阅读说明：本技术 基于子空间方法的干扰协方差矩阵重构的稳健波束形成方法 (Stable beam forming method for interference covariance matrix reconstruction based on subspace method ) 是由 潘涛 于 2020-07-06 设计创作，主要内容包括：本发明涉及一种基于子空间方法的干扰协方差矩阵重构的稳健波束形成方法,包括以下步骤：对样本协方差矩阵进行特征值分解,求得采样信号协方差矩阵的信号子空间；根据信号的导向矢量估计值,计算所有信号的协方差矩阵；计算干扰加噪声协方差矩阵和波束形成权向量；本发明中改进的稳健自适应波束形成方法有效可行,性能可靠。(The invention relates to a steady beam forming method for interference covariance matrix reconstruction based on a subspace method, which comprises the following steps: performing eigenvalue decomposition on the sample covariance matrix to obtain a signal subspace of the sampling signal covariance matrix; calculating covariance matrixes of all signals according to the guide vector estimation values of the signals; calculating an interference plus noise covariance matrix and a beam forming weight vector; the improved robust adaptive beam forming method is effective and feasible and has reliable performance.)

1. A steady beam forming method for interference covariance matrix reconstruction based on a subspace method is characterized by comprising the following steps:

step 1: receiving an echo of a target to be detected by using a radar;

step 2: reconstructing the covariance matrix of each target echo signal to obtain each matrix signal subspace; performing eigenvalue decomposition on the sample covariance matrix to obtain a signal subspace of the sampling signal covariance matrix;

step 2-1: reconstructing to obtain covariance matrix of echo signals in each direction

wherein the content of the first and second substances,nominal steering vector, p, in the theta direction_iIs the ith sampling point of the angle area of the p-th target incoming wave direction, I is the number of the sampling points,

step 2-2: find out

The space corresponding to the eigenvectors corresponding to the J larger eigenvalues is B_S：

B_S＝[b₁,b₂,…,b_J]

Wherein b is_mIs p_mCorresponding feature vector, thenOf the signal subspace S_pComprises the following steps:

j satisfies:

wherein rho is a set constant and satisfies that rho is more than 0 and less than 1;

step 2-3: find outOf the signal subspace S_x: to pairDecomposing the characteristic value to obtain the characteristic value lambda_mM is 1,2, … M, M is array element number, and has lambda₁≥λ₂≥…≥λ_M；

The space corresponding to the eigenvector corresponding to the D larger eigenvalues is U_S：

U_S＝[V₁,V₂,…,V_D]

Wherein V_mIs λ_mCorresponding feature vector, thenOf the signal subspace S_xComprises the following steps:

d satisfies the following conditions:

wherein the constant is a set constant and satisfies 0 < 1;

and step 3: reconstructing a signal covariance matrix according to the guide vector estimation value of each signal;

step 3-1: get the steering vectors for all signals:

in the formula (I), the compound is shown in the specification,representing the eigenvector corresponding to the maximum eigenvalue of the matrix;

step 3-2: reconstructing covariance matrixes of all target echo signals:

in the formula (I), the compound is shown in the specification,is a sample covariance matrix;

and 4, step 4: obtaining covariance matrixes of all signals, and calculating an interference and noise covariance matrix;

when the z-th target echo signal is an expected signal, and other targets are interference, calculating an interference-plus-noise covariance matrix:

the interference plus noise covariance matrix is:

in the formula I_MIs a matrix of the units,for noise energy, use

and 5: obtaining a weight vector using the interference-plus-noise covariance matrix and the desired signal steering vector:

2. a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of claim 1 are implemented when the computer program is executed by the processor.

3. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method as claimed in claim 1.

Technical Field

The invention belongs to the technical field of radar, and particularly relates to a steady beam forming algorithm for interference covariance matrix reconstruction based on a subspace method.

