Adaptive subspace detection method and system suitable for partially uniform environment

Abstract

The invention belongs to the technical field of multichannel signal detection, and particularly relates to a self-adaptive subspace detection method and system suitable for a partially uniform environment, and the method comprises the following steps: 1, constructing a target signal subspace matrix and an interference signal subspace matrix; step 2, constructing a sampling covariance matrix by using the training sample; 3, constructing a quasi-whitening matrix by using the sampling covariance matrix, and then performing quasi-whitening processing to respectively obtain whitening matrixes; 4, constructing an orthogonal projection matrix of the target signal and a corresponding complementary projection matrix; step 5, constructing an intermediate variable matrix and non-zero characteristic decomposition, and obtaining non-uniform parameters; step 6, constructing detection statistics by using the to-be-detected data matrix, the orthogonal projection matrix of the target signal, the orthogonal projection matrix of the interference signal and the non-uniform parameters; step 7, determining a detection threshold; and 8, judging whether a target exists or not. According to the invention, the detection performance of the airborne radar in a partially uniform environment is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of multi-channel signal detection, and in particular relates to an adaptive subspace detection method and system suitable for a partially uniform environment. Background Art

[0002] With the continuous improvement of airborne radar technology and the development of pulse coherent processing technology, airborne radar can obtain multi-channel data and realize efficient detection of target signals through space-time adaptive detection (STAD) technology. At present, the most widely used model in STAD is the subspace model. In this model, the row elements and column elements of the signal are located in a known subspace, but the corresponding coordinates are unknown. For both target signals and interference signals, both can be characterized by the subspace model.

[0003] In addition, in adaptive detection, the STAD method utilizes the correlation characteristics of signals and noise in different channels (i.e., the spatial information of the target), so it has better detection performance than traditional single-channel signal detection. However, existing STAD methods usually assume that the detection background is uniform, that is, the statistical characteristics of the noise in the samples to be detected are consistent with those in the training samples. In actual scenarios, due to environmental changes and radar instrument problems, the statistical characteristics of the noise between the samples to be detected and the training samples are often very different, which seriously affects the performance of the detector. A model that is often used to describe environmental inhomogeneity is the partially uniform environment model, in which the covariance matrix of the training sample and the sample to be detected differs by an unknown inhomogeneous parameter.

[0004] At present, the problem of adaptive target detection in partially homogeneous environments is mainly solved by designing detectors based on the generalized likelihood ratio test (GLRT) criterion. This GLRT detector can obtain better detection performance in certain scenarios. However, since the noise covariance matrix, the subspace of the target signal and the subspace coordinates are unknown, the GLRT detector cannot obtain detection advantages in all parameter scenarios, and it is extremely necessary to develop new subspace detection. Based on this consideration, the present invention adopts the Durbin criterion to design a new adaptive subspace detection method and system suitable for partially homogeneous environments, the purpose of which is to improve the detection performance of airborne radar in partially homogeneous environments and provide a new way for the application of STAD technology. Summary of the invention

[0005] The purpose of the present invention is to provide an adaptive subspace detection method and system suitable for partially uniform environments, and the purpose is to improve the detection performance of airborne radar in partially uniform environments.

[0006] The technical solution adopted by the present invention is as follows:

[0007] An adaptive subspace detection method suitable for partially uniform environments comprises the following steps:

[0008] Step 1: Construct the target signal subspace matrix and the interference signal subspace matrix, represented by matrices A and J respectively, with the dimensions of the matrices being N×p and N×q respectively, where (p+q)≤N, and N represents the number of system channels;

[0009] Step 2: Use training samples to construct the sampling covariance matrix S = YY H , where Y represents the training sample and the symbol (·) H represents conjugate transpose;

[0010] Step 3: Use the sampling covariance matrix S to construct a quasi-whitening matrix, and then use it to perform quasi-whitening processing on the target signal subspace matrix A, the interference signal subspace matrix J and the data matrix to be detected X, and obtain the whitening matrices and The dimension of the data matrix X to be detected is N×K, where K represents the extended dimension;

[0011] Step 4: Use the quasi-whitening matrix Construct the orthogonal projection matrix of the target signal And the corresponding complementary projection matrix At the same time, using the quasi-whitening matrix Constructing the orthogonal projection matrix of the interference signal And the corresponding complementary projection matrix

