Knowledge-aided adaptive beamforming against signal steering vector mismatch with small sample support

In this paper, a knowledge-aided adaptive beamforming approach against signal steering vector mismatch with small sample support is proposed. First, a combination covariance matrix of the prior covariance matrix, the sample covariance matrix and an identity matrix is obtained by the minimum mean-square error criterion. Then the mismatch between the actual and the presumed signal steering vector is modeled as a worst-case performance optimization constraint. Based on the combination covariance matrix and the constraint, the adaptive beamformer is formulated. The validity of the proposed beamformer is evaluated by simulation results, when the sample support is small and the signal steer vector mismatch exists.