A radar distributed target detection method and system for fixed interference direction vector mismatch

Abstract

The application belongs to the field of radar target detection, and discloses a radar distributed target detection method and system when a fixed interference direction vector is mismatched. In order to solve the problem that the prior art cannot guarantee reliable detection performance when the fixed interference direction vector is mismatched, the application provides a radar distributed target detection method when the fixed interference direction vector is mismatched. First, the noise covariance matrix is estimated based on auxiliary data. Then, the amplitude vector of the fixed interference and the direction vector coordinate vector, the target coordinate vector, and the noise power mismatch amount are estimated based on the data to be measured. Further, the detection statistic is calculated based on the generalized likelihood ratio criterion, and the detection threshold is determined according to the false alarm probability. Finally, the size of the detection statistic and the detection threshold is compared. If the former is larger, the target exists; if the latter is larger, the target does not exist. The application can guarantee reliable detection performance when the fixed interference direction vector is mismatched.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of radar target detection, and particularly relates to a radar distributed target detection method and system in the case of mismatching of a fixed interference direction vector. BACKGROUND

[0002] As one of the basic functions of radar, adaptive target detection has been widely concerned and researched. When a radar detects a target of interest, it is also affected by noise, clutter and interference. The target of interest may occupy multiple range cells under the observation of a high-resolution radar, and its echo can be described by a distributed model. Noise and clutter can be modeled as zero-mean Gaussian color noise, and the covariance matrix of the color noise can be calculated by obtaining auxiliary data containing only independent and identically distributed noise from adjacent range resolution cells. Interference may come from a jammer in a fixed direction, which may disturb the correct detection of the target of interest by the radar after entering the radar receiver. The position of the jammer can be known in advance through a series of interference identification techniques, so the current research assumes that the direction information of the fixed interference is known in advance.

[0003] For the radar distributed target detection problem with the direction information of the fixed interference known in advance, Francesco Bandiera et al. designed a detector based on the Generalized Likelihood Ratio Test (GLRT) in 2007, and Liu Weijian et al. designed detectors based on the Rao criterion and the Wald criterion in 2015 and 2017, respectively. These detectors have different performances in different scenarios: the Rao detector performs better when the sum of the dimensions of the signal subspace and the interference subspace is close to the dimension of the data model; the Wald detector performs better when the data model dimension is large and the signal subspace dimension is greater than the interference subspace dimension; and the GLRT detector performs better in other cases.

[0004] The existing detectors ensure reliable detection performance when the direction information of the fixed interference is accurately known. However, in increasingly complex real environments, affected by factors such as array calibration error and interference identification error, the direction vector of the fixed interference is often mismatched, i.e. the assumed direction vector deviates from the real direction vector, and the existing detectors cannot guarantee the reliability of the detection results. Therefore, it is an urgent problem to establish a reasonable and accurate target detection model and design a reliable and effective radar distributed target detector for the case of mismatching of the direction vector of the fixed interference.

[0005] In view of the above analysis, the existing technical problem to be solved in the prior art is how to perform reliable and effective target detection in the case of mismatching of the direction vector of the fixed interference. SUMMARY

[0006] In view of the problems existing in the prior art, the present application provides a radar distributed target detection method and system when a fixed interference steering vector is mismatched.

[0007] The present application is implemented in a radar distributed target detection method when a fixed interference steering vector is mismatched, characterized in that the radar distributed target detection method when a fixed interference steering vector is mismatched comprises the following steps.

[0008] S1: acquiring measured data of a radar detection system with M channels in K to-be-detected units The target signal of interest belongs to a column full-rank matrix The signal subspace spanned by the column full-rank matrix, and the coordinate matrix of the target signal in the signal subspace is The fixed interference steering vector exists mismatch but belongs to a column full-rank matrix The interference subspace spanned by the column full-rank matrix, and the coordinate vector of the fixed interference steering vector in the interference subspace is The conjugate amplitude vector of the fixed interference is Wherein P is the dimension of the signal subspace, Q is the dimension of the interference subspace, and the channel number M of the radar detection system can also be referred to as the dimension of the data model, represents a complex matrix with a dimension of m x n;

[0009] S2: acquiring auxiliary data of the radar detection system with M channels in L adjacent units of the to-be-detected unit

[0010] S3: using the auxiliary data X L to calculate the estimation value of the noise covariance matrix

[0011] S4: under the no-target hypothesis H0, the expression of the probability density function f0(X) of the measured data X is given according to the estimation value S of the noise covariance matrix R, and the estimation value of the coordinate vector of the fixed interference steering vector in the interference subspace spanned by the matrix J in the measured data is solved according to f0(X) R＝S and the interference subspace matrix J R＝S The estimation value of the fixed interference conjugate amplitude vector in the measured data is solved The estimation value of the power mismatch amount γ of the Gaussian color noise in the measured data and the auxiliary data is solved

[0012] S5: under the target hypothesis H1, the probability density function f1(X) of the measured data X is given according to the estimation value S of the noise covariance matrix R R＝S ​​​​the expression of f1(X) and substituting the estimated value of the fixed interference steering vector in the interference subspace spanned by matrix J into the expression of f1(X) R＝S , the signal subspace matrix H and the interference subspace matrix J, to solve the coordinate vector of the fixed interference steering vector in the interference subspace spanned by matrix J in the data under test the estimated value of to solve the estimated value of the fixed interference conjugate amplitude vector in the data under test the estimated value of to solve the estimated value of the coordinate matrix P of the target signal in the signal subspace the estimated value of to solve the estimated value of the power mismatch amount γ of the Gaussian color noise in the data under test and the auxiliary data