[0012] Step 5: Use the quasi-whitened data matrix to be detected and the orthogonal projection matrix of the interference signal to construct the intermediate variable matrix and non-zero eigendecomposition, and combine the system dimension to obtain the non-uniform parameter σ 2 ;

[0013] Step 6: Construct detection statistics using the data matrix to be detected after quasi-whitening processing, the orthogonal projection matrix of the target signal, the orthogonal projection matrix of the interference signal and the non-uniform parameters;

[0014] Step 7: Determine the detection threshold using the preset value of false alarm probability;

[0015] Step 8: Compare the detection statistic with the detection threshold and determine whether the target exists.

[0016] Preferably, in step 1, the constructed target signal matrix A and interference signal matrix J are both column-full rank unitary matrices, and the unitary matrices A and J are linearly independent.

[0017] Preferably, in step 3, the quasi-whitening matrix constructed by the sampling covariance matrix S is used to perform quasi-whitening processing on the target signal subspace matrix A, the interference signal subspace matrix J and the data matrix to be detected X, and the specific operation is as follows: and

[0018] Preferably, in step 4, the quasi-whitening target signal matrix The constructed orthogonal projection matrix and complementary projection matrix are expressed as and in(·) -1 represents the inverse of the matrix, I N is a unit matrix with dimension N×N; using the quasi-whitening interference signal matrix The constructed orthogonal projection matrix and complementary projection matrix are expressed as and

[0019] Preferably, in step 5, the intermediate variable constructed using the quasi-whitened data matrix to be detected and the orthogonal projection matrix of the interference signal is Obtaining non-uniform parameters is the solution of the following formula:

[0020]

[0021] Among them, L is the number of training samples, λ i for is the i-th non-zero eigenvalue of , and r is the total number of non-zero eigenvalues.

[0022] Preferably, in step 6, the detection statistic constructed using the data matrix to be detected after quasi-whitening processing, the orthogonal projection matrix of the target signal, the orthogonal projection matrix of the interference signal and the non-uniform parameters is expressed as:

[0023]

[0024] Among them, I K is the identity matrix of dimension K×K, and tr(·) represents the trace of the matrix.

[0025] Preferably, in step 7, the detection threshold determined by using the preset value of false alarm probability is

[0026] η=t(n)

[0027] Where η represents the detection threshold, n = [Qμ], Q is the number of Monte Carlo simulations, μ represents the false alarm probability value set by the system, [·] represents the rounding operation, and t(n) is the sequence

[0028]

[0029] The nth largest value when arranged from largest to smallest, for The conjugate transposed matrix of X(k) represents the k-th realization k=1, 2,…,Q of the data to be detected containing only interference and thermal noise components.

[0030] Preferably, in step 8, the detection statistic is compared with the detection threshold to determine whether the target exists, and the determination is made in the following two cases:

[0031] If the detection statistic t is greater than or equal to the detection threshold η, the target is determined to exist;

[0032] If the detection statistic t is less than the detection threshold η, it is determined that the target does not exist.

[0033] An adaptive subspace detection system for partially uniform environments, comprising:

[0034] Target and interference data matrix construction module: used to construct target signal subspace matrix and interference signal subspace matrix;

[0035] Sampling covariance matrix construction module: used to construct the sampling covariance matrix through training samples;

[0036] Data quasi-whitening module: used to perform quasi-whitening processing on the target signal subspace matrix, the interference signal subspace matrix and the data matrix to be detected, and obtain the quasi-whitened target signal subspace matrix, the quasi-whitened interference signal subspace matrix and the quasi-whitened data matrix to be detected respectively;

[0037] An orthogonal projection matrix construction module is used to construct an orthogonal projection matrix of a target signal and an orthogonal projection matrix of an interference signal respectively through a quasi-whitened target signal subspace matrix and a quasi-whitened interference signal subspace matrix;

[0038] Non-uniform parameter solving module: used to perform eigendecomposition on the intermediate variable matrix constructed by the orthogonal projection matrix of the data matrix to be detected and the interference signal, and then obtain the non-uniform parameters of the environment by combining the system dimension;

[0039] Detection statistics construction module: used to construct detection statistics through quasi-whitening target signal matrix, interference signal orthogonal projection matrix, to-be-detected data matrix and environmental non-uniform parameters;

[0040] Detection threshold calculation module: used to determine the detection threshold based on the preset value of false alarm probability;

[0041] Target decision module: used to compare the detection statistic with the detection threshold and determine whether the target exists.