Background

In practical applications, the theoretical covariance matrix of the received data is unknown, and is generally replaced by the sample covariance matrix, but the sample covariance matrix is susceptible to the number of samples, thereby causing covariance matrix errors. For the steering vector of the expected signal, it is a vector function that characterizes the array structure and the incoming wave direction of the expected signal, so it is often affected by some adverse factors such as a priori knowledge, process accuracy and environmental transformation, and a certain degree of error occurs. The standard Capon beamformer is highly sensitive to covariance matrix errors and steering vector errors caused by these non-idealities, and tends to suffer drastic performance degradation, even worse than that of the static beamformer. This is because under these non-ideal conditions, there is a risk that the desired signal is suppressed as interference (this phenomenon is called signal "self-cancellation"), and especially when the weight vector is calculated using the sampling covariance matrix containing the desired signal component, the beamforming performance is even worse, and the subsequent signal detection and estimation are directly affected, resulting in an inestimable effect.

Disclosure of Invention

Technical problem to be solved

In order to avoid the defects of the prior art, the invention provides a robust beam forming method for interference covariance matrix reconstruction based on a subspace method.

Technical scheme

A steady beam forming method for interference covariance matrix reconstruction based on a subspace method is characterized by comprising the following steps:

step 1: receiving an echo of a target to be detected by using a radar;

step 2: reconstructing the covariance matrix of each target echo signal to obtain each matrix signal subspace; performing eigenvalue decomposition on the sample covariance matrix to obtain a signal subspace of the sampling signal covariance matrix;

step 2-1: reconstructing to obtain covariance matrix of echo signals in each directionP is the total number of echo signals:

wherein the content of the first and second substances,in the theta directionNominal steering vector, p_iIs the ith sampling point of the angle area of the p-th target incoming wave direction, I is the number of the sampling points,

is a sample covariance matrix;

step 2-2: find outOf the signal subspace S_pTo, for

Decomposing the characteristic value to obtain the characteristic value p_iI is 1,2, … M, M is the number of array elements and is p₁≥p₂≥…≥p_M；

The space corresponding to the eigenvectors corresponding to the J larger eigenvalues is B_S：

B_S＝[b₁,b₂,…,b_J]

Wherein b is_mIs p_mCorresponding feature vector, then

Of the signal subspace S_pComprises the following steps:

j satisfies:

wherein rho is a set constant and satisfies that rho is more than 0 and less than 1;

step 2-3: find outOf the signal subspace S_x: to pair

Decomposing the characteristic value to obtain the characteristic value lambda_mM is 1,2, … M, M is array element number, and has lambda₁≥λ₂≥…≥λ_M；

The space corresponding to the eigenvector corresponding to the D larger eigenvalues is U_S：

U_S＝[V₁,V₂,…,V_D]

Wherein V_mIs λ_mCorresponding feature vector, then

Of the signal subspace S_xComprises the following steps:

d satisfies the following conditions:

wherein the constant is a set constant and satisfies 0 < 1;

and step 3: reconstructing a signal covariance matrix according to the guide vector estimation value of each signal;

step 3-1: get the steering vectors for all signals:

in the formula (I), the compound is shown in the specification,representing the eigenvector corresponding to the maximum eigenvalue of the matrix;

step 3-2: reconstructing covariance matrixes of all target echo signals:

in the formula (I), the compound is shown in the specification,is a sample covariance matrix;

and 4, step 4: obtaining covariance matrixes of all signals, and calculating an interference and noise covariance matrix;

when the z-th target echo signal is an expected signal, and other targets are interference, calculating an interference-plus-noise covariance matrix:

the interference plus noise covariance matrix is:

in the formula I_MIs a matrix of the units,for noise energy, useA minimum eigenvalue approximation of;

and 5: obtaining a weight vector using the interference-plus-noise covariance matrix and the desired signal steering vector:

a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of claim 1 are implemented when the computer program is executed by the processor.

A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method as claimed in claim 1.

Advantageous effects

The invention provides a steady beam forming algorithm for interference covariance matrix reconstruction based on a subspace method, which comprises the following steps: performing eigenvalue decomposition on the sample covariance matrix to obtain a signal subspace of the sampling signal covariance matrix; calculating covariance matrixes of all signals according to the guide vector estimation values of the signals; an interference plus noise covariance matrix and a beamforming weight vector are calculated. The improved robust adaptive beam forming method is effective and feasible and has reliable performance.

Compared with the prior art, its obvious advantage lies in: (1) by the subspace method, approximate errors can be effectively reduced, and the guide vector corresponding to each target is obtained at higher precision; (2) the desired Signal (SOI) component in the sampled covariance matrix can be eliminated substantially.