[0013] S6: Substitute the estimated value of the coordinate vector q of the fixed interference steering vector in the interference subspace spanned by matrix J in the data under test the estimated value of the fixed interference conjugate amplitude vector in the data under test the estimated value of the power mismatch amount γ of the Gaussian color noise in the data under test and the auxiliary data into the probability density function f0(X) of the data under test X R＝S , to obtain the maximum likelihood probability density function max q，β，γ f0(X) of the data under test X under the null hypothesis H0 RR＝S

[0014] S7: Substitute the estimated value of the coordinate vector q of the fixed interference steering vector in the interference subspace spanned by matrix J in the data under test the estimated value of the fixed interference conjugate amplitude vector in the data under test the estimated value of the coordinate matrix P of the target signal in the signal subspace the estimated value of the power mismatch amount γ of the Gaussian color noise in the data under test and the auxiliary data into the probability density function f1(X) of the data under test X R＝S , to obtain the maximum likelihood probability density function max q,β,P,γ f1(X) of the data under test X under the alternative hypothesis H1 R＝S ;

[0015] S8: According to the maximum likelihood probability density function max q,β,γ f0(X) of the data under test X under the null hypothesis H0 and the maximum likelihood probability density function max R＝S f1(X) of the data under test X under the alternative hypothesis H1, calculate the detection statistic t based on the generalized likelihood ratio criterion q,β,P,γ R＝S ;

[0016] S9: Determine the detection threshold T according to the false alarm probability P fa ;​

[0017] S10: judging whether the target signal of interest exists in the to-be-tested data X according to the size relationship between the detection statistic t based on the generalized likelihood ratio criterion and the detection threshold T.

[0018] Further, the step S3 utilizes the auxiliary data X L The estimation value S of the noise covariance matrix R is calculated as follows: Wherein (·) H represents the conjugate transpose.

[0019] Further, the step S4 gives the probability density function f0(X) of the to-be-tested data X under the null hypothesis H0 according to the estimation value S of the noise covariance matrix R R＝S as follows: Wherein exp(·) represents the natural exponential function, tr(·) represents the matrix trace, |·| represents the absolute value of a scalar or the determinant of a matrix, X S =S -1 / 2 X, J S =S -1 / 2 J, (·) -1 / 2 represents the matrix square root inverse.

[0020] Further, the step S4 gives the estimation value q of the coordinate vector of the fixed interference steering vector in the interference subspace spanned by the matrix J in the to-be-tested data as follows: is the eigenvector corresponding to the maximum eigenvalue of the matrix , wherein (·) -1 represents the inverse of the reversible matrix.

[0021] Further, the step S4 gives the estimation value β of the fixed interference conjugate amplitude vector in the to-be-tested data as follows:

[0022] Further, the step S4 gives the estimation value γ of the power mismatch amount of the Gaussian color noise in the to-be-tested data and the auxiliary data as follows:

[0023] Further, the step S5 gives the probability density function f1(X) of the to-be-tested data X under the alternative hypothesis H1 R＝S as follows: Wherein H S =S -1 / 2 H.

[0024] Further, the step S5 gives the estimation value q of the coordinate vector of the fixed interference steering vector in the interference subspace spanned by the matrix J in the to-be-tested data as follows: is the matrix the eigenvector corresponding to the largest eigenvalue of I M denotes an identity matrix of dimension M.

[0025] Further, the estimated value of the conjugate amplitude vector β of the fixed interference in the data under test in step S5 is Further, the estimated value of the conjugate amplitude vector β of the fixed interference in the data under test in step S5 is

[0026] Further, the estimated value of the coordinate matrix P of the target signal in the signal subspace in step S5 is Further, the estimated value of the coordinate matrix P of the target signal in the signal subspace in step S5 is

[0027] Further, the estimated value of the power mismatch γ of the Gaussian color noise in the data under test and the auxiliary data in step S5 is Further, the estimated value of the power mismatch γ of the Gaussian color noise in the data under test and the auxiliary data in step S5 is

[0028] Further, the maximum likelihood probability density function max q,β,γ f0(X)| R＝S of the data under test X under the null hypothesis H0 in step S6 is

[0029] Further, the maximum likelihood probability density function max q,β,P,γ f1(X)| R＝S of the data under test X under the alternative hypothesis H1 in step S7 is

[0030] Further, the detection statistic t based on the generalized likelihood ratio criterion in step S8 is

[0031] Further, the specific steps for determining the detection threshold T according to the false alarm probability P fa in step S9 are as follows:

[0032] S91: Calculate the detection statistic t based on the generalized likelihood ratio criterion based on the null signal test data;

[0033] S92: Perform M c times Monte Carlo simulation on step S91, and store the calculated detection statistic t based on the generalized likelihood ratio criterion in column vector V , where denotes a real matrix of dimension m x n;

[0034] S93: Rearrange the elements in vector V in ascending order, and the first element in vector V is the detection threshold T corresponding to the false alarm probability P fa , where denotes rounding down.

[0035] Further, the specific process of judging whether the target signal of interest exists in the to-be-tested data X by comparing the size relationship between the detection statistic t based on the generalized likelihood ratio criterion and the detection threshold T in the step S10 is as follows: if t>T, the target signal of interest exists; if t≤T, the target signal of interest does not exist.

[0036] Another object of the present application is to provide a radar distributed target detection system for realizing the radar distributed target detection method when the fixed interference steering vector is mismatched.

[0037] The test data acquisition module is used for acquiring the to-be-tested data from the to-be-tested unit.

[0038] The auxiliary data acquisition module is used for acquiring the auxiliary data from the adjacent unit.

[0039] The target-free test data acquisition module is used for acquiring the to-be-tested data from the to-be-tested unit when there is no target, so as to calculate the detection threshold.

[0040] The parameter acquisition module is used for acquiring the signal subspace column full rank matrix, the interference subspace column full rank matrix and the false alarm probability.

