An RCB-Like Steering Vector Estimation Method Based on Interference Matrix Reduction

Abstract

To develop an adaptive beamformer against the signal of interest (SOI) steering vector mismatch, a robust Capon beamformer (RCB) like steering vector estimation method based on the interference matrix reduction is proposed. Different from the RCB and its modified versions that optimize the SOI steering vector with the Capon power estimator, this article designs an SOI power estimator to formulate the steering vector optimization problem with an uncertainty set constraint. In terms of that, the unknown SOI covariance matrix is needed to realize the SOI power estimator, an efficient interference matrix reconstruction way via SOI blocking and matrix eigen-transition is exploited to reduce the interference component from the sample covariance matrix. Herein, after solving the given steering vector optimization problem and adding the noise component to the aforesaid interference matrix, the weight vector of the derived algorithm is, thereby, computed using the estimated SOI steering vector and interference covariance matrix. The proposed method only requires the source number and prior direction of the SOI. The numerical simulations show that the proposed approach can outperform the compared ones with reduced complexity in the situation of various steering vector mismatches.