A multi-channel anti-interference angle super-resolution method based on blocking matrix

Abstract

The application relates to the technical field of radar signal processing, and discloses a multi-channel anti-interference angle super-resolution method based on a blocking matrix, which comprises the following steps: interference covariance matrices in radar echo data are extracted, eigenvalue decomposition is carried out, interference spatial feature vectors are extracted, and interference suppression blocking matrices are calculated; then, target echoes after interference suppression based on the blocking matrices are calculated; finally, an angle super-resolution steering vector set reconstructed by the interference suppression blocking matrix is calculated, and an IAA algorithm is used to solve and realize angle super-resolution in an interference environment. In the application, the blocking matrix is used to suppress interference while retaining spatial degrees of freedom, and on this basis, the IAA algorithm is used to realize angle super-resolution of main lobe echoes, so that the angle super-resolution problem in an interference environment can be effectively solved, and the sea target detection capability of a multi-channel active phased array radar in a complex interference and dense target environment can be greatly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radar signal processing, and particularly relates to a multi-channel anti-interference angle super-resolution method based on a blocking matrix. BACKGROUND

[0002] Current electromagnetic confrontation is increasingly fierce, and radar sea detection is faced with a complex interference environment, and sea targets are relatively dense, and multiple target echoes can exist in a same range gate. Suppression of interference and realization of angle super-resolution are of great significance to improve the dense target detection capability of a sea search radar in an interference environment.

[0003] With the increasing maturity of phased array radar technology, a multi-channel active phased array system is widely used in a sea search radar, and a degree of freedom is introduced in a space domain, and theoretically, the system has the ability of sidelobe interference suppression and main lobe target angle super-resolution. The super-resolution spatial spectrum estimation technology is a commonly used angle super-resolution technology, and the technology is directly applied to target angle super-resolution in an interference environment, and has the following defects:

[0004] 1. Target echo power is far less than interference power, and is easily submerged by interference.

[0005] 2. The super-resolution spatial spectrum estimation technology usually requires that the number of echo samples is greater than twice the number of channels, and in practice, a target echo has only a few range gates in most cases, and cannot meet the processing requirements. SUMMARY

[0006] Therefore, the present application provides a multi-channel anti-interference angle super-resolution method based on a blocking matrix, which suppresses interference by using a blocking matrix while retaining the spatial domain degree of freedom, and realizes main lobe echo angle super-resolution through an IAA algorithm, and can effectively solve the angle super-resolution problem in an interference environment.

[0007] A multi-channel anti-interference angle super-resolution method based on a blocking matrix, comprising the following steps:

[0008] Step 1: obtaining an interference covariance matrix according to radar echo data;

[0009] Step 2: performing eigenvalue decomposition on the interference covariance matrix to calculate eigenvalues and corresponding eigenvectors thereof;

[0010] Step 3: extracting interference spatial eigenvectors to calculate an interference suppression blocking matrix;

[0011] Step 4: processing target echoes through the interference suppression blocking matrix to obtain interference-suppressed target echoes, and reconstructing a conventional spatial domain steering vector set based on the interference suppression blocking matrix to obtain an angle super-resolution steering vector set;

[0012] Step 5, based on the angle super-resolution steering vector set, the IAA algorithm is used to solve the angle super-resolution of the target echo after interference suppression.

[0013] Further, in step 1, the interference covariance matrix R x = Sj x Sj H / L, S is the radar echo data, Sj is the echo corresponding to the non-target distance gate in S, H is the conjugate transpose, L is the number of interference samples.

[0014] Further, in step 2, the EIG algorithm is used to perform eigenvalue decomposition on the interference covariance matrix

[0015] [V, D] = eig(R x )

[0016] , wherein V is the eigenvalue obtained by eigenvalue decomposition, and D is the corresponding eigenvector obtained by eigenvalue decomposition.

[0017] Further, in step 3, according to the echo noise power P n , the eigenvector D corresponding to the eigenvalue V greater than εP n is extracted as the interference space eigenvector Dj, wherein ε is a constant.

