CN113589269A - Passive multi-base-station radar target detection method based on linear fusion - Google Patents

Passive multi-base-station radar target detection method based on linear fusion Download PDF

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CN113589269A
CN113589269A CN202110797583.5A CN202110797583A CN113589269A CN 113589269 A CN113589269 A CN 113589269A CN 202110797583 A CN202110797583 A CN 202110797583A CN 113589269 A CN113589269 A CN 113589269A
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CN113589269B (en
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赵红燕
雷旭鹏
李灯熬
李朋伟
李付江
李窦哲
程俊兵
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Taiyuan University of Technology
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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
    • 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
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/06—Systems determining position data of a target
    • 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
    • G01S13/66—Radar-tracking systems; Analogous systems
    • G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
    • G01S13/723—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar by using numerical data
    • G01S13/726—Multiple target tracking
    • 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
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    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention belongs to the technical field of passive radar target detection, in particular to a passive multi-base-station radar target detection method based on linear fusion; the method comprises the following specific steps: modeling expressions of reference channel and monitoring channel signals; performing cross-correlation processing on the received signals of the two channels to obtain local test statistics of each receiving base station of the passive radar; establishing a signal-to-noise ratio of a reference channel, monitoring the linear expressions of the signal-to-noise ratio and the drying ratio of the channel to represent the detection performance of each base station, and determining the coefficient values of the three parameters of the base station by using an entropy weight method; according to the base station detection performance index obtained in the last step, determining weight coefficient vectors when local test statistics of each base station are linearly fused, further determining global test statistics, and comparing the global statistics with a threshold to make target detection judgment; the method can effectively utilize the information provided by the base station to obtain higher target detection probability.

Description

Passive multi-base-station radar target detection method based on linear fusion
Technical Field
The invention belongs to the technical field of radar, relates to a passive radar target detection technology, and particularly relates to a passive multi-base-station radar target detection method based on linear fusion.
Background
The passive radar is a radar system which is not provided with a transmitter and mainly realizes target detection by intercepting existing electromagnetic signals. The passive radar has the advantages of interference resistance, anti-stealth, low-altitude attack resistance, anti-radiation missile resistance, small system size, low cost and the like, shows outstanding operational strategic significance, operational tactical value and practical application prospect, and attracts a great deal of attention of researchers.
According to the number of selected opportunity sources and the number of receiving base stations, passive radars can be divided into single-source single-receiving base station passive radars, single-source multi-receiving base station passive radars and multi-source multi-receiving base station passive radars. The primary task of the radar is to detect an interested target in a noise background, and only with such a function, the radar can provide effective information such as a target azimuth, a distance, a motion track and the like for an operator.
At present, passive radar target detection algorithms can be roughly classified into three categories, (1) energy-based detection algorithms which are long-standing and easy to realize; (2) the detection algorithm based on the correlation aims at judging whether a target echo exists or not by utilizing the correlation among data acquired by each base station; (3) a detection algorithm based on a Generalized Likelihood Ratio (GLRT); the essence of the detection method is that the maximization of the likelihood ratio is searched in an unknown parameter interval, namely, the maximum likelihood estimation of the unknown parameters is used for replacing the unknown parameters, further GLRT test statistics is obtained, and the problem is converted into the statistical test of a definite signal.
With the continuous improvement of passive radar, the basic problem of target detection becomes increasingly complex, and the detection algorithm is more demanding by various signal types and different configurations of receiving base stations.
The multi-base station passive radar is used as a distributed system, wherein one type of signal processing structure is to directly upload original observation data received by each base station to a fusion center for processing, so that a data link with a large bandwidth is needed to transmit the original signals of each base station. In order to avoid the limitation of transmission bandwidth required by signal uploading, a feasible method is to respectively allocate a reference channel at each receiving base station, wherein the reference channel mainly receives radiation signals from an external radiation source, each base station respectively processes observation data of two channels to obtain corresponding local test statistics, then each base station uploads the local test statistics obtained by processing to a fusion center, and the center gives global judgment.
Intuitively, the receiving base station with better detection performance should allocate a larger weight to increase its contribution to the global decision. However, since the relationship between the system detection performance and the weight coefficient cannot be expressed by an explicit mathematical expression in general, the assignment of the weight coefficient becomes complicated.
