phase noise is to be measured;
second and third signal sources for providing second and third signals that each have substantially the same frequency and a similar noise content as said first signal provided by said primary signal source;
mixing means for mixing each of said first, second, and third signals two at a time to generate three respective difference signals;
servo means for processing said three difference signals to remove carrier signals and long term signal drift therefrom;
waveform recorder means coupled to said mixing means for capturing said respective three difference signals;
processing means coupled to said waveform recorder means for statistical analyzing said three difference signals to compute composite power spectral densities therefor, and for computing respective individual power spectral densities of said first, second and third signals from the composite power spectral densities, and hence determining the phase noise of said primary signal source.
a primary signal source for providing a first signal whose phase noise is to be measured;
second and third signal sources for providing second and third signals that each have substantially the same frequency and a similar noise content as said first signal provided by said primary signal source;
mixing means for mixing each of said first, second, and third signals two at a time to generate three respective difference signals;
servo means for processing said three difference signals to remove carrier signals and long term signal drift therefrom;
waveform recorder means coupled to said mixing means for capturing said respective three difference signals;
processing means coupled to said waveform recorder means for statistical analyzing said three difference signals to compute composite power spectral densities therefor, and for computing respective individual power spectral densities of said first, second and third signals from the composite power spectral densities, and hence determining the phase noise of said primary signal source.
2. The system of Claim 1 wherein said mixing means is characterized by:
a plurality of mixers;
a plurality of signal splitters selectively coupled between said signal sources and first inputs of said respective mixers;
a plurality of programmable phase shifters respectively coupled between a selected said splitter and second inputs of selected said mixers;
and a plurality of filters coupled to respective outputs of said plurality of mixers.
a plurality of mixers;
a plurality of signal splitters selectively coupled between said signal sources and first inputs of said respective mixers;
a plurality of programmable phase shifters respectively coupled between a selected said splitter and second inputs of selected said mixers;
and a plurality of filters coupled to respective outputs of said plurality of mixers.
3. The system of Claim 2 wherein said waveform recorder means is characterized by three waveform recorder channels.
4. The system of Claim 3 further characterized by a plurality of phase locked loops.
5. A method of determining the phase noise of signals provided by a primary signal source characterized by the steps of:
providing a first signal from a primary signal source whose phase noise is to be measured;
providing second and third signals from second and third signal sources that each have substantially the same frequency and a similar noise content as said first signal provided by said primary signal source;
mixing each of said first, second, and third signals two at a time to generate three respective difference signals;
processing said three difference signals to remove residual carrier and drift therefrom;
statistically analyzing said three difference signals to compute composite power spectral densities therefor; and computing respective individual power spectral densities of said first, second and third signals from the composite power spectral densities to determine the phase noise of said primary signal source.
providing a first signal from a primary signal source whose phase noise is to be measured;
providing second and third signals from second and third signal sources that each have substantially the same frequency and a similar noise content as said first signal provided by said primary signal source;
mixing each of said first, second, and third signals two at a time to generate three respective difference signals;
processing said three difference signals to remove residual carrier and drift therefrom;
statistically analyzing said three difference signals to compute composite power spectral densities therefor; and computing respective individual power spectral densities of said first, second and third signals from the composite power spectral densities to determine the phase noise of said primary signal source.
6. The method of Claim 5 wherein the step of processing said three difference signals to remove the residual carrier and drift therefrom comprises the step of:
sweeping a plurality of phase shifters to remove the respective residual carrier and drift from said three difference signals.