Drawings

Fig. 1 is a flow chart of the robust beamforming algorithm for interference covariance matrix reconstruction based on the subspace approach of the present invention.

Figure 2 is an algorithm normalized pattern of the present invention.

Fig. 3 is a graph of the output SINR versus SNR of the present invention.

Fig. 4 is a graph of output SINR versus DOA mismatch for the present invention.

Detailed Description

The invention will now be further described with reference to the following examples and drawings:

with reference to fig. 1, a robust beamforming algorithm based on interference plus noise covariance matrix reconstruction according to the present invention includes the following steps:

step 1, receiving an echo signal by using a radar;

step 2, carrying out eigenvalue decomposition on the sample covariance matrix to obtain a signal subspace of the sampling signal covariance matrix;

2-1, reconstructing to obtain covariance matrix of echo signals in each directionP is the total number of echo signals.

WhereinNominal steering vector, p, in the theta direction_iIs the ith sampling point of the angle area of the p-th target incoming wave direction, I is the number of the sampling points,

is a sample covariance matrix.

Step 2-2, obtainingOf the signal subspace S_p. To pairDecomposing the characteristic value to obtain the characteristic value p_mM is 1,2, … M, M is array element number, p is₁≥p₂≥…≥p_M。

The space corresponding to the eigenvectors corresponding to the J larger eigenvalues is B_S：

B_S＝[b₁,b₂,…,b_J]

Wherein b is_mIs p_mCorresponding feature vector, then

Of the signal subspace S_pComprises the following steps:

j satisfies:

wherein rho is a set constant and satisfies the condition that rho is more than 0 and less than 1.

Step 2-3, obtaining

Of the signal subspace S_x. To pairDecomposing the characteristic value to obtain the characteristic value lambda_iWhere i is 1,2, … M, M is the number of array elements, and λ is₁≥λ₂≥…≥_M。

The space corresponding to the eigenvector corresponding to the D larger eigenvalues is U_S：

U_S＝[V₁,V₂,…,V_D]

Wherein V_mIs λ_mCorresponding feature vector, thenOf the signal subspace S_xComprises the following steps:

d satisfies the following conditions:

wherein the constant is set to satisfy 0 < 1.

Step 3, according to S_pAnd S_xRespectively obtain PA covariance matrix of the signals;

step 3-1, obtaining the guide vectors of all signals

In the formula (I), the compound is shown in the specification,

and representing the eigenvector corresponding to the maximum eigenvalue of the matrix.

Step 3-2, accurately reconstructing covariance matrixes of all target echo signals:

in the formula (I), the compound is shown in the specification,is a sample covariance matrix.

Step 4, when the echo signal of the z-th target is an expected signal, other targets are interference, and an interference-plus-noise covariance matrix is calculated

The interference plus noise covariance matrix is:

in the formula I_MIs a matrix of the units,for noise energy, useIs approximated by the minimum eigenvalue of.

And 5, obtaining a weight vector.

The desired signal has an incoming wave direction of theta ₁0 °, interference is θ₂Fast beat count is 200 at-30 °, I is set to 50 at step 2-1, the desired signal DOA is mismatched by 5 °, and the beam pattern is as shown in fig. 2. As can be seen from the figure, the proposed algorithm can align the main beam direction to the desired signal, where OPT is the optimal beamforming algorithm based on the maximum output SINR criterion, DL is the diagonal loading algorithm, and MVDR is the MVDR algorithm.

To verify the variation of algorithm output SINR with SNR, the SNR was set to vary from-10 dB to 20dB, and the dry-to-noise ratio INR was 20 dB. The output SINR versus SNR varies as shown in fig. 3. As can be seen from the figure, the output SINR of the proposed algorithm increases as the SNR increases.

To verify the performance of the proposed algorithm output SINR at DOA mismatch, DOA mismatch error is set to vary from-5 ° to 5 °, input SNR 15dB, INR 20 dB. The output SINR as a function of DOA mismatch error is shown in fig. 4. As can be seen from the figure, the output SINR of the proposed algorithm is not affected by DOA mismatch error and thus has robustness.