[0041] The covariance matrix estimation module is used for estimating the noise covariance matrix by using the auxiliary data.

[0042] The parameter estimation module is used for calculating unknown parameters by using the estimated noise covariance matrix, the signal subspace column full rank matrix, the interference subspace column full rank matrix and the to-be-tested data, wherein the unknown parameters include the coordinate vector of the fixed interference steering vector in the interference subspace, the fixed interference amplitude vector, the power mismatch amount of the Gaussian color noise in the to-be-tested data and the auxiliary data, and the coordinate matrix of the target signal in the signal subspace under the target assumption.

[0043] The maximum likelihood probability density function calculation module is used for calculating the maximum likelihood probability density function of the to-be-tested data under the target-free assumption and the target assumption by using the output result of the parameter estimation module.

[0044] The detection statistic calculation module is used for calculating the detection statistic based on the generalized likelihood ratio criterion by using the output result of the maximum likelihood probability density function calculation module.

[0045] The detection threshold calculation module is used for calculating the detection threshold according to the target-free test data and the false alarm probability.

[0046] The target detection output module is used for comparing the size relationship between the detection statistic and the detection threshold, and outputting the judgment result of whether the target signal of interest exists in the to-be-tested data.

[0047] Another object of the present application is to provide a computer device comprising a memory and a processor, the memory storing a computer program, the computer program being executed by the processor to make the processor perform the steps of the radar distributed target detection method when the fixed interference steering vector is mismatched.

[0048] Another object of the present application is to provide a computer readable storage medium storing a computer program, the computer program being executed by the processor to make the processor perform the steps of the radar distributed target detection method when the fixed interference steering vector is mismatched.

[0049] Another object of the present application is to provide an information data processing terminal comprising the radar distributed target detection system when the fixed interference steering vector is mismatched.

[0050] In combination with the above technical solutions and the solved technical problems, the technical solutions to be protected by the present application have the following advantages and positive effects:

[0051] Firstly, the present application introduces a subspace model to describe the steering vector of the fixed interference, which covers the possible deviation of the steering vector of the fixed interference, ensuring the reliability of the mathematical model of the detection problem.

[0052] The present application can achieve more reliable and optimal distributed target detection performance in the case of mismatched fixed interference steering vector. The present application can still work when the sum of the dimensions of the signal subspace and the interference subspace is close to or equal to the dimension of the data model, and achieve effective and reliable distributed target detection performance.

[0053] Secondly, the technical solutions of the present application fill the technical gap in the industry at home and abroad: the existing technical solutions all assume that the fixed interference steering information is accurately known, and ensure reliable detection performance when the fixed interference steering information is completely consistent with the real fixed interference steering information. The present application fully considers the problems of array calibration error, interference recognition error and other problems commonly existing in complex environments, describes the fixed interference steering vector that may be mismatched by a subspace model, effectively solves the radar distributed target detection problem when the fixed interference steering vector is mismatched, and eliminates the risk of unreliable target detection results caused by the fixed interference steering vector.

[0054] The technical solution of the present application solves the technical problem that people have been eager to solve but have always failed to succeed: the prior art solutions all have application blind spots: when the sum of the dimensions of the signal subspace and the interference subspace is close to or even equal to the dimension of the data model, the performance of the prior art solutions deteriorates sharply or even fails. The present application covers the possible mismatch range of the fixed interference steering vector by using a subspace model, and the detection statistics of the new detector designed by the present application do not contain the part that causes the application blind spot of the existing detector. When the sum of the dimensions of the signal subspace and the interference subspace is close to or even equal to the dimension of the data model, reliable and effective detection performance can still be guaranteed. Therefore, the technical solution of the present application does not have application blind spots. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 is a radar distributed target detection method principle diagram provided by the embodiment of the present application when the fixed interference steering vector is mismatched;

[0056] Figure 2 is a radar distributed target detection system structure framework diagram provided by the embodiment of the present application when the fixed interference steering vector is mismatched;

[0057] Figure 3 is a detection probability comparison diagram of the technical solution of the present application and the prior art solution under different signal-to-noise ratios;

[0058] Figure 4 is a detection probability comparison diagram of the technical solution of the present application and the prior art solution under different jamming-to-noise ratios;

[0059] Figure 5 is a detection probability comparison diagram of the technical solution of the present application and the prior art solution under different interference subspace dimensions. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present application clearer and more apparent, the present application will be further described in detail below in combination with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0061] The radar detection system of the present application can be applied to multiple industries and fields, especially in scenarios that require high-resolution detection and anti-interference capability. Two specific industrial application embodiments are listed below:

[0062] Embodiment 1: marine radar detection system

[0063] In marine environments, radar systems are often used to detect and track maritime targets such as ships, icebergs, and buoys. However, due to the complex wave motion in the marine environment, changes in weather conditions, and the presence of sea surface reflections and interference sources (e.g., sea surface reflection waves, fixed sea surface structures, communication interference, etc.), radar systems face significant challenges in identifying and tracking maritime targets. Traditional radar detectors are susceptible to interference, particularly when the location of the interference source changes, leading to false positives and false negatives. The detection method in the present invention utilizes the deviation of the interference subspace to cover the fixed interference steering vector, significantly improving the detection performance of the system in an interference environment. The system has higher resolution and anti-interference capability in detecting maritime moving targets, and is suitable for applications such as coastal patrol and maritime traffic monitoring.