sweeping a plurality of phase shifters to remove the respective residual carrier and drift from said three difference signals.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US08/173,534 | 1993-12-23 | ||
| US08/173,534 US5412325A (en) | 1993-12-23 | 1993-12-23 | Phase noise measurement system and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CA2138184A1 CA2138184A1 (en) | 1995-06-24 |
| CA2138184C true CA2138184C (en) | 1998-07-28 |
Family
ID=22632467
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA002138184A Expired - Fee Related CA2138184C (en) | 1993-12-23 | 1994-12-15 | Phase noise measurement system and method |
Country Status (7)
| Country | Link |
|---|---|
| US (1) | US5412325A (en) |
| EP (1) | EP0660124B1 (en) |
| JP (1) | JP2869012B2 (en) |
| AU (1) | AU663033B2 (en) |
| CA (1) | CA2138184C (en) |
| DE (1) | DE69432212T2 (en) |
| ES (1) | ES2194020T3 (en) |
Families Citing this family (49)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6057690A (en) * | 1998-01-14 | 2000-05-02 | Advanced Testing Technologies, Inc. | Automatic testing device for signal evaluation |
| EP1055129A4 (en) * | 1998-01-14 | 2004-07-28 | Advanced Testing Technologies | Phase noise measurement system |
| US6393372B1 (en) * | 1999-05-17 | 2002-05-21 | Eugene Rzyski | Automated frequency stepping noise measurement system |
| US7583728B2 (en) * | 2002-10-25 | 2009-09-01 | The Directv Group, Inc. | Equalizers for layered modulated and other signals |
| US7184489B2 (en) * | 2001-04-27 | 2007-02-27 | The Directv Group, Inc. | Optimization technique for layered modulation |
| US7245671B1 (en) * | 2001-04-27 | 2007-07-17 | The Directv Group, Inc. | Preprocessing signal layers in a layered modulation digital signal system to use legacy receivers |
| US7639759B2 (en) * | 2001-04-27 | 2009-12-29 | The Directv Group, Inc. | Carrier to noise ratio estimations from a received signal |
| US8005035B2 (en) | 2001-04-27 | 2011-08-23 | The Directv Group, Inc. | Online output multiplexer filter measurement |
| US7471735B2 (en) * | 2001-04-27 | 2008-12-30 | The Directv Group, Inc. | Maximizing power and spectral efficiencies for layered and conventional modulations |
| US7502430B2 (en) * | 2001-04-27 | 2009-03-10 | The Directv Group, Inc. | Coherent averaging for measuring traveling wave tube amplifier nonlinearity |
| US7423987B2 (en) | 2001-04-27 | 2008-09-09 | The Directv Group, Inc. | Feeder link configurations to support layered modulation for digital signals |
| US7173981B1 (en) | 2001-04-27 | 2007-02-06 | The Directv Group, Inc. | Dual layer signal processing in a layered modulation digital signal system |
| US7778365B2 (en) * | 2001-04-27 | 2010-08-17 | The Directv Group, Inc. | Satellite TWTA on-line non-linearity measurement |
| US7209524B2 (en) * | 2001-04-27 | 2007-04-24 | The Directv Group, Inc. | Layered modulation for digital signals |
| US7483505B2 (en) | 2001-04-27 | 2009-01-27 | The Directv Group, Inc. | Unblind equalizer architecture for digital communication systems |
| US7822154B2 (en) * | 2001-04-27 | 2010-10-26 | The Directv Group, Inc. | Signal, interference and noise power measurement |
| WO2004004193A2 (en) * | 2002-07-01 | 2004-01-08 | The Directv Group, Inc. | Improving hierarchical 8psk performance |
| TWI279113B (en) * | 2002-07-03 | 2007-04-11 | Hughes Electronics Corp | Method and apparatus for layered modulation |
| US7529312B2 (en) * | 2002-10-25 | 2009-05-05 | The Directv Group, Inc. | Layered modulation for terrestrial ATSC applications |
| US7463676B2 (en) * | 2002-10-25 | 2008-12-09 | The Directv Group, Inc. | On-line phase noise measurement for layered modulation |
| US7474710B2 (en) | 2002-10-25 | 2009-01-06 | The Directv Group, Inc. | Amplitude and phase matching for layered modulation reception |
| EP1563620B1 (en) * | 2002-10-25 | 2012-12-05 | The Directv Group, Inc. | Lower complexity layered modulation signal processor |
| US7502429B2 (en) * | 2003-10-10 | 2009-03-10 | The Directv Group, Inc. | Equalization for traveling wave tube amplifier nonlinearity measurements |
