Robust adaptive beamforming based on covariance matrix reconstruction and SV estimation

Abstract

In this research, we propose an optimized robust Capon beamforming (RCB) algorithm, which is based on covariance matrix reconstruction along with steering vector (SV) estimation. It is crucial to assurance the robustness of the beamforming algorithm performs better. The proposed algorithm indicates an alternative method to roughly estimate the SV of the interference signal, thus we are able to construct the optimal Capon space spectrum. Secondly, the reconstructed interference signal covariance matrix is obtained by integrating the optimized Capon space power spectrum in the corresponding sector of the interference signal. Subsequently, we adopt the eigen decomposition of the sample covariance matrix (SCM) to obtain the noise covariance matrix, as well as complete the reconstruction of the interference-plus-noise covariance matrix (IPNCM). Finally, this paper optimizes the algorithm of uncertainty set constraint, as well as gets more accurate desired signal SV. The numerical simulation results obtained through two hundred Monte-Carlo experiments suggest that the proposed algorithm indicates higher robustness, better noise suppression ability, and higher output SINR, which is closer to the optimal output curve than several traditional RAB algorithms.