[0064] Embodiment 2: Air target monitoring and early warning system

[0065] In the field of airspace monitoring and early warning, radar systems need to monitor multiple air targets to deal with potential threats (such as drones, aircraft, missiles, etc.). In practical applications, air radar systems are often affected by various fixed interference (such as fixed buildings, interference from radio transmission stations) and environmental noise. Traditional radars have weak detection capabilities in complex environments and are easily affected by interference mismatch, thereby reducing detection accuracy. The system provided in the present invention can reduce the interference effect through interference subspace modeling method when there is a mismatch in the fixed interference steering vector, so that the radar can accurately identify target signals and improve the detection performance and target recognition rate of the system. The system can be widely applied in the fields of airport airspace safety monitoring, border air defense monitoring, military early warning systems, etc., effectively improving the reliability and accuracy of target detection.

[0066] A high-resolution radar detection system with M channels samples test data from K units to be detected wherein Xk is the test data in the kth unit to be detected, k = 1, 2, …, K, represents a complex matrix with dimensions m x n. Under the null hypothesis H0, the test data X contains Gaussian colored noise and fixed interference. The Gaussian colored noise can be represented as All column vectors are independently and identically distributed and follow a Gaussian distribution with mean zero and covariance matrix R t . The fixed interference can be represented as jβ H , where is the steering vector of the fixed interference, β = [β1, β2, …, β K ] T is the conjugate amplitude vector of the fixed interference, is the amplitude of the fixed interference in the kth unit to be detected, (·) T represents the transpose, (·)* represents conjugate. Due to the existence of radar channel calibration error, interference recognition error, etc., the steering vector j of the fixed interference exists mismatch, at this time a known column full rank matrix spanned by the interference subspace, that is, the fixed interference steering vector can be re-modeled as j = Jq, and further the fixed interference can be described as Jqβ H , wherein characterizes the coordinate vector of the fixed interference steering vector j in the interference subspace. In the target hypothesis detection H1, the target echo HP is also contained in the test data X, wherein is a known column full rank matrix of the signal subspace, is a coordinate matrix of the target signal, which characterizes the position of the target signal in the signal subspace spanned by the matrix H. In order to estimate the noise covariance matrix R in the test data t , the auxiliary data is obtained by sampling from L adjacent units of the unit to be detected The auxiliary data X L contains only Gaussian color noise , wherein represents the auxiliary data in the lth adjacent unit, l = 1, 2, …, L. All column vectors in the auxiliary data X L are independent and identically distributed and subject to Gaussian distribution with mean zero and covariance matrix R. Due to the actual environmental fluctuation, the noise covariance matrix R in the test data t = γQ, wherein γ characterizes the noise power mismatch amount between the test data and the auxiliary data. In summary, the binary hypothesis test problem of the radar distributed target under the condition of mismatch of the fixed interference steering vector can be represented as:

[0067] The purpose of the present application is to improve the detection performance of the detector for the radar distributed target under the condition of mismatch of the fixed interference steering vector.

[0068] As shown in Figure 1 , the embodiment provides a radar distributed target detection method under the condition of mismatch of the fixed interference steering vector, and the steps of the method comprise:

[0069] S1: obtaining the test data sampled by a radar detection system with channel number M in K units to be detected The target signal of interest belongs to the signal subspace spanned by the column full rank matrix , and the coordinate matrix of the target signal in the signal subspace is The steering vector of the fixed interference exists mismatch but belongs to the interference subspace spanned by the column full rank matrix , and the coordinate vector of the steering vector of the fixed interference in the interference subspace is The conjugate amplitude vector of the fixed interference is Where P represents the signal subspace dimension, Q represents the interference subspace dimension, and the number of radar detection system channels M can also be referred to as the data model dimension. This represents a complex matrix of dimension m×n;

[0070] S2: Acquire auxiliary data sampled by a radar detection system with M channels within L neighboring cells of the cell to be detected.

[0071] S3: Utilizing auxiliary data X L Calculate the noise covariance matrix The estimated value

[0072] S4: Under the objective hypothesis H0, the probability density function f0(X) of the test data X is given based on the estimated value S of the noise covariance matrix R. R＝S The expression, and according to f0(X)| R＝S And the interference subspace matrix J, solve for the coordinate vector of the fixed interference steering vector in the interference subspace spanned by matrix J in the test data. The estimated value Solve for the fixed disturbance conjugate amplitude vector in the test data The estimated value Find the estimated value of the power mismatch γ of Gaussian colored noise in the test data and auxiliary data.

[0073] S5: Under the objective hypothesis H1, the probability density function f1(X) of the test data X is given based on the estimated value S of the noise covariance matrix R. R＝S The expression, and according to f1(X)| R＝S Given the signal subspace matrix H and the interference subspace matrix J, solve for the coordinate vector of the fixed interference steering vector in the interference subspace spanned by matrix J. The estimated value Solve for the fixed disturbance conjugate amplitude vector in the test data The estimated value Solve for the coordinate matrix of the target signal in the signal subspace. The estimated value Find the estimated value of the power mismatch γ of Gaussian colored noise in the test data and auxiliary data.

[0074] S6: Estimate the coordinate vector q of the fixed interference steering vector in the interference subspace spanned by matrix J in the test data. Estimate of the fixed disturbance conjugate amplitude vector β in the test data Estimates of the power mismatch γ of Gaussian colored noise in the test data and auxiliary data Substitute the probability density function f0(X) of the data to be tested X R＝S Under the null hypothesis H0, the maximum likelihood probability density function max q,β,γ f0(X) of the data to be tested X is obtained R＝S ;

[0075] S7: Estimate the coordinate vector q of the fixed interference steering vector in the interference subspace spanned by the matrix J in the data to be tested Estimate the conjugate amplitude vector β of the fixed interference in the data to be tested Estimate the coordinate matrix P of the target signal in the signal subspace Estimate the power mismatch γ of the Gaussian color noise in the data to be tested and the auxiliary data Substitute the probability density function f1(X) of the data to be tested X R＝S Under the alternative hypothesis H1, the maximum likelihood probability density function max q,β,P,γ f1(X) of the data to be tested X is obtained R＝S ;

[0076] S8: According to the maximum likelihood probability density function max q,β,γ f0(X) of the data to be tested X under the null hypothesis H0 R＝S And the maximum likelihood probability density function max q,β,P,γ f1(X) of the data to be tested X under the alternative hypothesis H1 R＝S Calculate the detection statistic t based on the generalized likelihood ratio criterion;

[0077] S9: Determine the detection threshold T according to the false alarm probability P fa ;

[0078] S10: Compare the size relationship between the detection statistic t based on the generalized likelihood ratio criterion and the detection threshold T, and determine whether the target signal of interest exists in the data to be tested X.