[0042] The technical effects achieved by the present invention are:

[0043] In the present invention, by constructing the detection statistic t, interference suppression, signal accumulation and target detection are achieved in an integrated manner;

[0044] In the present invention, the sampling covariance matrix S is used to perform quasi-whitening processing on the data matrix to be detected, the target signal matrix and the interference signal matrix, so that the detector proposed in the present invention has a constant false alarm characteristic.

[0045] The present invention can solve the problem of adaptive target detection in a partially uniform environment, and improves radar target detection performance compared with the existing detection method based on the GLRT criterion. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of an adaptive subspace detection method applicable to a partially uniform environment of the present invention;

[0047] Figure 2 It is a structural framework diagram of an adaptive subspace detection system suitable for partially uniform environments of the present invention;

[0048] Figure 3 It is a schematic diagram of detection probability of extended targets under different signal-to-noise ratios in the target detection results of the method proposed in the present invention;

[0049] Figure 4 It is a schematic diagram of detection probability under different degrees of non-uniformity in the target detection results of the method proposed in the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose and advantages of the present invention more clearly understood, the present invention is specifically described below in conjunction with embodiments. It should be understood that the following text is only used to describe one or several specific embodiments of the present invention, and does not strictly limit the scope of protection of the specific claims of the present invention.

[0051] Embodiment 1:

[0052] Take the airborne radar linear array as the research object, assuming that the number of array elements is N a , the number of pulses is N b , the corresponding system dimension N = N a ×N b The sample X to be tested occupies K echo units, and each echo unit is independent and identically distributed, so the dimension of X is N×K. 1 Assume that the sample to be tested X contains the target signal component T, the interference signal component A and the noise component N. The signal components T and A can be expressed as:

[0053] T=HΦ (1)

[0054] and

[0055] A=JΨ (2)

[0056] Among them, H and J are N×p-dimensional target signal subspace and N×q-dimensional interference signal subspace, respectively, both of which are column-full rank unitary matrices; Φ and Ψ are the corresponding unknown subspace coordinates, and the corresponding dimensions are p×1 and q×1, respectively. i ,i=1,2,…,K satisfies the mean value of 0 N×1 , the covariance is R t The complex Gaussian distribution, that is, n i ~CN N (0 N×1 ,R t ).

[0057] In H 0 In the assumption, the sample to be detected X only contains the interference signal component A and the noise component N; in summary, the binary hypothesis detection problem for extended target detection can be expressed as:

[0058]

[0059] Among them, X L is a training sample set with dimension N×L, where L is equal to the total number of training samples; N L Then it is the training sample X L The noise component in each column n e,i , i=1,2,…,L all satisfy the mean value of 0 N×1 , a complex Gaussian distribution with covariance R, that is, n i ~CN N (0 N×1 ,R). In a partially uniform environment, the noise covariance matrix R in the sample to be detected t The relationship between and the noise covariance matrix R in the training sample is:

[0060] R t =σ 2 R (4)

[0061] Among them, σ 2 is the non-uniform parameter of the environment. In a uniform environment, σ 2 =1

[0062] like Figure 1 As shown, an adaptive subspace detection method suitable for a partially uniform environment comprises the following steps:

[0063] Step 1: Construct the target signal subspace matrix and the interference signal subspace matrix, represented by matrices A and J respectively, with the dimensions of the matrices being N×p and N×q respectively, where (p+q)≤N, and N represents the number of system channels;

[0064] In the step 1, the constructed target signal matrix A and interference signal matrix J are both full-rank unitary matrices, and the unitary matrices A and J are linearly independent;

[0065] Step 2: Use training samples to construct the sampling covariance matrix S = YY H , where Y represents the training sample and the symbol (·) H represents conjugate transpose;

[0066] Step 3: Use the sampling covariance matrix S to construct a quasi-whitening matrix, and then use it to perform quasi-whitening processing on the target signal subspace matrix A, the interference signal subspace matrix J and the data matrix to be detected X, and obtain the whitening matrices and The dimension of the data matrix X to be detected is N×K, where K represents the extended dimension;

