CN113721208A - Radar signal-to-noise ratio estimation method - Google Patents

Radar signal-to-noise ratio estimation method Download PDF

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CN113721208A
CN113721208A CN202111020388.8A CN202111020388A CN113721208A CN 113721208 A CN113721208 A CN 113721208A CN 202111020388 A CN202111020388 A CN 202111020388A CN 113721208 A CN113721208 A CN 113721208A
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signal
radar
matrix
noise
noise ratio
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CN113721208B (en
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张永超
张寅�
张平
周枭坤
黄钰林
张永伟
杨海光
杨建宇
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Yangtze River Delta Research Institute of UESTC Huzhou
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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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/40—Means for monitoring or calibrating
    • 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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention discloses a radar signal-to-noise ratio estimation method, which obtains an autocorrelation matrix of a radar receiving signal in an iteration mode, separates noise subspaces through characteristic decomposition, estimates noise variance power by using a noise characteristic value, and finally obtains signal-to-noise ratio estimation according to signal power. Compared with the existing method, the method can obtain the autocorrelation matrix of the signal in an iteration mode according to the signal sample once, does not depend on the snapshot number, and has higher estimation precision under the condition of single sample; the method can adaptively obtain the signal-to-noise ratio estimation for any input signal, thereby accurately estimating the signal-to-noise ratio.

