Robust Beamforming Method Based on Double-layer Reconstruction of Covariance Matrix

Abstract

Focusing on the problem that the performance of traditional adaptive beamformer declines sharply when the covariance matrix contains the target signal component and the mismatch occurs in target steering vector, a robust beamforming algorithm based on double-layer reconstruction of interference-plus-noise covariance matrix is proposed in this paper. Firstly, the sparse reconstruction method is used to estimate the interference-plus-noise covariance matrix. Then the interference-plus-noise covariance matrix is optimized by estimating the interference steering vector and interference power. Secondly, based on subspace theory, an optimization model of steering vector is established, and the convex optimization model is solved by iterative method to obtain the optimal weight vector. The simulation results show that the proposed algorithm can improve the robustness of the beamformer in the case of target vector constraint error and array error. Also, the algorithm performs well in low snapshot number condition, and the output performance is better than current methods.