Disclosure of Invention
The invention overcomes the defects of the prior art, provides the passive multi-base-station radar target detection method based on linear fusion, has low calculation complexity and strong real-time performance, and compared with the traditional target detection method with equal weight coefficients of all base stations, the detection performance of the target detection method provided by the invention is obviously improved.
In order to achieve the purpose, the invention is realized by the following technical scheme:
the passive multi-base-station radar target detection method based on linear fusion comprises the following steps:
s1: determining received signal models of a monitoring channel and a reference channel, and converting the signal received by the reference channel of the kth base station
Figure BDA0003163405060000021
Expressed as:
Figure BDA0003163405060000022
wherein b iskRepresents the fading coefficient from the external radiation source to the reference channel, s (n) is the emission waveform of the external radiation source,
Figure BDA0003163405060000023
is the mean value in the reference channel is 0 and the variance is
Figure BDA0003163405060000024
The cyclic symmetric complex gaussian noise of (1); and establishing a binary hypothesis testing model for radar target detection on the basis of the signal model.
S2: performing cross-correlation operation on the reference channel and the monitoring channel signal of each base station to obtain local statistic of each base station
Figure BDA0003163405060000025
Wherein, represents to take the conjugation to the signal, N is the number of sampling points of the signal of the receiving base station; then, all the local statistics t are calculatedkAnd sending the data to a fusion center for fusion, and finally obtaining the global statistic of radar target detection as follows:
Figure BDA0003163405060000026
wherein w ═ w1，w2，w3，....wK]TDenotes a weight coefficient vector assigned to each base station, T ═ T1，t2，t3，...tK]TThe vector is a vector formed by local test statistics of each base station.
S3: determining the detection performance of the base station, and measuring the performance of the base station through three parameters of SNR-R, SNR-S and INR-S; establishing a linear combination expression of three parameters of SNR-R, SNR-S and INR-S, and characterizing the performance index q of the base station by using the linear combination expression.
S4: determining a weight coefficient vector of a receiving base station according to
Figure BDA0003163405060000031
Thus, the value of the global statistic is calculated, and the global statistic is compared with the threshold value to complete the detection of the target.
Further, for step S1, when considering the residual of the direct wave in the monitoring channel, the signal received by the kth monitoring channel
Figure BDA0003163405060000032
Expressed as:
Figure BDA0003163405060000033
wherein a iskRepresenting the intensity of reflection of the signal by the target, ckRepresenting the strength, tau, of the direct-wave interference signalkFor time delay of target echo signal, omegakFor doppler shifts due to the motion characteristics of the target,
Figure BDA0003163405060000034
for monitoring the mean value in the channel as 0 and the variance as
Figure BDA0003163405060000035
Is circularly symmetric to complex gaussian noise.
Further, for step S1, a binary hypothesis testing model for radar target detection is established based on the signal model, specifically, hypothesis H0Indicating that the monitoring channel does not contain the target echo; and assume H1Indicating that the monitoring channel contains a target echo; the hypothesis testing model for radar target detection is constructed as follows:
Figure BDA0003163405060000036
further, for step S3, the linear combination expression is:
q＝αSNR-R+βSNR-S+γINR-S
determining the coefficients alpha, beta, gamma of the individual parameters by means of an entropy weight method and calculating therefrom a performance index q for the individual base stationsi＝αSNR-Ri+βSNR-Si+γINR-Si。
Compared with the prior art, the invention has the following beneficial effects:
compared with the traditional passive radar target detection method, the radar detection method uploads the local statistic processed by each base station, so that the bandwidth required by signal transmission is reduced; meanwhile, the weight coefficients of all base stations are optimally distributed through an entropy weight method, so that the contribution of a receiving base station with better performance to global judgment is increased, and the performance of the detection algorithm is greatly improved under the condition that the calculation complexity is not greatly improved.
Drawings
Fig. 1 is a schematic structural diagram of the multi-base station passive radar of the present invention.
Fig. 2 is a flow chart of the multi-base station passive radar target detection method of the present invention.
Fig. 3 is a diagram illustrating the results of computer simulation of the variation of detection probability with false alarm probability under the first set of base station parameter settings according to the embodiment.