[0079] In the step S3, the auxiliary data X L The estimate S of the noise covariance matrix R is calculated as: Where (·) H represents the conjugate transpose.

[0080] In the step S4, the probability density function f0(X) of the data to be tested X under the null hypothesis H0 is given according to the estimate S of the noise covariance matrix R R＝S : Where exp(·) represents the natural exponential function, tr(·) represents the matrix trace, |·| represents the absolute value of the scalar or the determinant of the matrix, X S =S -1*2 X, J S=S -1 / 2 J, (·) -1 / 2 This represents finding the inverse of the square root of a matrix.

[0081] In step S4, the estimated value of the coordinate vector q of the fixed interference steering vector in the interference subspace spanned by matrix J in the test data. for: It is a matrix The eigenvector corresponding to the largest eigenvalue, where (·) -1 This indicates finding the inverse of an invertible matrix.

[0082] In step S4, the estimated value of the fixed interference conjugate amplitude vector β in the test data. for:

[0083] The estimated value of the power mismatch γ of Gaussian colored noise in the test data and auxiliary data in step S4 for:

[0084] In step S5, the probability density function f1(X) of the test data X under the target hypothesis H1 is... R＝S for: Where H S =S -1 / 2 H.

[0085] In step S5, the estimated value of the coordinate vector q of the fixed interference steering vector in the interference subspace spanned by matrix J in the test data. for: It is a matrix The eigenvector corresponding to the largest eigenvalue, where I M This represents an identity matrix of dimension M.

[0086] In step S5, the estimated value of the fixed interference conjugate amplitude vector β in the test data. for:

[0087] The estimated value of the coordinate matrix P of the target signal in the signal subspace in step S5 for:

[0088] The estimated value of the power mismatch γ of Gaussian colored noise in the test data and auxiliary data in step S5. for:

[0089] In step S6, the maximum likelihood probability density function of the test data X under the no-target hypothesis H0 is max q,β,γ f0(X)| R＝S for:

[0090] In step S7, the maximum likelihood probability density function max of the test data X under target hypothesis H1 is obtained. q,β,P,γ f1(X)| R＝S for:

[0091] The detection statistic t based on the generalized likelihood ratio criterion in step S8 is:

[0092] In step S9, the false alarm probability P is calculated. fa The specific steps for determining the detection threshold T are as follows:

[0093] S91: Calculate the detection statistic t based on the generalized likelihood ratio criterion using targetless signal test data;

[0094] S92: Perform M on step S91 c The Monte Carlo simulation stores the calculated detection statistic t based on the generalized likelihood ratio criterion in a column vector. Among them Represents a real matrix of dimension m×n;

[0095] S93: Rearrange the elements of vector V in ascending order, and the nth element in vector V... Each element represents the false alarm probability P. fa The corresponding detection threshold T, where This indicates rounding down to the nearest integer.

[0096] Furthermore, in step S10, the specific process of comparing the detection statistic t based on the generalized likelihood ratio criterion with the detection threshold T to determine whether there is a target signal of interest in the data to be tested X is as follows: if t>T, then the target signal of interest exists; if t≤T, then the target signal of interest does not exist.

[0097] like Figure 2 As shown, the present invention provides a radar distributed target detection system for fixed interference steering vector mismatch, comprising:

[0098] Test data acquisition module: used to acquire test data from the unit to be tested;

[0099] Auxiliary data acquisition module: used to acquire auxiliary data from neighboring units;

[0100] Targetless signal test data acquisition module: used to acquire test data from the unit to be detected when there is no target, in order to calculate the detection threshold;

[0101] Parameter acquisition module: used for acquiring signal subspace column full rank matrix, interference subspace column full rank matrix and false alarm probability;

[0102] Covariance matrix estimation module: used for estimating noise covariance matrix by using auxiliary data;

[0103] Parameter estimation module: used for calculating unknown parameters by using estimated noise covariance matrix, signal subspace column full rank matrix, interference subspace column full rank matrix and to-be-tested data, wherein the unknown parameters include fixed interference steering vector coordinate vector in interference subspace, fixed interference amplitude vector, power mismatch amount of Gaussian color noise in to-be-tested data and auxiliary data, and target signal coordinate matrix in signal subspace under target hypothesis;

[0104] Maximum likelihood probability density function calculation module: used for calculating maximum likelihood probability density function of to-be-tested data under no-target hypothesis and target hypothesis by using output result of parameter estimation module;

[0105] Detection statistic calculation module: used for calculating detection statistic based on generalized likelihood ratio criterion by using output result of maximum likelihood probability density function calculation module;

[0106] Detection threshold calculation module: used for calculating detection threshold according to no-target signal test data and false alarm probability;

[0107] Target detection output module: used for comparing size of detection statistic and detection threshold, and outputting judgment result of whether target signal of interest exists in to-be-tested data.

[0108] The radar distributed target detection system provided by the application under fixed interference steering vector mismatch firstly acquires to-be-tested data through a test data acquisition module, acquires auxiliary data through an auxiliary data acquisition module, and acquires to-be-tested data under no-target through a no-target signal test data acquisition module, which is used for subsequent target detection processing.

