An adaptive subspace detection method and system for extended targets

Abstract

The application belongs to the technical field of multi-channel signal detection, and particularly relates to an adaptive subspace detection method for extended targets, which comprises the following steps: S1: constructing a target signal subspace matrix and an interference signal subspace matrix, which are respectively represented by matrices A and J, and the dimensions of the matrices are N*p and N*q respectively, wherein (p+q)≤N, and N represents the number of system channels; S2: constructing a sample covariance matrix S=YY H using training samples, wherein Y represents the training samples, and the symbol (·) H represents a conjugate transpose. The application integrally realizes interference suppression, signal accumulation and extended target detection by constructing a detection statistic t.

Description

Technical Field

[0001] This invention belongs to the field of multi-channel signal detection technology, specifically relating to an adaptive subspace detection method and system for extended targets. Background Technology

[0002] In the field of Space-time Adaptive Detection (STAD), multi-channel signal detection utilizes the correlation characteristics of signals and noise in different channels (i.e., the spatial information of the target), thus exhibiting superior detection performance compared to traditional single-channel signal detection. However, with the improvement of radar resolution, the method of treating the detected target as a single scattering point is clearly no longer applicable. In real-world scenarios, many targets (such as large aircraft and ships) appear as several isolated reflection points, meaning the targets exhibit a distributed pattern, referred to as extended targets.

[0003] For the extended target detection problem in STAD (Target-Oriented Array Detection), most existing detectors assume that the target steering vector is completely known. However, in reality, due to array element errors, multipath effects, target maneuvering, and antenna shape distortion, it is usually difficult to obtain accurate target steering vector information. The subspace model can effectively suppress the aforementioned steering vector mismatch problem. In this model, the target steering vector is assumed to be located in a known subspace, but the corresponding coordinates are unknown. The emergence of the subspace model provides a new solution to the extended target detection problem.

[0004] Currently, adaptive subspace detection methods for extended targets mainly rely on detectors designed based on the Generalized Likelihood Ratio Test (GLRT) criterion. These GLRT detectors can achieve good detection performance under certain scenarios. However, since the noise covariance matrix, the target signal subspace, and the subspace coordinates are all unknown, GLRT detectors cannot achieve detection advantages in all parameter scenarios, making the development of new subspace detection methods essential. Based on this consideration, this invention employs the Durbin criterion to design a novel adaptive subspace detection method and system for extended targets, providing a new approach for extended target detection. Summary of the Invention

[0005] The purpose of this invention is to provide an adaptive subspace detection method for extended targets, which can construct detection statistics. It integrates interference suppression, signal accumulation, and extended target detection.

[0006] The specific technical solution adopted by this invention is as follows:

[0007] An adaptive subspace detection method for expanding targets, the detection method comprising the following steps:

[0008] S1: Construct the target signal subspace matrix and the interference signal subspace matrix, respectively using matrix... and The dimensions of the matrices are respectively and ,in , Indicates the number of system channels;

[0009] In S1, the constructed target signal matrix and interference signal matrix All are full-rank unitary matrices, and unitary matrices and Linearly independent.

[0010] S2: Construct the sampling covariance matrix using training samples ,in Represents training samples, symbol Indicates conjugate transpose;

[0011] S3: Utilizing the sampling covariance matrix Construct a quasi-whitening matrix, and then apply it to the target signal subspace matrix. Interference signal subspace matrix and the data matrix to be detected Quasi-whitening processing was performed to obtain whitening matrices. , and The data matrix to be detected The dimension is , Indicates extended dimensions;

[0012] S4: Using the quasi-whitening matrix Construct the orthogonal projection matrix of the target signal Simultaneously utilizing the quasi-whitening matrix Construct the orthogonal projection matrix of the interference signal and the corresponding complementary projection matrix ;

[0013] In S4, the quasi-whitened target signal matrix is ​​used. The constructed orthogonal projection matrix is ​​expressed as: ,in Represents the inverse of a matrix. For dimension is The identity matrix; utilizing the quasi-whitened interference signal matrix The constructed orthogonal projection matrix and the complement projection matrix are respectively expressed as: and .

[0014] S5: Utilizing the whitening matrix The detection statistic is constructed from the orthogonal projection matrix of the target signal and the complementary projection matrix of the interference signal, and is expressed as follows:

[0015]

[0016] in, Represents the trace of a matrix. and They represent dimensions as follows: and The identity matrix.

[0017] S6: Determine the detection threshold using a preset value for the false alarm probability;

[0018] In step S6, the detection threshold determined using the preset false alarm probability value is:

[0019]

[0020] in, Indicates the detection threshold. , For the number of Monte Carlo simulations, This represents the false alarm probability value set by the system. This indicates the rounding operation. For sequence

[0021]

[0022] Arranged from largest to smallest The maximum value, , This represents the first data point of the test data containing only interference and thermal noise components. Secondary implementation .