| JP2005308510A (en) * | 2004-04-21 | 2005-11-04 | Agilent Technol Inc | Apparatus and system for measuring phase noise |
| JP2005308511A (en) * | 2004-04-21 | 2005-11-04 | Agilent Technol Inc | Method and apparatus for measuring phase noise |
| JP4761724B2 (en) * | 2004-04-21 | 2011-08-31 | アジレント・テクノロジーズ・インク | Method for measuring phase noise and phase noise measuring apparatus |
| US8965727B2 (en) * | 2005-05-20 | 2015-02-24 | Omniphase Research Laboratories, Inc. | Intelligent low noise design |
| JP2007086006A (en) * | 2005-09-26 | 2007-04-05 | Fujitsu Ten Ltd | Radar device |
| US20080279268A1 (en) * | 2007-05-10 | 2008-11-13 | Agilent Technologies, Inc. | Method for measuring noise, apparatus for measuring noise, and program for measuring noise |
| US9140750B2 (en) * | 2009-10-21 | 2015-09-22 | Advantest Corporation | Apparatus comprising a recursive delayer and method for measuring a phase noise |
| WO2011047699A1 (en) * | 2009-10-21 | 2011-04-28 | Verigy (Singapore) Pte. Ltd. | Test device and test method for measuring a phase noise of a test signal |
| CN102043091B (en) * | 2009-10-21 | 2013-06-26 | 中国科学院半导体研究所 | Digitized high-precision phase detector |
| US8248297B1 (en) * | 2011-04-11 | 2012-08-21 | Advanced Testing Technologies, Inc. | Phase noise measurement system and method |
| US9442148B2 (en) * | 2011-07-15 | 2016-09-13 | Teradyne, Inc. | ATE to detect signal characteristics of a DUT |
| US8688052B2 (en) | 2012-01-03 | 2014-04-01 | Silicon Laboratories Inc. | Apparatus and method for phase noise self-test |
| US10718804B2 (en) * | 2012-01-31 | 2020-07-21 | Keysight Technologies, Inc. | System for measuring residual phase noise |
| US8711992B2 (en) * | 2012-05-31 | 2014-04-29 | Agilent Technologies, Inc. | Phase noise extraction apparatus and technique |
| CN102901880B (en) * | 2012-08-03 | 2014-12-17 | 郑州轻工业学院 | Phase noise measuring method based on wide frequency range, short response time and high resolution |
| GB201315089D0 (en) * | 2013-08-23 | 2013-10-09 | Camlin Technologies Ltd | Diagnostic method for automatic discrimination of phase-to-ground partial discharge, phase-to-phase partial discharge and electromagnetic noise |
| US9007259B1 (en) * | 2014-06-13 | 2015-04-14 | Advanced Testing Technologies Inc. | Flight line noise tester |
| US9864044B2 (en) * | 2014-06-25 | 2018-01-09 | Raytheon Company | Methods and systems for improving signal to phase noise in radars |
| US9453906B2 (en) * | 2014-07-31 | 2016-09-27 | North Carolina State University | Phase calibration circuit and method for multi-channel radar receiver |
| US10571501B2 (en) * | 2016-03-16 | 2020-02-25 | Intel Corporation | Technologies for verifying a de-embedder for interconnect measurement |
| US10432325B1 (en) * | 2018-06-07 | 2019-10-01 | Globalfoundries Inc. | Testing phase noise in output signal of device under test using transformable frequency signals |
| CN110187197B (en) * | 2019-04-11 | 2021-07-27 | 东南大学 | Automatic additional phase noise measuring device |
| CN110967565A (en) * | 2019-10-11 | 2020-04-07 | 基合半导体（宁波）有限公司 | Noise detection circuit and method of capacitive screen and mobile terminal |
| US10756829B1 (en) | 2019-12-03 | 2020-08-25 | Teradyne, Inc. | Determining error vector magnitude using cross-correlation |
| CN117097353A (en) | 2022-05-11 | 2023-11-21 | 莱特普茵特公司 | Correcting error vector magnitude measurements |
| CN117478250A (en) | 2022-07-21 | 2024-01-30 | 莱特普茵特公司 | Correcting error vector magnitude measurements |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4748399A (en) * | 1986-04-21 | 1988-05-31 | The United States Of America As Represented By The Secretary Of The Air Force | Multichannel phase noise measurement system |
| FR2647221B1 (en) * | 1989-05-19 | 1991-08-16 | Thomson Csf | MEASUREMENT CHAIN OF THE ADDITIVE PHASE NOISE CHARACTERISTICS OF A COMPONENT NEAR A CARRIER FREQUENCY |
| FR2663750B1 (en) * | 1990-06-22 | 1992-09-11 | Alcatel Espace | DEVICE FOR MEASURING THE MULTIPACTOR EFFECT BY PHASE NOISE. |