[0109] Secondly, signal subspace column full rank matrix, interference subspace column full rank matrix and false alarm probability are obtained from a parameter acquisition module, so as to ensure that statistical confidence of detection meets design requirement and improve detection precision of the system.

[0110] Then, an estimation value of noise covariance matrix is obtained from a covariance matrix estimation module. Output results of the test data acquisition module, the covariance matrix estimation module and the parameter acquisition module are input into a parameter estimation module, and a plurality of key parameters are estimated in sequence: fixed interference steering vector coordinate vector in interference subspace, fixed interference amplitude vector, power mismatch amount of Gaussian color noise in to-be-tested data and auxiliary data, and target signal coordinate matrix in signal subspace under target hypothesis.

[0111] Then, the maximum likelihood probability density function calculation module uses the output results of the parameter estimation module and the covariance matrix estimation module to calculate the maximum likelihood probability density function of the to-be-tested data under the no-target hypothesis and the target hypothesis respectively, and calculates the detection statistic under the generalized likelihood ratio criterion through the detection statistic calculation module.

[0112] Finally, the detection threshold calculation module calculates the detection threshold according to the no-target signal test data and the preset false alarm probability, and compares it with the detection statistic. The target detection output module judges whether the target signal of interest exists in the to-be-tested data according to the comparison result, and outputs the final detection decision. This result provides the radar system with reliable distributed target detection capability, especially in the case of mismatch of fixed interference steering vector.

[0113] The application can be applied to all target detection fields where the fixed interference steering information part is known, such as

[0114] ① Military field: there may be jammer in the beam illumination area of military detection radar, and when the prior information of the jammer is not accurately known, the technical solution of the application can be used to model the fixed interference, and then the target can be effectively detected.

[0115] ② Civil aviation field: when the airport radar detects the aircraft in the take-off and landing stage and performs air traffic control and other tasks, there may be interference of fixed buildings and other objects in the radar illumination range, and the use of the technical solution of the application can effectively reduce the influence of interference.

[0116] ③ Traffic field: when the vehicle-mounted radar illuminates the distant interference target (building, tree, etc.) in the normal motion process of the vehicle, there is a micro-Doppler effect, which causes the positioning of the vehicle-mounted radar to the interference target to be jittered, and then affects the detection of the vehicle-mounted radar to the target of interest such as the nearby vehicle and the crowd. By using the technical solution of the application, the jitter range of the interference target steering information is covered by the subspace model, which can effectively improve the detection performance of the vehicle-mounted radar to the nearby target.

[0117] ④ Medical field: when detecting abnormal tissues such as tumors and nodules in the human body in medical activities, the interference of normal tissue reflection signals may occur, and since the human body and the detection instrument cannot be absolutely stationary, the steering information of the interference signal reflected by the normal tissue is jittered in a small range, which is exactly the application scenario of the technical solution of the application.

[0118] In summary, the target detection technology involved in the application plays an important role in many fields, and the application field of the technology will continue to expand and deepen.

[0119] The detection processes of the prior art and the technology of the present application both involve complex matrix operations, and the advantages and disadvantages of each technology cannot be analyzed theoretically. At this time, the complex detection process can be simulated by using independent repeated Monte Carlo simulation to evaluate the detection probability of the prior art and the technology of the present application under the same environment. Monte Carlo simulation is a numerical calculation method based on probability and statistical theory. In a statistical sense, Monte Carlo simulation is equivalent to theoretical analysis and is a commonly used numerical analysis method in the industry.

[0120] The effect of the present application on detecting a distributed target when the fixed interference steering vector is mismatched is explained below based on a Monte Carlo simulation experiment.

[0121] Figure 3 The detection probability comparison chart of the technology of the present application and the existing One-Step Generalized Likelihood Ratio Test (OS-GLRT) detector, Two-Step GLRT (TS-GLRT) detector, One-Step Rao criterion (OS-Rao) detector, Two-Step Rao criterion (TS-Rao) detector, and One-Step Wald criterion (OS-Wald) detector under different signal-to-noise ratios is shown. The simulation conditions are: false alarm probability P fa = 0.001, Monte Carlo simulation number M c for calculating the detection threshold = 10 5 , radar detection system channel number M = 16, signal subspace dimension P = 2, interference subspace dimension Q = 8, to-be-detected data amount K = 4, auxiliary data amount L = 2M = 32, signal-to-noise ratio tr H H H (γR) -1 HP] increases from 5 dB to 30 dB at equal intervals, and the jamming-to-noise ratio is β H β×q H J H (γR) -1 Jq= 20 dB, where γR is the noise covariance matrix in the to-be-detected data, the scalar γ is a random positive real number, the (i, j) element of the matrix R is ρ |i-j| , 1≤i,j≤M, and ρ = 0.95. It can be seen from Figure 3 that the detection probability of the method proposed in the present application is higher than that of the existing detectors under different signal-to-noise ratios. When the detection probability of each technology is 80%, the signal-to-noise ratio required by the method proposed in the present application is reduced by more than 3 dB compared with the existing detectors. When the signal-to-noise ratio is large, such as when the signal-to-noise ratio is ≥ 27 dB, the method proposed in the present application and the existing OS-GLRT and TS-GLRT detectors all achieve a detection probability of 100%.