[0067] In step 3, the quasi-whitening matrix constructed by the sampling covariance matrix S is used to perform quasi-whitening processing on the target signal subspace matrix A, the interference signal subspace matrix J and the data matrix to be detected X, and the specific operation is:

[0068]

[0069] Step 4: Use the quasi-whitening matrix Construct the orthogonal projection matrix of the target signal And the corresponding complementary projection matrix At the same time, using the quasi-whitening matrix Constructing the orthogonal projection matrix of the interference signal And the corresponding complementary projection matrix

[0070] In step 4, the quasi-whitening target signal matrix is ​​used The constructed orthogonal projection matrix and complementary projection matrix are expressed as and in(·) -1 represents the inverse of the matrix, I N is a unit matrix with dimension N×N; using the quasi-whitening interference signal matrix The constructed orthogonal projection matrix and complementary projection matrix are expressed as:

[0071]

[0072] Step 5: Use the quasi-whitened data matrix to be detected and the orthogonal projection matrix of the interference signal to construct the intermediate variable matrix and non-zero eigendecomposition, and combine the system dimension to obtain the non-uniform parameter σ 2 ;

[0073] In step 5, the intermediate variable constructed using the quasi-whitened data matrix to be detected and the orthogonal projection matrix of the interference signal is Obtaining non-uniform parameters is the solution of the following formula:

[0074]

[0075] Among them, L is the number of training samples, λ i for is the i-th non-zero eigenvalue of , and r is the total number of non-zero eigenvalues.

[0076] Step 6: Construct detection statistics using the data matrix to be detected after quasi-whitening processing, the orthogonal projection matrix of the target signal, the orthogonal projection matrix of the interference signal and the non-uniform parameters;

[0077] In step 6, the detection statistic constructed using the quasi-whitened data matrix to be detected, the orthogonal projection matrix of the target signal, the orthogonal projection matrix of the interference signal and the non-uniform parameters is expressed as

[0078]

[0079] Among them, I K is the identity matrix of dimension K×K, tr(·) represents the trace of the matrix;

[0080] Step 7: Determine the detection threshold using the preset value of false alarm probability;

[0081] In step 7, the detection threshold determined by the preset value of false alarm probability is:

[0082] η=t(n) (9)

[0083] Where η represents the detection threshold, n = [Qμ], Q is the number of Monte Carlo simulations, μ represents the false alarm probability value set by the system, [·] represents the rounding operation, and t(n) is the sequence

[0084]

[0085] The nth largest value when arranged from largest to smallest, for The conjugate transposed matrix of X(k) represents the k-th realization of the data to be detected containing only interference and thermal noise components, k=1, 2, ..., Q;

[0086] Step 8: Compare the detection statistic with the detection threshold and determine whether the target exists.

[0087] In step 8, the detection statistic is compared with the detection threshold to determine whether the target exists, and the determination is made in the following two cases:

[0088] If the detection statistic t is greater than or equal to the detection threshold η, the target is determined to exist;

[0089] If the detection statistic t is less than the detection threshold η, it is determined that the target does not exist.

[0090] In the present invention, by constructing the detection statistic t, interference suppression, signal accumulation and target detection are achieved in an integrated manner;

[0091] In the present invention, the sampling covariance matrix S is used to perform quasi-whitening processing on the data matrix to be detected, the target signal matrix and the interference signal matrix, so that the detector proposed in the present invention has a constant false alarm characteristic.

[0092] The present invention can solve the problem of adaptive target detection in a partially uniform environment, and improves radar target detection performance compared with the existing detection method based on the GLRT criterion.

[0093] Embodiment 2:

[0094] like Figure 2 An adaptive subspace detection system for partially uniform environments, comprising:

[0095] Target and interference data matrix construction module: used to construct target signal subspace matrix and interference signal subspace matrix;

[0096] Sampling covariance matrix construction module: used to construct the sampling covariance matrix through training samples;

[0097] Data quasi-whitening module: used to perform quasi-whitening processing on the target signal subspace matrix, the interference signal subspace matrix and the data matrix to be detected, and obtain the quasi-whitened target signal subspace matrix, the quasi-whitened interference signal subspace matrix and the quasi-whitened data matrix to be detected respectively;