[0023] S7: Compare the detection statistic with the detection threshold and determine whether the target exists.

[0024] In step S7, the presence of a target is determined by comparing the detection statistic with the detection threshold, and the determination is made in the following two cases:

[0025] If the detection statistic Greater than or equal to the detection threshold If so, then the target is determined to exist;

[0026] If the detection statistic Less than the detection threshold If the target does not exist, then it is determined that the target does not exist.

[0027] An adaptive subspace detection system for extended targets includes the following modules:

[0028] Target and interference data matrix construction module:

[0029] Construct the target signal subspace matrix and the interference signal subspace matrix, respectively using matrix... and The dimensions of the matrices are respectively and ,in , Indicates the number of system channels;

[0030] The sampling covariance matrix construction module is used to construct the sampling covariance matrix from training samples. ,in Represents training samples, symbol Indicates conjugate transpose;

[0031] Data whitening module: utilizing the sampling covariance matrix Construct a quasi-whitening matrix, and then apply it to the target signal subspace matrix. Interference signal subspace matrix and the data matrix to be detected Quasi-whitening processing was performed to obtain whitening matrices. , and The data matrix to be detected The dimension is , Indicates extended dimensions;

[0032] Using the quasi-whitening matrix Construct the orthogonal projection matrix of the target signal and the corresponding complementary projection matrix Simultaneously utilizing the quasi-whitening matrix Construct the orthogonal projection matrix of the interference signal and the corresponding complementary projection matrix ;

[0033] Detection Statistic Construction Module: Utilizing the Whitening Matrix The detection statistic is constructed from the orthogonal projection matrices of the target signal and the interference signal, and is expressed as follows:

[0034]

[0035] in, Represents the trace of a matrix. and They represent dimensions as follows: and The identity matrix;

[0036] Detection threshold calculation module: Determines the detection threshold using a preset false alarm probability value;

[0037] Target decision module: used to compare the detection statistics with the detection threshold and determine whether the target exists.

[0038] The technical effects achieved by this invention are as follows:

[0039] The present invention provides an adaptive subspace detection method and system for extended targets by constructing detection statistics. It integrates interference suppression, signal accumulation, and extended target detection.

[0040] The present invention provides an adaptive subspace detection method and system for extended targets, which utilizes the sampling covariance matrix. The detector proposed in this invention is made to have constant false alarm rate characteristics by performing quasi-whitening processing on the detection data matrix, target signal and interference signal matrix.

[0041] The present invention provides an adaptive subspace detection method and system for extended targets, which can solve the problem of adaptive subspace detection of extended targets and improves radar target detection performance compared with existing detection methods based on the GLRT criterion. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the process of Embodiment 1 of the present invention;

[0043] Figure 2 This is a structural framework diagram of the detection system of Embodiment 2 of the present invention;

[0044] Figure 3 This is a schematic diagram showing the detection probability results of the extended target under different signal-to-noise ratios in the experimental examples of this invention;

[0045] Figure 4 This is a schematic diagram of the detection probability results of the experimental examples of the present invention under different degrees of expansion. Detailed Implementation

[0046] To make the objectives and advantages of this invention clearer, the invention will be specifically described below with reference to embodiments. It should be understood that the following text is merely used to describe one or more specific embodiments of the invention and does not strictly limit the scope of protection specifically claimed by the invention.

[0047] Taking an airborne radar linear array as the research object, it is assumed that its number of array elements is... The number of pulses is The corresponding system dimension Sample to be tested Occupy a The echo cells are independently and identically distributed, therefore The dimension is .exist In the hypothesis, the sample to be tested Includes target signal components Interference signal components and noise components . signal component and They can be represented as and ;

[0048] in, and They are respectively dimensional target signal subspace and The interference signal subspace is a dimensional matrix, and both are column-full rank unitary matrices. and These are the corresponding unknown subspace coordinates, with corresponding dimensions of... and Noise components Each column Satisfying the mean is covariance is The complex Gaussian distribution, i.e. .

[0049] exist In the hypothesis, the sample to be tested Includes only interference signal components and noise components In summary, the binary hypothesis detection problem for extended target detection can be expressed as:

[0050]

[0051] in, For dimension The training sample set, Equal to the total number of training samples; These are the training samples. The noise components in each column All satisfy the mean is covariance is The complex Gaussian distribution, i.e. .