[0018] Further, in step 3, the interference suppression blocking matrix Φ = I M×M - D j x [(D j x D j H ) D j H ], wherein I M*M is the unit matrix.

[0019] Further, in step 4, according to the target distance gate detected by the conventional ADBF processing, the echo S t corresponding to the distance gate where the target is located is extracted, and the interference suppression is performed on S t through the blocking matrix, to obtain the target echo after interference suppression based on the blocking matrix y = Φ x S t .

[0020] Further, in step 4, the conventional multi-channel spatial steering vector calculation method is used to obtain the conventional spatial steering vector set Ψ = [fsub(Az1)...fsub(Az n )...fsub(Az N )], wherein fsub(x) is the conventional multi-channel spatial steering vector calculation function, N is the number of uniformly discrete angles in space, Az is the main lobe range angle discrete array, Az = [Az1...Az n ...Az N ].

[0021] The angle super-resolution steering vector set A = Φ × Ψ is obtained based on the interference suppression blocking matrix reconstruction.

[0022] Further, the angle super-resolution solving algorithm of the target echo after interference suppression in step 4 is as follows:

[0023] A, initialization vector P: Wherein k = 1 2... N

[0024] B, calculate the matrix R = A × diag (P) × A H + P n × I M×M , diag (*) is a function for constructing a diagonal matrix;

[0025] C, iterative calculation of vector P:

[0026] Wherein k = 1 2... N;

[0027] Repeat B ~ C until P converges, and the super-resolution processing result P is obtained.

[0028] Compared with the prior art, the beneficial effects of the present application are:

[0029] 1. The present application adopts the blocking matrix to suppress interference while retaining the spatial freedom, and on this basis, the main lobe echo angle super-resolution is realized through the IAA algorithm, which can effectively avoid the influence of the interference echo on the main lobe target angle super-resolution. Secondly, by using the IAA algorithm, the angle super-resolution can be solved and realized only by a single echo sample, avoiding the problem of insufficient target echo samples.

[0030] 2. The present application can effectively solve the angle super-resolution problem of the target echo with few samples in the sidelobe interference environment, and can greatly improve the sea target detection capability of the multi-channel active phased array radar in the complex interference and dense target environment. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0032] Figure 1 The flow chart of the multi-channel anti-interference angle super-resolution method based on the blocking matrix in embodiment 1 or 2;

[0033] Figure 2 The target echo diagram before and after interference suppression based on the blocking matrix in embodiment 2;

[0034] Figure 3 Fig. 2 is a schematic diagram of the IAA angle super-resolution processing result in Example 2. DETAILED DESCRIPTION

[0035] The embodiments of the present application will be described in detail below with reference to the drawings.

[0036] The above embodiments are only some of the embodiments of the present application, but not all of them. The present application can be implemented or applied in other different specific embodiments, and the details in the specification can be modified or changed based on different views and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0037] Example 1

[0038] Referring to Figure 1 A multi-channel anti-jamming angle super-resolution method based on a blocking matrix includes the following steps:

[0039] Step 1: obtaining a jamming covariance matrix according to radar echo data;

[0040] Step 2: performing eigenvalue decomposition on the jamming covariance matrix to calculate eigenvalues and corresponding eigenvectors thereof;

[0041] Step 3: extracting a jamming spatial eigenvector and calculating a jamming suppression blocking matrix;

[0042] Step 4: processing target echoes by the jamming suppression blocking matrix to obtain jamming-suppressed target echoes, and reconstructing a conventional spatial steering vector set based on the jamming suppression blocking matrix to obtain an angle super-resolution steering vector set;

[0043] Step 5: taking the angle super-resolution steering vector set as a basis, and solving angle super-resolution of the jamming-suppressed target echoes by using an IAA algorithm.