[0098] An orthogonal projection matrix construction module is used to construct an orthogonal projection matrix of a target signal and an orthogonal projection matrix of an interference signal respectively through a quasi-whitened target signal subspace matrix and a quasi-whitened interference signal subspace matrix;

[0099] Non-uniform parameter solving module: used to perform eigendecomposition on the intermediate variable matrix constructed by the orthogonal projection matrix of the data matrix to be detected and the interference signal, and then obtain the non-uniform parameters of the environment by combining the system dimension;

[0100] Detection statistics construction module: used to construct detection statistics through quasi-whitening target signal matrix, interference signal orthogonal projection matrix, to-be-detected data matrix and environmental non-uniform parameters;

[0101] Detection threshold calculation module: used to determine the detection threshold based on the preset value of false alarm probability;

[0102] Target decision module: used to compare the detection statistic with the detection threshold and determine whether the target exists.

[0103] The effect of the present invention is further described below in conjunction with simulation experiments;

[0104] Simulation experiment;

[0105] Assume that the number of radar system channels is N = 12 and the false alarm probability is 1 × 10 -3 , the element at the (i, j)th position of the covariance matrix R of the clutter and noise is set to R(i, j) = 0.95 |i-j| , i,j=1,2,…,N, where the symbol |·| indicates taking the absolute value. Figure 3 The detection probability of the target under different signal-to-noise ratios (SNRs) is given, where the subspace dimensions of the target signal and the interference signal are p=4 and q=4, K=5, the number of training samples is L=2N, the interference-to-noise ratio is INR=5dB, and the INR and SNR are INR=ψ H J H R -1 Jψ and SNR=φ H A H R -1 Aφ

[0106] from Figure 3 It can be seen that when the signal-to-noise ratio is higher than 23dB, the detection probability of the proposed method for extended targets is higher than 80%. Compared with the existing GLRT criterion detector, the proposed detection method has better detection performance. In addition, Figure 4 The detection probability of the proposed method for targets with different degrees of non-uniformity is given, where p = 4, q = 3, signal-to-noise ratio SNR = 25dB, and interference-to-noise ratio INR = 5dB. From the results, it can be seen that when the degree of non-uniformity increases, the performance of the detector will decrease, but the Durbin criterion detector proposed in the present invention still has a higher detection probability than the traditional GLRT detector.

[0107] In the present invention, by constructing the detection statistic t, interference suppression, signal accumulation and target detection are achieved in an integrated manner;

[0108] In the present invention, the sampling covariance matrix S is used to perform quasi-whitening processing on the data matrix to be detected, the target signal matrix and the interference signal matrix, so that the detector proposed in the present invention has a constant false alarm characteristic.

[0109] The present invention can solve the problem of adaptive target detection in a partially uniform environment, and improves radar target detection performance compared with the existing detection method based on the GLRT criterion.

[0110] The above is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art unless otherwise specified and limited.

Claims

1. An adaptive subspace detection method suitable for partially uniform environments, characterized in that: The following steps are involved: Step 1: Construct the target signal subspace matrix and the interference signal subspace matrix, represented by matrices A and J respectively, with the dimensions of the matrices being N×p and N×q respectively, where (p+q)≤N, and N represents the number of system channels; Step 2: Use training samples to construct the sampling covariance matrix S = YY H , where Y represents the training sample and the symbol (·) H represents conjugate transpose; Step 3: Use the sampling covariance matrix S to construct a quasi-whitening matrix, and then use it to perform quasi-whitening processing on the target signal subspace matrix A, the interference signal subspace matrix J and the data matrix to be detected X, and obtain the whitening matrices and The dimension of the data matrix X to be detected is N×K, where K represents the extended dimension; Step 4: Use the quasi-whitening matrix Construct the orthogonal projection matrix of the target signal And the corresponding complementary projection matrix At the same time, using the quasi-whitening matrix Constructing the orthogonal projection matrix of the interference signal And the corresponding complementary projection matrix Step 5: Use the quasi-whitened data matrix to be detected and the orthogonal projection matrix of the interference signal to construct the intermediate variable matrix and non-zero eigendecomposition, and combine the system dimension to obtain the non-uniform parameter σ 2 ; Step 6: Construct detection statistics using the data matrix to be detected after quasi-whitening processing, the orthogonal projection matrix of the target signal, the orthogonal projection matrix of the interference signal and the non-uniform parameters; Step 7: Determine the detection threshold using the preset value of false alarm probability; Step 8: Compare the detection statistic with the detection threshold and determine whether the target exists.