[0052] Example 1:

[0053] like Figure 1As shown, an adaptive subspace detection method for extended targets includes the following steps:

[0054] S1: Construct the target signal subspace matrix and the interference signal subspace matrix, respectively using matrix... and The dimensions of the matrices are respectively and ,in , Indicates the number of system channels;

[0055] In S1, the constructed target signal matrix and interference signal matrix All are full-rank unitary matrices, and unitary matrices and Linearly independent.

[0056] S2: Construct the sampling covariance matrix using training samples ,in Represents training samples, symbol Indicates conjugate transpose;

[0057] S3: Utilizing the sampling covariance matrix Construct a quasi-whitening matrix, and then apply it to the target signal subspace matrix. Interference signal subspace matrix and the data matrix to be detected Quasi-whitening processing was performed to obtain whitening matrices. , and The data matrix to be detected The dimension is , Indicates extended dimensions;

[0058] S4: Using the quasi-whitening matrix Construct the orthogonal projection matrix of the target signal Simultaneously utilizing the quasi-whitening matrix Construct the orthogonal projection matrix of the interference signal and the corresponding complementary projection matrix ;

[0059] In S4, the quasi-whitened target signal matrix is ​​used. The constructed orthogonal projection matrix is ​​expressed as: ,in Represents the inverse of a matrix. For dimension is The identity matrix; utilizing the quasi-whitened interference signal matrix The constructed orthogonal projection matrix and the complement projection matrix are respectively expressed as: and .

[0060] S5: Utilizing the whitening matrix The detection statistic is constructed from the orthogonal projection matrix of the target signal and the complementary projection matrix of the interference signal, and is expressed as follows:

[0061]

[0062] in, Represents the trace of a matrix. and They represent dimensions as follows: and The identity matrix.

[0063] S6: Determine the detection threshold using a preset value for the false alarm probability;

[0064] In step S6, the detection threshold determined using the preset false alarm probability value is:

[0065]

[0066] in, Indicates the detection threshold. , For the number of Monte Carlo simulations, This represents the false alarm probability value set by the system. This indicates the rounding operation. For sequence

[0067]

[0068] Arranged from largest to smallest The maximum value, , This represents the first data point of the test data containing only interference and thermal noise components. Secondary implementation .

[0069] S7: Compare the detection statistic with the detection threshold and determine whether the target exists.

[0070] In step S7, the presence of a target is determined by comparing the detection statistic with the detection threshold, and the determination is made in the following two cases:

[0071] If the detection statistic Greater than or equal to the detection threshold If so, then the target is determined to exist;

[0072] If the detection statistic Less than the detection threshold If the target does not exist, then it is determined that the target does not exist.

[0073] Example 2:

[0074] like Figure 2 As shown, an adaptive subspace detection system for extended targets includes the following modules:

[0075] Target and interference data matrix construction module:

[0076] Construct the target signal subspace matrix and the interference signal subspace matrix, respectively using matrix... and The dimensions of the matrices are respectively and ,in , Indicates the number of system channels;

[0077] Sampling covariance matrix construction module: Constructs the sampling covariance matrix using training samples. ,in Represents training samples, symbol Indicates conjugate transpose;

[0078] Data whitening module: utilizing the sampling covariance matrix Construct a quasi-whitening matrix, and then apply it to the target signal subspace matrix. Interference signal subspace matrix and the data matrix to be detected Quasi-whitening processing was performed to obtain whitening matrices. , and The data matrix to be detected The dimension is , Indicates extended dimensions;

[0079] Using the quasi-whitening matrix Construct the orthogonal projection matrix of the target signal and the corresponding complementary projection matrix Simultaneously utilizing the quasi-whitening matrix Construct the orthogonal projection matrix of the interference signal and the corresponding complementary projection matrix ;

[0080] Detection Statistic Construction Module: Utilizing the Whitening Matrix The detection statistic is constructed from the orthogonal projection matrices of the target signal and the interference signal, and is expressed as follows:

[0081]

[0082] in, Represents the trace of a matrix. and They represent dimensions as follows: and The identity matrix;

[0083] Detection threshold calculation module: Determines the detection threshold using a preset false alarm probability value;

[0084] Target decision module: used to compare the detection statistics with the detection threshold and determine whether the target exists.