[0044] In this embodiment, firstly, the interference covariance matrix in the radar echo data is extracted, the interference spatial feature vector is extracted through eigenvalue decomposition, and the interference suppression blocking matrix is calculated; then the target echo after interference suppression based on the blocking matrix is calculated; finally, the angle super-resolution steering vector set reconstructed by the interference suppression blocking matrix is calculated, and the IAA algorithm is used to solve and realize angle super-resolution in the interference environment. The blocking matrix is used to suppress interference while preserving the spatial freedom, and the IAA algorithm is used to realize angle super-resolution of the main lobe echo, which effectively solves the angle super-resolution problem in the interference environment and also realizes angle super-resolution with only a single echo sample, avoiding the problem of insufficient target echo samples; thereby greatly improving the sea target detection capability of the multi-channel active phased array radar in the complex interference and dense target environment.

[0045] Embodiment 2

[0046] Referring to Figures 1-3 , the specific process and effects of the multi-channel anti-jamming angle super-resolution method based on the blocking matrix are explained with the following simulation parameters:

[0047] Radar system parameters: one-dimensional linear active phased array radar, element spacing 0.015 m, element number 64, uniformly divided into M=16 subarrays, radar system beam width about 1.8°, beam pointing 0°, system noise power P n =1000, 1000 range gates in one pulse period;

[0048] Interference parameters: all-range-gate noise suppression interference, interference direction 15°, single-element jamming-to-noise ratio JNR=60 dB;

[0049] Target parameters: single-point signal, two target directions are 0.5° and-0.5° respectively, single-element SNR=0 dB at the 300th range gate;

[0050] The radar echo is S, which is a 16x1000 matrix;

[0051] 1. Eigenvalue decomposition of interference covariance matrix

[0052] The interference covariance matrix R x = Sjx Sj H / L, Sj is the echo corresponding to the 600th-900th range gate in S.

[0053] The EIG algorithm is used for eigenvalue decomposition of R x , [V, D]=eig(R x ), V is the eigenvalue obtained by eigenvalue decomposition, and D is the eigenvector obtained by eigenvalue decomposition.

[0054] 2. Extracting interference spatial feature vector to calculate interference suppression blocking matrix

[0055] According to the noise noise power P n , the feature value V greater than kP n corresponding to the feature vector D is taken as the interference space feature vector Dj, and k=30 in the embodiment.

[0056] Calculate the interference suppression blocking matrix: Φ = I M×M -D j ×[(D j ×D j H )\D j H ]

[0057] 3. Calculate the target echo after interference suppression based on the blocking matrix

[0058] According to the target range gate detected by the conventional ADBF processing, the echo S corresponding to the target range gate is extracted t , which is the echo corresponding to the 300th range gate in S. The interference suppression based on the blocking matrix is performed to obtain the target echo after interference suppression based on the blocking matrix y = Φ × S t .

[0059] 4. Calculate the angle super-resolution steering vector set reconstructed based on the interference suppression blocking matrix

[0060] Discretize the main lobe range [-1.8° 1.8°] uniformly in space to obtain N = 32 angles, and obtain the main lobe range angle discrete array Az = [Az1...Az n ...Az N ]. The conventional multi-channel spatial steering vector calculation method is used to obtain the conventional spatial steering vector set Ψ = [fsub(Az1)...fsub(Az n )...fsub(Az N )], wherein fsub(x) is a conventional multi-channel spatial steering vector calculation function, and the calculation result is an M × 1 vector.

[0061] The angle super-resolution steering vector set A = Φ × Ψ is obtained based on the interference suppression blocking matrix reconstruction.

[0062] 5. IAA algorithm is used to solve and realize angle super-resolution in an interference environment

[0063] The angle super-resolution steering vector set reconstructed based on the interference suppression blocking matrix is taken as the basis, the target echo after interference suppression based on the blocking matrix is taken as the fitting target, and the IAA super-resolution algorithm is used to solve and realize angle super-resolution in an interference environment to obtain the super-resolution processing result P. The detailed solving algorithm is as follows:

[0064] A. Initialize the vector P: where k = 1 2...N

[0065] B, calculate matrix R = A x diag (P) x A H + P n x I M×M , diag (*) is a function of constructing a diagonal matrix;

[0066] C, iteratively calculate vector P:

[0067] where k = 1 2...N;

[0068] Repeat B-C until P converges, and obtain the super-resolution processing result P

[0069] The embodiment adopts the blocking matrix to suppress interference while preserving the spatial freedom, and on this basis, the main lobe echo angle super-resolution is realized through the IAA algorithm, so that the angle super-resolution problem in the interference environment can be effectively solved. Figure 2 The embodiment effect diagram of the application is given, the radar system beam width is about 1.8°, 64 array elements are evenly divided into 16 sub-arrays, two targets with an interval of 1° are effectively distinguished, while suppressing the sidelobe interference, the angle resolution is about 0.6 times the beam width.