[0122] Figure 4The detection probability comparison chart of the technical solution of the application and the existing OS-GLRT, TS-GLRT, OS-Rao, TS-Rao and OS-Wald detectors under different jam-to-noise ratios is shown. The simulation conditions are: false alarm probability P fa = 0.001, the number of Monte Carlo simulation times M c for calculating the detection threshold is 10 5 , the number of radar detection system channels M is 16, the signal subspace dimension P is 2, the interference subspace dimension Q is 8, the amount of to-be-detected data K is 4, the amount of auxiliary data L is 2M = 32, the signal-to-noise ratio is tr[P H H H (γR) -1 HP] = 20 dB, the jam-to-noise ratio is β H β×q H J H (γR) -1 Jq increases from -30 dB to 30 dB at equal intervals, where γR is the noise covariance matrix in the to-be-detected data, the scalar γ is a random positive real number, the (i, j) element of the matrix R is ρ |i-j| , 1 ≤ i, j ≤ M, and ρ = 0.95. Figure 4 It can be seen that: the detection probability of the existing detectors does not change with the change of the jam-to-noise ratio, because the existing detectors project the to-be-detected data into the orthogonal interference subspace, thus having interference suppression capability; the method proposed in the application does not have interference suppression capability, but from the simulation results, it can be seen that the detection probability of the method proposed in the application fluctuates by no more than 10% with the change of the jam-to-noise ratio, and the detection probability of the method proposed in the application is more than 30% higher than that of the existing detectors under different jam-to-noise ratios.

[0123] Figure 5 The detection probability comparison chart of the technical solution of the application and the existing OS-GLRT, TS-GLRT, OS-Rao, TS-Rao and OS-Wald detectors under different interference subspace dimensions is shown. The simulation conditions are: false alarm probability P fa = 0.001, the number of Monte Carlo simulation times M c for calculating the detection threshold is 10 5 , the number of radar detection system channels M is 16, the signal subspace dimension P is 1, the interference subspace dimension Q increases from 1 to M-P = 15 at equal intervals, the amount of to-be-detected data K is 4, the amount of auxiliary data L is 2M = 32, the signal-to-noise ratio is tr[P H H H (γR) -1 HP] = 20 dB, the jam-to-noise ratio is β H β×q H J H (γR) -1Jq = 20dB, where γR is the noise covariance matrix in the test data, scalar γ is a random positive real number, and the (i,j)th element in matrix R is ρ. |i-j| , 1≤i,j≤M, ρ=0.95. From Figure 5 It can be seen that when the interference subspace dimension is small, such as Q≤3, the detection probability of the proposed method is basically maintained at close to 100%, similar to that of existing OS-GLRT, TS-GLRT, and OS-Wald detectors. As the interference subspace dimension continues to increase, the detection probabilities of all detectors decrease; however, the detection probability of existing detectors rapidly deteriorates to 0%, while the proposed method consistently maintains a detection probability above 80%. Notably, when the interference subspace dimension Q=MP=15, the signal subspace and interference subspace constitute the entire observation space. At this point, existing detectors all fail because they project the data to be measured onto the orthogonal interference subspace, but the proposed method remains effective with a detection probability greater than 80%, demonstrating that the proposed method has a wider range of applications.

[0124] It should be noted that embodiments of the present invention can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-described hardware circuitry and software, such as firmware.

[0125] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A radar distributed target detection method for fixed interference steering vector mismatch, characterized in that, The method specifically comprises the following steps: S1: obtaining the sampled data of a radar detection system with M channels in K units to be detected The target signal of interest belongs to a column full rank matrix The signal subspace spanned by the target signal, and the coordinate matrix of the target signal in the signal subspace is The steering vector of the fixed interference exists mismatch but belongs to a column full rank matrix The interference subspace spanned by the target signal, and the coordinate vector of the steering vector of the fixed interference in the interference subspace is The conjugate amplitude vector of the fixed interference is Where P is the dimension of the signal subspace, Q is the dimension of the interference subspace, and the channel number M of the radar detection system can also be referred to as the dimension of the data model, Indicates a complex matrix with a dimension of m*n; S2: obtaining auxiliary data sampled by a radar detection system with M channels in L adjacent units of the unit to be detected S3: Utilizing auxiliary data X L Computing an estimate of the noise covariance matrix ​ S4: Under the objective hypothesis H0, the probability density function f0(X) of the test data X is given based on the estimated value S of the noise covariance matrix R. R＝S The expression, and according to f0(X)| R＝S And the interference subspace matrix J, solve for the coordinate vector of the fixed interference steering vector in the interference subspace spanned by matrix J in the test data. The estimated value Solve for the fixed disturbance conjugate amplitude vector in the test data The estimated value Find the estimated value of the power mismatch γ of Gaussian colored noise in the test data and auxiliary data. S5: under the target hypothesis H1, according to the estimated value S of the noise covariance matrix R, the probability density function f1(X) of the data to be measured X is given R＝S The expression of f1(X) is given according to f1(X) R＝s , the signal subspace matrix H and the interference subspace matrix J, the estimated value of the fixed interference steering vector in the data to be measured in the interference subspace spanned by the matrix J The estimated value of the fixed interference conjugate amplitude vector The estimated value of the fixed interference conjugate amplitude vector The estimated value of the fixed interference conjugate amplitude vector The estimated value of the coordinate matrix of the target signal in the signal subspace The estimated value of the coordinate matrix of the target signal in the signal subspace The estimated value of the power mismatch amount γ of the Gaussian color noise in the data to be measured and the auxiliary data S6: the estimated value of the coordinate vector q of the fixed interference steering vector in the interference subspace spanned by the matrix J in the data under test the estimated value of the conjugate amplitude vector β of the fixed interference in the data under test the estimated value of the power mismatch γ of the Gaussian color noise in the data under test and the auxiliary data substitute the probability density function f0(X) of the data under test X R＝S , to obtain the maximum likelihood probability density function of the data under test X under the no-target hypothesis H0 S7: the estimated value of the coordinate vector q of the fixed interference steering vector in the interference subspace spanned by the matrix J in the data under test the estimated value of the conjugate amplitude vector β of the fixed interference in the data under test the estimated value of the coordinate matrix P of the target signal in the signal subspace the estimated value of the power mismatch γ of the Gaussian color noise in the data under test and the auxiliary data substitute the probability density function f1(X) of the data under test X β＝s Under the target hypothesis H1, the maximum likelihood probability density function of the data under test X is obtained S8: calculating a detection statistic t based on a generalized likelihood ratio criterion and a maximum likelihood probability density function of the data under test X under the target hypothesis H1 calculating a detection statistic t based on a generalized likelihood ratio criterion S9: determining a detection threshold T according to the false alarm probability P fa determining a detection threshold T; S10: comparing the size relationship between the detection statistic t based on the generalized likelihood ratio criterion and the detection threshold T to determine whether the target signal of interest exists in the to-be-detected data X; The step S3 uses the auxiliary data X L The estimate S of the noise covariance matrix R is computed as: where (·) H denotes the conjugate transpose; The step S4 gives the probability density function f0(X) of the data X under the null hypothesis H0 from the estimate s of the noise covariance matrix R R＝S is: where exp(·) denotes the natural exponential function, tr(·) denotes the matrix trace, |·| denotes the absolute value of a scalar or the determinant of a matrix, X S = S -1 / 2 X, J S = S -1 / 2 J, (·) -1 / 2 denotes the matrix square root inverse; The estimated value of the coordinate vector q of the fixed interference steering vector in the interference subspace spanned by the matrix J in the data to be tested in the step S4 is: is the maximum eigenvalue corresponding to the eigenvector of the matrix , wherein (·) -1 denotes the inverse of the invertible matrix The estimated value of the fixed interference conjugate amplitude vector β in the data to be tested in step S4 is: The estimated value of the power mismatch amount γ of the Gaussian color noise in the data to be measured and the auxiliary data in the step S4 is:

