STAP method based on precision matrix nonlinear shrinkage estimation
A nonlinear and matrix technology, applied in the field of STAP based on precision matrix nonlinear shrinkage estimation, can solve problems such as reducing estimation error, and achieve the effect of improving detection ability and good performance
Active Publication Date: 2017-02-22
TSINGHUA UNIV
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However, the diagonal loading method of Jian Li et al. considers the problem from the perspective of the smallest estimation error of the covariance matrix, and does not consider the problem from the perspective of the accuracy matrix (the inverse of the covariance matrix) that is more closely related to the filtering weight coefficient; and, in When estimating the covariance matrix, it adopts a unified parameter processing method for all eigenvalues of the sample covariance matrix, and uses different parameters for processing different sample eigenvalues, so that it is possible to further reduce the estimation error
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[0039] The present invention will be described in further detail below in conjunction with the accompanying drawings.
[0040] A kind of STAP method based on precision matrix nonlinear shrinkage estimation of the present invention, such as figure 1 shown, including the following steps:
[0041] The first step is to calculate the sample covariance matrix S.
[0042] Suppose the dimension of the received data is p, the number of auxiliary samples is N, and the auxiliary samples are arranged into a p×N-dimensional matrix
[0043] X=[x 1 ,x 2 ,...,x N ]
[0044] Then the sample covariance matrix can be written as:
[0045] S=XX H / N
[0046] The second step is to perform eigendecomposition on the sample covariance matrix S to obtain its eigenvalues {(λ 1 ,...,λ p )} and the corresponding eigenvector {(u 1 ,...,u p )}. After that, if the number of samples is greater than the dimension of the filter, go to the third step and skip the fourth step; if the number of samp...
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The invention discloses a STAP method based on precision matrix nonlinear shrinkage estimation. The method comprises a first step of calculating a sample covariance matrix; a second step of subjecting the sample covariance matrix to feature decomposition to obtain feature values and feature vectors, going to the third step and skipping the fourth step if the number of samples is greater than the number of the dimensionalities of filters, or skipping the third step and going to the fourth step if the number of samples is less than the number of the dimensionalities of filters; a third step of calculating shrinkage values corresponding to respective feature values of the sample covariance matrix, and estimating the precision matrix in combination with the feature vectors of the sample covariance matrix; a fourth step of in two cases that the feature values of the sample covariance matrix are greater than 0 or equal to 0, calculating the shrinkage values and estimating the precision matrix in combination with the feature vectors of the sample covariance matrix; and a fifth step of calculating a STAP filter coefficients according to the estimated precision matrix and processing received data.
Description
technical field [0001] The invention belongs to the technical field of clutter suppression and slow target detection in radar signal processing, and in particular relates to a space-time adaptive processing (Space Time Adaptive Processing, STAP) method based on precision matrix nonlinear shrinkage estimation. Background technique [0002] Space Time Adaptive Processing (STAP) technology is one of the important methods to suppress clutter in airborne radar. The STAP technology usually uses a sample covariance matrix inversion (Sample Matrix Inversion, SMI) method to estimate filter coefficients. When the above method is used, if the expected SINR loss is less than 3dB, the number of independent and identically distributed samples required is twice the dimension of the filter. Due to the large product of time degrees of freedom and space degrees of freedom, the estimation of noise covariance matrix requires a large number of auxiliary samples. In practical applications, the n...
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IPC IPC(8): G01S7/41
CPCG01S7/414
Inventor 汤俊张丹丹朱伟
Owner TSINGHUA UNIV