| US5179344A (en) * | 1991-06-21 | 1993-01-12 | Harris Corporation | Phase noise measurements utilizing a frequency down conversion/multiplier, direct spectrum measurement technique |
| US5172064A (en) * | 1991-12-02 | 1992-12-15 | The United States Of America As Represented By The Secretary Of Commerce | Calibration system for determining the accuracy of phase modulation and amplitude modulation noise measurement apparatus |
-
1993
- 1993-12-23 US US08/173,534 patent/US5412325A/en not_active Expired - Fee Related
-
1994
- 1994-12-15 CA CA002138184A patent/CA2138184C/en not_active Expired - Fee Related
- 1994-12-17 DE DE69432212T patent/DE69432212T2/en not_active Expired - Fee Related
- 1994-12-17 ES ES94120059T patent/ES2194020T3/en not_active Expired - Lifetime
- 1994-12-17 EP EP94120059A patent/EP0660124B1/en not_active Expired - Lifetime
- 1994-12-20 AU AU81610/94A patent/AU663033B2/en not_active Ceased
- 1994-12-26 JP JP6323367A patent/JP2869012B2/en not_active Expired - Fee Related
Also Published As
| Publication number | Publication date |
|---|---|
| EP0660124A2 (en) | 1995-06-28 |
| JPH07270464A (en) | 1995-10-20 |
| CA2138184A1 (en) | 1995-06-24 |
| AU663033B2 (en) | 1995-09-21 |
| JP2869012B2 (en) | 1999-03-10 |
| DE69432212D1 (en) | 2003-04-10 |
| ES2194020T3 (en) | 2003-11-16 |
| EP0660124A3 (en) | 1996-07-03 |
| EP0660124B1 (en) | 2003-03-05 |
| DE69432212T2 (en) | 2003-12-11 |
| AU8161094A (en) | 1995-07-13 |
| US5412325A (en) | 1995-05-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CA2138184C (en) | Phase noise measurement system and method | |
| US3731188A (en) | Signal analysis of multiplicatively related frequency components in a complex signal | |
| Horton | Noise-modulated distance measuring systems | |
| EP0125838B1 (en) | Direction finding | |
| Micheletti | Phase angle measurement between two sinusoidal signals | |
| US20100274510A1 (en) | Method of Detecting System Function by Measuring Frequency Response | |
| FI88076C (en) | SIMULTANT NMR-BILDVISNINGSSYSTEM | |
| US5198748A (en) | Frequency measurement receiver with bandwidth improvement through synchronized phase shifted sampling | |
| US5179344A (en) | Phase noise measurements utilizing a frequency down conversion/multiplier, direct spectrum measurement technique | |
| US8116709B2 (en) | Frequency measuring broadband digital receiver | |
| Ghonge et al. | Reconstructing gravitational wave signals from binary black hole mergers with minimal assumptions | |
| US20080270440A1 (en) | Data Compression for Producing Spectrum Traces | |
| Ables et al. | A 1024− channel digital correlator | |
| US5508605A (en) | Method for measuring RF pulse frequency | |
| JP2005233932A (en) | Analyzer of analyzing wide band irregular signal, device for decomposition-displaying wide band signal, device for determining deviation between two wide band irregular signals, object detector, and method of preparing separation means for analyzer | |
| EP0268519A1 (en) | System of processing signals drowned in a noise and its application to fringe laser velocimetry | |
| US5235287A (en) | Frequency measurement receiver with bandwidth improvement through phase shifted sampling of real signals using sampling rate selection | |
| Wright et al. | A digital heterodyne 2-to 150-kHz measurement method based on multiresolution analysis | |
| US4860227A (en) | Circuit for measuring characteristics of a device under test | |
| US7701195B2 (en) | Actual-ray and image-ray distinction in frequency converters for spectrum analysers | |
| GB2295899A (en) | Method for measuring the frequency of continous wave and wide pulse RF signals | |
| Gruchaila-Węsierski et al. | The performance of the IFM receiver in a dense signal environment | |
| WO2006051498A1 (en) | Image cancellation in frequency converters for spectrum analysers | |
| JPH0374341B2 (en) | ||
| Min et al. | Design concepts of instruments for vector parameter identification |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| EEER | Examination request | ||
| MKLA | Lapsed |