Description

Radar signal-to-noise ratio estimation method
Technical Field
The invention belongs to the technical field of radar imaging, and particularly relates to a signal-to-noise ratio estimation method in radar imaging.
Background
The signal-to-noise ratio is one of important parameters for imaging and detecting of the scanning radar and is an important parameter for measuring the interference performance of an interference machine or the anti-interference performance of the radar; in scanning radar target detection, the signal-to-noise ratio affects the target detection probability; in radar imaging, the signal-to-noise ratio affects the performance of various imaging algorithms and the selection of optimal parameters. In radar signal processing, therefore, an estimation of the signal-to-noise ratio is often required.
The earliest methods for measuring the signal-to-noise ratio of radar were manual measurements from the video side, but they were dependent on the interpretation experience and level of the radar operator and were difficult to achieve adaptive estimation. For SNR Estimation, especially for adaptive Estimation Based on signal processing, the document "motion-Based SNR Estimation over linear-Modulated Wireless SIMO Channels" (Wireless Communications, IEEE Transactions on,2010, pp.714-722) distinguishes signals and noise by constructing second and fourth order moments according to their statistical properties, however this method is highly dependent on a large number of snapshots. In radar signal processing, especially under a moving platform, the beam residence time is short, and accumulation of large fast beat number cannot be formed, so that the method is not suitable for radar signal processing. The maximum likelihood estimator is used in the document "Non-Data-aid Signal-to-Noise-Ratio Estimation" (Communications,2002.ICC 2002.IEEE International Conference on, 2002, pp.197-201) to estimate the Noise variance as well as the Signal power. Under data-aided conditions, the ML-like algorithm is optimal; but without data assistance, the estimation effect is better at high signal-to-noise ratio, but the performance is very poor at low signal-to-noise ratio due to the influence of decision errors.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a radar signal-to-noise ratio estimation method.
The specific technical scheme of the invention is as follows: a radar signal-to-noise ratio estimation method specifically comprises the following steps:
step 1. definition of s ═ s1,s2,...,sK]Target distribution in the same beam range is realized, wherein K is the number of targets; define h as [ h ]1,h2,...,hL]TTransmitting a signal for a radar, wherein L is the length of a transmission sequence; let the radar receive signal be y,
Figure BDA0003241181960000011
wherein e is a noise signal, and the length of the radar receiving signal y sequence is M;
constructing an M multiplied by K size convolution matrix according to the radar emission signal h:
Figure BDA0003241181960000012
constructing a signal-noise steering matrix: a' ═ A I]Let akIs the kth column of matrix a', where K is 1.., K + M;
constructing an autocorrelation matrix of the radar received signal y:
Figure BDA0003241181960000021
initializing R ═ I, where I is the M-dimensional identity matrix, (·)HRepresenting a conjugate transpose operation;
constructing a covariance matrix P:
Figure BDA0003241181960000022
obtaining s by least squareskEstimation of (2):
Figure BDA0003241181960000023
wherein s iskTo a target distribution, pkIs the target power.
Step 2. to find pkStructural letterNumber of
Figure BDA0003241181960000024
Due to pkFurther transformed into:
Figure BDA0003241181960000025
step 3, constructing a matrix Q so that QHLet Q be PAR-1，β＝Qy＝PAR-1y；
Step 4. according to step 3, equation (1) is converted to:
Figure BDA0003241181960000026
step 5, solving the formula (2) to obtain pk：
Figure RE-GDA0003308720030000027
Wherein ρ (i) is a set iterative operator, specifically:
Figure BDA0003241181960000028
and 6, obtaining the final estimation of the autocorrelation matrix R according to the preset iteration N times:
Figure BDA0003241181960000031
and 7, calculating an estimated value of the noise power:
Figure BDA0003241181960000032
and further calculating a signal-to-noise ratio estimation value:
Figure BDA0003241181960000033
wherein, PsIs the radar echo signal power.
The invention has the beneficial effects that: the signal-to-noise ratio estimation method obtains the self-correlation matrix of the radar receiving signals in an iteration mode, then separates noise subspaces through characteristic decomposition, estimates noise variance power by using noise characteristic values, and finally obtains the signal-to-noise ratio estimation according to the signal power. Compared with the existing method, the method can obtain the autocorrelation matrix of the signal in an iteration mode according to the signal sample of one time, does not depend on the number of snapshots, and has higher estimation precision under the condition of single sample; the method can adaptively obtain the signal-to-noise ratio estimation for any input signal, thereby accurately estimating the signal-to-noise ratio.
Drawings
FIG. 1 is a schematic flow chart of a signal-to-noise ratio estimation method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a target distribution according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a transmitted signal according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating echoes received by a radar in accordance with an embodiment of the present invention;
FIG. 5 is a diagram illustrating matched filtered echoes in accordance with an embodiment of the present invention;
FIG. 6 is a diagram illustrating SNR estimates at different SNR according to an embodiment of the present invention;
FIG. 7 is a diagram illustrating normalized RMS error at different SNR according to an embodiment of the invention.
Detailed Description
The embodiments of the present invention will be further described with reference to the accompanying drawings.
(1) Let the radar emission signal be a chirp signal with a bandwidth B of 50MHz and a time width T of 2 μ s, and its specific expression is as shown in fig. 3
Figure BDA0003241181960000034
Where K is B/T is the chirp rate, fcIs the carrier frequency, t is the distance time; assuming two point targets s with normalized amplitudes of 0.5 and 1, respectively, in the same orientation1And s2Respectively located at a range radar R11000m and R21100m, the distribution of objects is shown in fig. 2, and then the echoes of these objects after coherent demodulation can be expressed as:
Figure BDA0003241181960000041
additionally setting a sampling rate fsWhen the length L of the transmitted signal h (t) is 120 and the length M of the received signal y (t) is 162, it can be known that the number K of scene samples is M-L +1 is 43. When gaussian white noise is mixed in the received signal, where the signal-to-noise ratio is set to 0dB, the signal-to-noise ratio after matched filtering is theoretically improved by 100 times, that is, by 20 dB.
A specific flow of the specific estimation method of the present embodiment is shown in fig. 1, and includes the following steps:
A. firstly, constructing a direction matrix A according to a transmitting signal h (t):
constructing a first row and a first column of the matrix A according to h (t), and obtaining A according to Toeplitz properties of A; constructing a signal-noise steering matrix:
A′＝[A I] (8)
B. initializing an autocorrelation matrix R as an identity matrix I;
C. expression for obtaining radar echo signal from s and h
Figure BDA0003241181960000042
Where e is a noise signal.
Constructing an autocorrelation matrix of the radar echo signal y:
Figure BDA0003241181960000043
D. according to the steps A and B, constructing a covariance matrix P:
Figure BDA0003241181960000044
obtaining s by least squareskEstimation of (2):
Figure BDA0003241181960000045
E. according to step A, B, C, to determine pkAnd constructing a function:
Figure BDA0003241181960000051
due to pkThe convergence of (2) can be further transformed into:
Figure BDA0003241181960000052
F. constructing a matrix Q such that QHLet Q be PAR-1
β＝Qy＝PAR-1y (15)
G. According to step F above, equation (14) can be converted into:
Figure BDA0003241181960000053
H. solving the formula (16) to obtain pk：
Figure BDA0003241181960000054
Wherein ρ (i) is a set iterative operator, specifically:
Figure BDA0003241181960000055
I. repeating the step H to appoint iteration N times to obtain the final estimation of the autocorrelation matrix R
Figure BDA0003241181960000056
J. Performing characteristic decomposition on R to obtain a characteristic value lambda1,λ2,...,λM
K. For lambda1,λ2,...,λMIs ordered as lambda1,λ2,...,λMHere, the number of large eigenvalues is X equal to 30. From the remaining small eigenvalues, the noise power is estimated as follows.
Calculating an estimate of the noise power:
Figure BDA0003241181960000057
l. fig. 4 shows a diagram of echoes received by the radar, and fig. 5 shows a diagram of echoes after matching filtering.
Calculating the signal-to-noise ratio estimated value after matching filtering:
Figure BDA0003241181960000061
wherein, Py1.829W is the average power of the received signal y (t).
(2) Keeping other parameters unchanged, and changing the signal-to-noise ratio between 0dB and 30dB, the signal-to-noise ratio estimation value of the method of the invention is shown in figure 6; FIG. 7 shows normalized RMS error for 100 Monte Carlo experiments at different signal-to-noise ratios, where normalized RMS error is defined as:
Figure BDA0003241181960000062
wherein the content of the first and second substances,s represents the number of monte carlo experiments, μ represents the true value,
Figure BDA0003241181960000063
represents the estimated value in the i-th experiment.
The embodiment shows that the method can obtain the autocorrelation matrix of the signal in an iterative mode according to the signal sample once, does not depend on the number of snapshots, and has higher estimation accuracy under the condition of single sample; the method can adaptively obtain the signal-to-noise ratio estimation for any input signal, thereby accurately estimating the signal-to-noise ratio.