[0070] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited to this, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A blocking matrix based multi-channel anti-jamming angular super-resolution method, characterized in that, The method comprises the following steps: Step 1, obtaining an interference covariance matrix according to radar echo data; Step 2, performing eigenvalue decomposition on the interference covariance matrix to calculate eigenvalues and corresponding eigenvectors; Step 3, extracting interference spatial eigenvectors and calculating an interference suppression blocking matrix; Step 4, processing target echoes by the interference suppression blocking matrix to obtain interference-suppressed target echoes, and reconstructing a conventional spatial steering vector set based on the interference suppression blocking matrix to obtain an angle super-resolution steering vector set; Step 5, using the angle super-resolution steering vector set as a basis and using an IAA algorithm to solve the angle super-resolution of the interference-suppressed target echoes.

2. The blocking matrix based multichannel anti-jamming angular super-resolution method according to claim 1, characterized in that, The interference covariance matrix R in step 1 x = Sj x Sj H / L, S is radar echo data, Sj is the echo corresponding to the non-target distance gate in S, H is the conjugate transpose, and L is the number of interference samples.

3. The blocking matrix based multichannel anti-jamming angular super-resolution method according to claim 2, characterized in that, The eigenvalue decomposition of the interference covariance matrix in Step 2 is performed by using an EIG algorithm [V, D] = eig(R x ) Wherein, V is an eigenvalue obtained by eigenvalue decomposition, and D is a corresponding eigenvector obtained by eigenvalue decomposition.

4. The blocking matrix based multichannel anti-jamming angular super resolution method of claim 3, wherein, In step 3, the feature vector D corresponding to the feature value V greater than εP n is extracted as the interference space feature vector Dj, where ε is a constant. n In step 3, the feature vector D corresponding to the feature value V greater than εP n is extracted as the interference space feature vector Dj, where ε is a constant. n 5. The blocking matrix based multichannel anti-jamming angular super-resolution method according to claim 4, characterized in that, The interference suppression blocking matrix Φ = I in step 3 M×M - D j × [(D j × D j H ) D j H ], where I M*M is the identity matrix.

6. The blocking matrix based multichannel anti-jamming angular super-resolution method of claim 2, wherein, In step 4, the target distance gate obtained in step 3 is detected according to a conventional ADBF process, and echoes corresponding to the distance gate where the target is located are extracted t , and interference suppression is performed on S t by a blocking matrix to obtain target echoes after interference suppression based on the blocking matrix, y = Φ × S t .

7. The blocking matrix based multichannel anti-jamming angular super resolution method of claim 6, wherein, The conventional multi-channel spatial steering vector calculation method is used in step 4 to obtain a conventional spatial steering vector set Ψ = [fsub(Az1)...fsub(Az n )...fsub(Az N )], wherein fsub(x) is a conventional multi-channel spatial steering vector calculation function, N is the number of uniformly discrete angles in space, and Az is a main lobe range angle discrete array, Az = [Az1...Az n ...Az N ]. The angle super-resolution steering vector set A is reconstructed based on the interference suppression blocking matrix.

8. The blocking matrix based multichannel anti-jamming angular super resolution method of claim 6, wherein, The angle super-resolution solving algorithm of the interference-suppressed target echoes in Step 4 is as follows: A, initialization vector P: where k = 1 2... N B, compute the matrix R = A x diag(P) x A H + P n x I M×M diag(*) is a function that constructs a diagonal matrix. C, iteratively calculating a vector P: P(k) = aky x aky H , where k = 1 2... N; Repeat B-C until P converges, and obtain a super-resolution processing result P.

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