Robust Adaptive Beamforming Anti-interference Method Based on Multiple Covariance Matrix Reconstruction and Steering Vector Estimation

When the signal steering vector (SV) mismatches or the interference plus noise covariance matrix (INCM) is contaminated by desired signal due to the limited number of snapshots, the interference suppression performance is severely degraded. In this paper, a robust adaptive beamforming method based on multiple covariance matrix reconstruction and desired signal steering vector (DSSV) estimation is proposed. This method reconstructs three covariance matrices. Firstly, minimizes the dispersion extent of the noise eigenvalues to reconstruct the data covariance matrix. Then reconstructs the signal covariance matrix by Capon power spectrum integration and estimates the DSSV. Finally, removes the signal covariance matrix from data covariance matrix to reconstruct INCM. The simulation results show that the proposed algorithm converges faster, is robust to the array model mismatch and various errors, which proves the effectiveness and superiority of the proposed method.