Prediction-Based Virtual Array Beamforming

Adaptive beamforming using the sample covariance matrix inversion (SMI) method suffers from severe performance degradation in the presence of steering vector (SV) mismatches. Classical robust algorithms based on the sample covariance matrix (SCM) sacrifice the capability of interference suppression to improve main-lobe robustness. In this paper, a prediction-based adaptive beamforming algorithm for virtual arrays is proposed, which can suppress spatial interference while ensuring a distortionless main-lobe response. First, the virtual sensor is constructed, and its observation data are estimated using a prediction algorithm that has noncumulative errors and real array observation data. Then, the interference-plus-noise covariance matrix (INCM) of the extended array is reconstructed within the airspace sector, and finally, the extended SV is estimated by the eigenvector corresponding to the largest eigenvalue. Simulation results indicate that the proposed algorithm can reduce the impact of interference on the output while improving main-lobe robustness, thereby improving the output signal-to-interference-plus-noise ratio (SINR).