Robust Adaptive Beamforming Based on Interference Covariance Matrix Reconstruction and Steering Vector Estimation

The adaptive beamformer is very sensitive to model mismatches. In order to improve the robustness of the adaptive beamformer against mismatch errors, this paper proposes a novel robust adaptive beamforming (RAB) method. Firstly, we use a subspace-based method to estimate the steering vectors of signal-of-interest (SOI) and interference signals. The essence of this method is that each steering vector (SV) can be derived from the vector located at the intersection of two subspaces. Meanwhile, the estimate of each SV is given in a closed-form expression. Secondly, a simplified power estimation method that is independent of the array structure is used to estimate the interference and noise powers. Then based on the estimated SVs of interference and the powers of interference and noise, a more accurate interference-plus-noise covariance matrix (INCM) is reconstructed according to its definition. Finally, the proposed beamformer is obtained based on the more accurate SOI SV and INCM, which has higher robustness to array model mismatches. Simulation results demonstrate the effectiveness of the proposed RAB method.