2. The method of claim 1, wherein the radar distributed target detection method is characterized by, The probability density function f1(X) of the data X under the target hypothesis H1 in the step S5 R＝S is: where H S = S -1 / 2 H; The estimated value of the coordinate vector q of the fixed interference steering vector in the interference subspace spanned by the matrix J in the to-be-tested data in the step S5 is: is the maximum eigenvalue of the matrix corresponding to the eigenvector, wherein I M denotes a unit matrix with a dimension of M; The estimated value of the fixed interference conjugate amplitude vector β in the data to be tested in step S5 is: The estimated value of the coordinate matrix P of the target signal in the signal subspace in the step S5 is: The estimated value of the power mismatch amount γ of the Gaussian color noise in the data to be measured and the auxiliary data in the step S5 is:

3. The method of claim 1, wherein the radar distributed target detection method is fixed when the interference steering vector mismatch occurs. The maximum likelihood probability density function of the data to be measured X under the null hypothesis H0 in the step S6 is is:

4. The method of claim 1, wherein the radar distributed target detection method is fixed when the interference steering vector mismatch occurs. The step S7 has the maximum likelihood probability density function of the data X under the target hypothesis H1 is:

5. The method of claim 1, wherein the radar distributed target detection method is fixed when the interference steering vector mismatch occurs. The detection statistic t based on the generalized likelihood ratio criterion in the step S8 is:

6. The method of claim 1, wherein the radar distributed target detection method is fixed when the interference steering vector mismatch is fixed. The step S9 according to the false alarm probability P fa The specific steps for determining the detection threshold T are: S91: calculating the detection statistic t based on the generalized likelihood ratio criterion based on the target-free test data; S92: perform M on step S91 c The computed generalized likelihood ratio test statistic t is stored in the column vector wherein denotes a real matrix of dimension m x n; S93: rearrange the elements in vector V in ascending order, the i-th element in vector V is the false alarm probability P corresponding to the detection threshold T, where fa corresponding to the detection threshold T, where denotes the floor function.

7. The method of claim 1, wherein the radar distributed target detection method is fixed when the interference steering vector mismatch is fixed. The specific process of comparing the size relationship between the detection statistic t based on the generalized likelihood ratio criterion and the detection threshold T in the step S10 to determine whether the target signal of interest exists in the to-be-detected data X is as follows: if t>T, the target signal of interest exists; if t≤T, the target signal of interest does not exist.

8. A system for radar distributed target detection based on the method of claim 1-7, wherein, The system specifically comprises the following steps: A test data acquisition module is configured to acquire the to-be-detected data from the to-be-detected unit; An auxiliary data acquisition module is configured to acquire the auxiliary data from the adjacent unit; A target-free test data acquisition module is configured to acquire the to-be-detected data from the to-be-detected unit when there is no target to calculate the detection threshold; A parameter acquisition module is configured to acquire the signal subspace column full-rank matrix, the interference subspace column full-rank matrix, and the false alarm probability; A covariance matrix estimation module is configured to estimate the noise covariance matrix by using the auxiliary data; A parameter estimation module is configured to calculate unknown parameters by using the estimated noise covariance matrix, the signal subspace column full-rank matrix, the interference subspace column full-rank matrix, and the to-be-detected data, wherein the unknown parameters include the fixed interference steering vector in the interference subspace, the fixed interference amplitude vector, the power mismatch amount of the Gaussian color noise in the to-be-detected data and the auxiliary data, and the target signal in the signal subspace under the target assumption; A maximum likelihood probability density function calculation module is configured to calculate the maximum likelihood probability density function of the to-be-detected data under the target-free assumption and the target assumption by using the output result of the parameter estimation module; A detection statistic calculation module is configured to calculate the detection statistic based on the generalized likelihood ratio criterion by using the output result of the maximum likelihood probability density function calculation module; A detection threshold calculation module is configured to calculate the detection threshold according to the target-free test data and the false alarm probability; A target detection output module is configured to compare the size of the detection statistic and the detection threshold, and output the judgment result of whether the target signal of interest exists in the to-be-detected data.

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