Robust interference-plus-noise covariance matrix reconstruction method

Abstract

The invention belongs to the field of array signal processing and mainly relates to robustness of an interference-plus-noise covariance matrix reconstruction algorithm as well as robustness of optimal Capon adaptive beamforming based on the worst-case performance of the interference-plus-noise covariance matrix reconstruction algorithm. The algorithm comprises, firstly, establishing error models of steering vectors of desired signals and interference signals through worst-case performance optimization standard; secondly, estimating the power and the steering vector of a (D-1)th interference signal through Robust Capon beamforming, and meanwhile, performing EVD (evolution-data optimization) on a sample covariance matrix to estimate array receiving Gaussian white noise power and accordingly to a reconstructed interference-plus-noise covariance matrix which takes interference signal steering vector errors into consideration. The interference-plus-noise covariance matrix reconstruction algorithm can effectively solve the inherent shortcomings of summation type interference-plus-noise covariance matrix reconstruction and improve the robustness of beamforming algorithms.

Description

technical field [0001] The invention belongs to the field of array signal processing, and mainly relates to the robustness of an interference noise covariance matrix reconstruction algorithm and the robustness of the optimal Capon adaptive beamforming based on the worst performance of the algorithm. Background technique [0002] The Capon adaptive beamforming algorithm can minimize the output power of the array and maximize the output Signal-to-Interference-plus-Noise Ratio (SINR), 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, and in practical applications , the interference noise covariance matrix is ​​generally difficult to obtain,...

AI Technical Summary

Problems solved by technology

However, in the interval Θ 2 Integral calculation is very complicated, in order to effectively reduce the amount of calculation, the interval Θ 2 Perform interval discretization into an angle set containing L elements The requirement for interval discretization is to include the direction of arrival of each interference signal, that is, Use discrete intervals Sums instead of intervals Θ 2 Integral to obtain the reconstruction of the interference noise covariance matrix

[0005] However, the INCMR algorithm has its inherent shortcomings, its discrete interval Must include all interfering signal incoming wave signal direction angles which is The summation term in must include all interfering signal terms This requires accurate knowledge of the steering vectors a(θ d ), d=2,...,D, otherwise the algorithm performance will be greatly reduced; but in practical applications, the steering vector of the interference signal is unknown, and it needs to be estimated by a method similar to the estimation of the desired signal steering vector

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