Fig. 4 is a diagram illustrating the results of computer simulation of the detection probability as a function of the false alarm probability under the second set of base station parameters according to the embodiment. In fig. 3 and 4, the solid line represents the average value of the detection probability of the conventional target detection method; the dotted line represents the average of the detection probability of the target detection method proposed by the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail with reference to the embodiments and the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. The technical solution of the present invention is described in detail below with reference to the embodiments and the drawings, but the scope of protection is not limited thereto.
The structure diagram of the system of the multi-base station passive radar is shown in fig. 1, the system is composed of K receiving base stations and a non-cooperative opportunistic radiation source, and each receiving base station is provided with a reference channel and a monitoring channel which are respectively used for receiving direct waves radiated by the opportunistic source and target echoes.
The detection decision process is briefly summarized here using a flow chart, as shown in fig. 2, and a detailed description of the detection algorithm follows.
Example (b):
step 1, receiving signal models of two channels are determined. The reference channel collects direct wave signals radiated by the opportunity source, and the signal received by the reference channel of the kth receiving base station
Figure BDA0003163405060000041
Can be expressed as:
Figure BDA0003163405060000042
wherein b iskRepresenting the channel coefficients from the opportunistic source to the reference channel, s (n) is the waveform of the opportunistic source radiation,
Figure BDA0003163405060000043
mean of 0 and variance of
Figure BDA0003163405060000044
Is circularly symmetric to complex gaussian noise.
Considering the residual of the direct wave in the monitoring channel, the monitoring channel of the kth receiving base station receives the signal
Figure BDA0003163405060000045
Can be expressed as:
Figure BDA0003163405060000046
wherein a iskRepresenting the intensity of reflection of the signal by the target, ckRepresenting the intensity of the residual direct-wave interference signal, s (n) being the direct-wave residual originating from the radiation of the opportunistic source, τkFor time delay of target echo signal, omegakFor doppler shifts due to motion of the target,
Figure BDA0003163405060000047
mean of 0 and variance of
Figure BDA0003163405060000048
The cyclic distribution of complex gaussian noise.
In summary, the radar target detection problem can be expressed as a binary hypothesis test as shown in the following formula, where H0The assumption shows that the monitoring channel does not contain target echo, namely, no target exists; suppose H1Indicating that the detection channel contains the target echo, i.e. the target is present.
Figure BDA0003163405060000051
Step 2: performing cross-correlation operation on the reference channel and the monitoring channel signal of each base station to obtain local statistics of each base station:
Figure BDA0003163405060000052
where, denotes the conjugate of the signal, and N is the number of signal sampling points of the receiving base station. Then we will have the full local statistic tkSending the data to a fusion center to fuse the data, and obtaining the global test statistic as follows:
Figure BDA0003163405060000053
wherein w ═ w1，w2，w3，....wK]TDenotes a weight coefficient vector assigned to each base station, T ═ T1，t2，t3，...tK]TThe vector formed by the local test statistics of each receiving base station is shown, and it can be seen that the global test statistics are linear combinations of the local statistics of each receiving base station. Because the detection performances of the base stations are different, a larger weight can be allocated to the receiving base station with better performance, so that the contribution of the receiving base station to the global detection is improved.