Claims (1)

1. A radar signal-to-noise ratio estimation method specifically comprises the following steps:
step 1. definition of s ═ s1,s2,...,sK]Target distribution in the same beam range is realized, wherein K is the number of targets; definition h ═ h1,h2,...,hL]TTransmitting a signal for a radar, wherein L is the length of a transmission sequence; let the radar receive signal be y,
Figure FDA0003241181950000011
wherein e is a noise signal, and the length of the radar receiving signal y sequence is M;
constructing an M multiplied by K size convolution matrix according to the radar emission signal h:
Figure FDA0003241181950000012
constructing a signal-noise steering matrix: a' ═ A I]Let akIs the kth column of matrix a', where K is 1.., K + M;
constructing an autocorrelation matrix of the radar received signal y:
Figure FDA0003241181950000013
initializing R ═ I, where I is the M-dimensional identity matrix, (·)HRepresenting a conjugate transpose operation;
constructing a covariance matrix P:
Figure FDA0003241181950000014
obtaining s by least squareskEstimation of (2):
Figure FDA0003241181950000015
wherein s iskTo a target distribution, pkIs the target power.
Step 2. to find pkConstruction function
Figure FDA0003241181950000016
Due to pkFurther transformed into:
Figure FDA0003241181950000017
step 3, constructing a matrix Q so that QHLet Q be PAR-1，β＝Qy＝PAR-1y；
Step 4. according to step 3, equation (1) is converted to:
Figure FDA0003241181950000021
step 5, solving the formula (2) to obtain pk：
Figure FDA0003241181950000022
Wherein ρ (i) is a set iterative operator, specifically:
Figure FDA0003241181950000023
and 6, obtaining the final estimation of the autocorrelation matrix R according to the preset iteration times:
Figure FDA0003241181950000024
and 7, calculating an estimated value of the noise power:
Figure FDA0003241181950000025
and further calculating a signal-to-noise ratio estimation value:
Figure FDA0003241181950000026
wherein, PsIs the radar echo signal power.
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