Robust Adaptive Beamforming Based on Covariance Matrix Reconstruction via Steering Vector Estimation
Huichao Yang, Zhongfu Ye
Abstract
The performance of the sample matrix inverse (SMI) beamformer degrades greatly when the signal-to-noise ratio (SNR) increases because the signal of interest (SOI) is mistaken as interferences and suppressed. To avoid this situation, the interference-plus-noise covariance matrix (INCM) is introduced via steering vector (SV) estimation for robust adaptive beamforming (RAB). To avoid the convex optimization for the SV estimation, a vertical error vector is constructed based on the property of subspace in the proposed method, and the SV error neighborhood table is built in advance to lower the computational complexity. Through the Capon spectrum search, the initial directions of the SOI and interference signals are estimated, and more accurate SVs are confirmed through neighborhood optimization in the table. Next, the interference covariance matrix (ICM) is generated by the estimated SVs and the noise covariance matrix (NCM) is obtained by the least-square (LS) solution based on the corrected SVs. Finally, INCM is reconstructed and the weight vector is computed for RAB. The main advantage of the proposed method is robust against unknown arbitrary-type mismatches. Simulation results demonstrate the effectiveness and robustness of the proposed method.