And step 3: the weight coefficients of each receiving base station are determined. The detection performance of the base station is related to many parameters, and in the case of considering the direct wave residual, we consider the following three main factors: the reference channel signal-to-noise ratio, the monitoring channel signal-to-noise ratio and the monitoring channel interference-to-noise ratio are respectively expressed by SNR-R, SNR-S and INR-S. Establishing a linear combination expression of three variables of SNR-R, SNR-S and INR-S, and using the index to characterize the performance of the base station, namely:
q＝αSNR-R+βSNR-S+γINR-S
the coefficients α, β, γ for each parameter are determined using entropy weighting, and the specific steps for determining the coefficients using entropy weighting are as follows:
(1) each index was normalized:
suppose the SNR-R, SNR-S, INR-S of the kth base station is denoted as [ SNR-R ]k,SNR-Sk,INR-Sk]The vectors formed by SNR-R, SNR-S and INR-S of all base stations are respectively recorded as:
y1＝[SNR-R1,SNR-R2,...SNR-Rk,...SNR-RK]T
y2＝[SNR-S1,SNR-S2,...SNR-Sk,...SNR-SK]T
y3＝[INR-S1,INR-S2,...INR-Sk,...INR-SK]T
the index vectors are spliced into a matrix Y ═ Y1,y2,y3]
And standard forward converting two index vectors of SNR-R and SNR-S of the base station according to the following formula:
Figure BDA0003163405060000061
the INR-S index vector is normalized and inverted according to the following formula:
Figure BDA0003163405060000062
(2) calculating the probability:
Figure BDA0003163405060000063
(3) computing
Figure BDA0003163405060000064
Obtaining the information entropy (if p) of each indexkj＝0pijWhen 0, then
Figure BDA0003163405060000065
)
(4) Calculating the difference coefficient of each index: dj＝1-ej
(5) Calculating coefficients of the indexes:
Figure BDA0003163405060000066
(6) obtaining coefficient vectors [ alpha, beta, gamma ] of each parameter]＝[v1,v2,v3]
(7) Calculating the performance index q of each base stationk＝α·SNR-Rk+β·SNR-Sk+γ·INR-Sk。
And 4, step 4: according to the performance indexes of the base stations obtained above, determining a weight coefficient vector w of the receiving base station according to the following formula
Figure BDA0003163405060000067
I.e. w ═ w1,w2,...,wK]TThereby determining the global statistic T.
(8) And after the global statistic is determined, determining a threshold by using a Monte Carlo method, and comparing the statistic with the threshold to finish the judgment of the existence of the target. The monte carlo method is a technique known to those skilled in the art, and can be referred to as "statistical signal processing basis" -estimation and detection theory (Steven m. The passive multi-base-station radar target detection method based on linear fusion in the embodiment of the invention is finished.
Verification example:
the invention result of the patent is verified and explained by MATLAB simulation experiment. The computer simulation diagram is shown in fig. 3 and 4.
1. Setting simulation parameters:
in the simulation, the number of base stations K is assumed to be 3, and the number of sampling points N is assumed to be 200. Noise variance of reference channel
Figure BDA0003163405060000071
Monitoring noise variance of channel
Figure BDA0003163405060000072
The transmitted signal s obeys a mean value u of 0 and a variance σ2Complex gaussian distribution of 1. Doppler shift of each receiving base station is [0.20 pi, 0.06 pi, 0.08 pi ]]Time delay τ ═ 10, 15, 20]In fig. 3, the parameters of each base station are SNR-R ═ 1, -0.5, 0.5]，SNR-S＝[-12，-25，-20],INR-S＝[0，1，-1](ii) a In fig. 4, the parameters of each base station are SNR-R ═ 1, 0.5, -0.5]，SNR-S＝[-12，-25，-20],INR-S＝[-1，0，1]。
2. Emulated content
Experiment one:
the parameters of each base station are SNR-R ═ 1, -0.5, 0.5], SNR-S [ -12, 25, -20], and INR-S [ -0, 1, -1], respectively. The traditional detection algorithm (the weights of all receiving base stations are equal) and the detection algorithm in the invention are respectively adopted to carry out 100000 Monte Carlo experiments, the detection probabilities under different false alarm probabilities are counted, and a curve graph of the detection probability changing along with the false alarm probability is drawn.
The experimental results are shown in FIG. 3, and the abscissa represents the false alarm Rate (PFA) with a variation range of 10-4-10-1The ordinate is the detection Probability (PD), in the legend, "New" represents the target detection algorithm for weight optimization distribution proposed by the present invention, and "Average" represents the traditional detector algorithm for weight Average distribution.
Experiment two:
the parameters of each base station are SNR-R [ -1, 0.5, -0.5], SNR-S [ -12, -25, -20], and INR-S [ -1, 0, 1], respectively. The traditional detection algorithm (the weights of all receiving base stations are equal) and the detection algorithm in the invention are respectively adopted to carry out 100000 Monte Carlo experiments, the detection probabilities under different false alarm probabilities are counted, and a curve graph of the detection probability changing along with the false alarm probability is drawn.