2. The adaptive subspace detection method for partially uniform environment according to claim 1, characterized in that: In step 1, the constructed target signal matrix A and interference signal matrix J are both column-full rank unitary matrices, and the unitary matrices A and J are linearly independent.

3. The adaptive subspace detection method for partially uniform environment according to claim 1, characterized in that: In step 3, the quasi-whitening matrix constructed by the sampling covariance matrix S is used to perform quasi-whitening processing on the target signal subspace matrix A, the interference signal subspace matrix J and the data matrix to be detected X, and the specific operation is: and 4. The adaptive subspace detection method for partially uniform environment according to claim 1, characterized in that: In step 4, the quasi-whitening target signal matrix is ​​used The constructed orthogonal projection matrix and complementary projection matrix are expressed as and in(·) -1 represents the inverse of the matrix, I N is the identity matrix of dimension N×N; Using the quasi-whitening interference signal matrix The constructed orthogonal projection matrix and complementary projection matrix are expressed as: and 5. The adaptive subspace detection method for partially uniform environment according to claim 1, characterized in that: In step 5, the intermediate variable constructed using the quasi-whitened data matrix to be detected and the orthogonal projection matrix of the interference signal is Obtaining non-uniform parameters is the solution of the following formula: Among them, L is the number of training samples, λ i for is the i-th non-zero eigenvalue of , and r is the total number of non-zero eigenvalues.

6. The adaptive subspace detection method for partially uniform environment according to claim 1, characterized in that: In step 6, the detection statistic constructed using the data matrix to be detected after quasi-whitening processing, the orthogonal projection matrix of the target signal, the orthogonal projection matrix of the interference signal and the non-uniform parameters is expressed as: Among them, I K is the identity matrix of dimension K×K, and tr(·) represents the trace of the matrix.

7. The adaptive subspace detection method for partially uniform environment according to claim 1, characterized in that: In step 7, the detection threshold determined by the preset value of false alarm probability is: η=t(n); wherein η represents the detection threshold, n=[Qμ], Q is the number of Monte Carlo simulations, μ represents the false alarm probability value set by the system, [·] represents a rounding operation, and t(n) is a sequence; The nth largest value when arranged from largest to smallest, for The conjugate transposed matrix of X(k) represents the k-th realization k=1, 2,…,Q of the data to be detected containing only interference and thermal noise components.

8. The adaptive subspace detection method for partially uniform environment according to claim 1, characterized in that: In step 8, the detection statistic is compared with the detection threshold to determine whether the target exists, and the determination is made in the following two cases: If the detection statistic t is greater than or equal to the detection threshold η, the target is determined to exist; If the detection statistic t is less than the detection threshold η, it is determined that the target does not exist.

9. An adaptive subspace detection system suitable for partially uniform environments, characterized in that: include: Target and interference data matrix construction module: used to construct target signal subspace matrix and interference signal subspace matrix; Sampling covariance matrix construction module: used to construct the sampling covariance matrix through training samples; Data quasi-whitening module: used to perform quasi-whitening processing on the target signal subspace matrix, the interference signal subspace matrix and the data matrix to be detected, and obtain the quasi-whitened target signal subspace matrix, the quasi-whitened interference signal subspace matrix and the quasi-whitened data matrix to be detected respectively; An orthogonal projection matrix construction module is used to construct an orthogonal projection matrix of a target signal and an orthogonal projection matrix of an interference signal respectively through a quasi-whitened target signal subspace matrix and a quasi-whitened interference signal subspace matrix; Non-uniform parameter solving module: used to perform eigendecomposition on the intermediate variable matrix constructed by the orthogonal projection matrix of the data matrix to be detected and the interference signal, and then obtain the non-uniform parameters of the environment by combining the system dimension; Detection statistics construction module: used to construct detection statistics through quasi-whitening target signal matrix, interference signal orthogonal projection matrix, to-be-detected data matrix and environmental non-uniform parameters; Detection threshold calculation module: used to determine the detection threshold based on the preset value of false alarm probability; Target decision module: used to compare the detection statistic with the detection threshold and determine whether the target exists.

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