A Robust Beamforming Algorithm Based on Covariance Matrix Reconstruction and Steering Vector Estimation

Abstract

Adaptive beamforming algorithms often suffer from performance degradation due to the presence of the desired signal component in the interference-plus-noise covariance matrix (IPNCM) and mismatch of steering vector. In order to improve the robustness of the beamforming algorithm performance, a new robust adaptive beamforming algorithm is proposed in this paper. In the proposed algorithm, Capon spatial spectrum is first used to reconstruct the IPNCM. Then, based on the robust Capon beamforming algorithm based on the steering vector uncertain set, new constraints are added so that the steering vector does not converge to the interference direction. The proposed beamforming algorithm is a non-convex quadratic constraint quadratic programming (QCQP) problem that is solved using semidefinite programming (SDP) relaxation. Simulation results show that the proposed algorithm is more robust than some other existing algorithms and is closest to the optimal solution.