The experimental results are shown in FIG. 4, and the abscissa represents the false alarm Rate (PFA) with a variation range of 10-4-10-1The ordinate is the detection Probability (PD), in the legend, "New" represents the target detection algorithm for weight optimization distribution proposed by the present invention, and "Average" represents the traditional detector algorithm for weight Average distribution.
3. Analysis of simulation results
As can be seen from fig. 3, the detection probability of the target detection algorithm proposed by the present invention is higher than that of the conventional target detection algorithm. In fig. 4, the detection probability of the target detection algorithm proposed by the present invention is also higher than that of the conventional target detection algorithm. The above conclusions can fully illustrate that the method provided by the patent has certain effectiveness and practicability.
While the invention has been described in further detail with reference to specific preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (4)

1. The passive multi-base-station radar target detection method based on linear fusion is characterized by comprising the following steps of:
s1: determining received signal models of a monitoring channel and a reference channel, and converting the signal received by the k reference channel
Figure FDA0003163405050000011
Expressed as:
Figure FDA0003163405050000012
wherein b iskRepresents the fading coefficient from the external radiation source to the reference channel, s (n) is the emission waveform of the external radiation source,
Figure FDA0003163405050000013
is the mean value in the reference channel is 0 and the variance is
Figure FDA0003163405050000014
The cyclic symmetric complex gaussian noise of (1); establishing a binary hypothesis test model for radar target detection on the basis of the signal model;
s2: performing cross-correlation operation on the reference channel and the monitoring channel signals of each base station to obtain local test statistic of each base station
Figure FDA0003163405050000015
Wherein, represents to take the conjugation to the signal, N is the number of sampling points of the signal of the receiving base station; then, all the local statistics t are calculatedkAnd sending the data to a fusion center for fusion, and finally obtaining the global statistic of radar target detection as follows:
Figure FDA0003163405050000016
wherein w ═ w1，w2，w3，....wK]TDenotes a weight coefficient vector assigned to each base station, T ═ T1，t2，t3，...tk]TA vector composed of local test statistics of each base station is represented;
s3: determining the detection performance of each base station, and measuring the performance of the base station through three indexes of SNR-R, SNR-S and INR-S; establishing a linear combination expression of three variables of SNR-R, SNR-S and INR-S, and representing the performance index q of each base station by using the linear combination expression;
s4: determining a weight coefficient vector of a receiving base station according to
Figure FDA0003163405050000017
Thus, the value of the global statistic is calculated, and the global statistic is compared with the threshold value to complete the detection of the target.
2. The method for passive multi-base-station radar target detection based on linear fusion of claim 1, wherein for step S1, when considering the residual of the direct wave in the monitoring channel, the signal received by the k-th monitoring channel
Figure FDA0003163405050000018
Expressed as:
Figure FDA0003163405050000019
wherein a iskRepresenting the intensity of reflection of the signal by the target, ckRepresenting the strength, tau, of the direct-wave interference signalkFor time delay of target echo signal, omegakFor doppler shifts due to the motion characteristics of the target,
Figure FDA0003163405050000021
for monitoring the mean value in the channel as 0 and the variance as
Figure FDA0003163405050000022
Is circularly symmetric to complex gaussian noise.
3. The method for passive multi-base-station radar target detection based on linear fusion as claimed in claim 1, wherein for step S1, a binary hypothesis testing model for radar target detection is established based on the signal model, specifically, hypothesis H0Indicating that the monitoring channel does not contain the target echo; and assume H1Indicating that the monitoring channel contains a target echo; the hypothesis testing model for radar target detection is constructed as follows:
Figure FDA0003163405050000023
。
4. the method for passive multi-base-station radar target detection based on linear fusion of claim 1, wherein for step S3, the linear combination expression is:
q＝αSNR-R+βSNR-S+γINR-S
determining coefficients alpha, beta, gamma of each index by an entropy weight method and calculating performance index q of each base station according to the coefficients alpha, beta, gammai＝αSNR-Ri+βSNR-Si+γINR-Si。
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CN118534431A (en) * 2024-07-24 2024-08-23 烟台北方星空自控科技有限公司 Improved algorithm and device for Doppler frequency shift of multi-target radar signal

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