[0085] Experimental example:

[0086] Assume the radar system has the following number of channels: Let the false alarm probability be Covariance matrix of clutter and noise The The element at position 1 was set to , , where the symbol This indicates taking the absolute value. Figure 3 The detection probabilities of the target under different signal-to-noise ratios (SNR) are given by the method proposed in this invention, wherein the subspace dimensions of the target signal and the interference signal are respectively... and , Number of training samples Noise-to-interference ratio INR and SNR are respectively

[0087] and

[0088] from Figure 3 As can be seen, when the signal-to-noise ratio is higher than 17 dB, the proposed method achieves a detection probability of over 80% for the extended target. Compared with existing GLRT criterion detectors, the proposed detection method exhibits better detection performance. Furthermore, Figure 4 The detection probabilities of the proposed method for targets with different degrees of expansion are given, wherein... , Signal-to-noise ratio Noise-to-interference ratio The results show that when the extent of expansion is low, the proposed Durbin criterion detector significantly outperforms the existing GLRT detector in detecting expanded targets.

[0089] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited.

Claims

1. An adaptive subspace detection method for extended targets, characterized in that: The detection method includes the following steps: S1: Construct the target signal subspace matrix and the interference signal subspace matrix, respectively using matrix... and The dimensions of the matrices are respectively and ,in , Indicates the number of system channels; S2: Construct the sampling covariance matrix using training samples ,in Represents training samples, symbol Indicates conjugate transpose; S3: Utilizing the sampling covariance matrix Construct a quasi-whitening matrix, and then apply it to the target signal subspace matrix. Interference signal subspace matrix and the data matrix to be detected Quasi-whitening processing was performed to obtain quasi-whitening matrices. , and The data matrix to be detected The dimension is , Indicates extended dimensions; S4: Construct the orthogonal projection matrix of the target signal using the quasi-whitening matrix. Simultaneously, an orthogonal projection matrix of the interference signal is constructed using a quasi-whitening matrix. and the corresponding complementary projection matrix ; S5: Construct the detection statistic using the quasi-whitening matrix, the orthogonal projection matrix of the target signal, and the complementary projection matrix of the interference signal, expressed as: in, Represents the trace of a matrix. and They represent dimensions as follows: and The identity matrix; S6: Determine the detection threshold using a preset value for the false alarm probability; S7: Compare the detection statistic with the detection threshold and determine whether the target exists.

2. The adaptive subspace detection method for extended targets according to claim 1, characterized in that: In S1, the constructed target signal matrix and interference signal matrix All are full-rank unitary matrices, and unitary matrices and Linearly independent.

3. The adaptive subspace detection method for extended targets according to claim 1, characterized in that: In step S4, the quasi-whitened target signal matrix is ​​used. The constructed orthogonal projection matrix is ​​expressed as: ,in Represents the inverse of a matrix; Using quasi-whitening interference signal matrix The constructed orthogonal projection matrix and the complement projection matrix are respectively expressed as: and .

4. The adaptive subspace detection method for extended targets according to claim 1, characterized in that: In step S6, the detection threshold determined using the preset false alarm probability value is: in, Indicates the detection threshold. , For the number of Monte Carlo simulations, This represents the false alarm probability value set by the system. This indicates the rounding operation. For sequence When arranged from largest to smallest The maximum value, , This represents the first data point of the test data containing only interference and thermal noise components. Secondary implementation .

5. The adaptive subspace detection method for extended targets according to claim 1, characterized in that: In step S7, the presence of a target is determined by comparing the detection statistic with the detection threshold, and the determination is made in the following two cases: If the detection statistic Greater than or equal to the detection threshold If so, then the target is determined to exist; If the detection statistic Less than the detection threshold If the target does not exist, then it is determined that the target does not exist.

6. An adaptive subspace detection system for extended targets, characterized in that: Includes the following modules: Target and Interference Data Matrix Construction Module: Constructs the target signal subspace matrix and the interference signal subspace matrix, respectively using matrix... and The dimensions of the matrices are respectively and ,in , Indicates the number of system channels; Sampling covariance matrix construction module: Constructs the sampling covariance matrix using training samples. ,in Represents training samples, symbol Indicates conjugate transpose; Data whitening module: utilizing the sampling covariance matrix Construct a quasi-whitening matrix, and then apply it to the target signal subspace matrix. Interference signal subspace matrix and the data matrix to be detected Quasi-whitening processing was performed to obtain quasi-whitening matrices. , and The data matrix to be detected The dimension is , Indicates extended dimensions; Constructing the orthogonal projection matrix of the target signal using the quasi-whitening matrix Simultaneously, an orthogonal projection matrix of the interference signal is constructed using a quasi-whitening matrix. and the corresponding complementary projection matrix ; The detection statistic construction module constructs detection statistics using the quasi-whitening matrix, the orthogonal projection matrix of the target signal, and the complementary projection matrix of the interference signal, expressed as follows: in, Represents the trace of a matrix. and They represent dimensions as follows: and The identity matrix; Detection threshold calculation module: Determines the detection threshold using a preset false alarm probability value; Target decision module: used to compare the detection statistics with the detection threshold and determine whether the target exists.

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