Beam forming method based on subspace interference-plus-noise covariance matrix reconstruction

Abstract

The invention belongs to the field of array signal processing and mainly relates to robustness of a standard Capon self-adaptive beam forming algorithm based on covariance matrix reconstruction to interference signal steering vector errors. The beam forming algorithm based on subspace interference-plus-noise covariance matrix reconstruction comprises the following steps: at first, utilizing matrix received data to estimate the steering vector a(theta d), d=2,3,...,D and the power sigma<2> d(theta d) of all D-1 interference signals and meanwhile estimate the noise power sigma<2>, wherein d=2,3,...,D; then reconstructing an interference-plus-noise covariance matrix R' according to the definition R of the interference-plus-noise covariance matrix; finally, constructing a signal covariance matrix in a relatively small angle range 1; taking the dominant eigenvector of the signal covariance matrix as a desired signal steering vector to estimate a(theta 1); together with the reconstructed R', obtaining a novel beam forming weighing vector W, wherein the formulas of R, R' and W are shown in the description. The beam forming method provided by the invention overcomes the defects of the conventional beam forming algorithm, thereby having good robustness to interference signal steering vector errors.

Description

technical field [0001] The invention belongs to the field of array signal processing, and mainly relates to the robustness of a standard Capon adaptive beamforming algorithm based on covariance matrix reconstruction to interference signal steering vector errors. Background technique [0002] The Capon adaptive beamforming algorithm can minimize the output power of the array and maximize the signal-to-interference-plus-Noise Ratio (SINR) of the beam output and the maximum Maximize the array gain, have better azimuth resolution and strong interference suppression ability. However, Capon beamforming is based on the assumption that both the desired signal steering vector and the interference noise covariance matrix are known accurately, and it is sensitive to the errors of the desired signal steering vector and the interference noise covariance matrix. In practical applications, the interference noise covariance matrix is ​​generally difficult to obtain, and the sample covarian...

AI Technical Summary

Problems solved by technology

This algorithm can approach the optimal performance when the interference signal steering vector has no error, but it also has its own shortcomings. The algorithm relies too much on the prior information of the interference noise structure of the array, and is very sensitive to the interference signal steering vector error, etc.

The main reason is that the algorithm does not reconstruct the interference noise covariance matrix from the defined point of view, so that the correlation coefficient between it and the ideal interference noise covariance matrix is ​​very small, especially when there is an error in the steering vector of the interference signal

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