Adaptive beamforming algorithms with robustness against steering vector mismatch of signals

Abstract

Three novel adaptive beamforming algorithms are developed to mitigate the steering vector (SV) random errors of both the desired signal and interferences. With the use of iterative robust Capon beamforming technique, each interference's SV is estimated first, and then the interference-plus-noise covariance matrix is reconstructed according to its definition. To accurately estimate the signal of interest's (SOI's) SV, the authors introduce three new methods, which are based on the techniques of the second-order cone programming, the eigendecomposition and the oracle approximating shrinkage. For the proposed beamformers, only the prior information about the array geometry and the SOI's angular sector are needed. The main advantage is that the proposed methods are robust against a variety of SV mismatches. Simulation results are presented to demonstrate the robustness and effectiveness of the proposed adaptive beamformers.