Minimum Variance Beamforming Based on Covariance Matrix Reconstruction Using Orthogonal Vectors

Minimum Variance Beamforming methods, have a weak performance in situation where error is available in covariance matrix estimation of noise and interference. The presence of the desired signal components in the estimated noise and interference vectors is of important factors of error which significantly reduces the output SINR level of the beamformer. In this paper, in order to make the beamformer robust to the incorrect estimation of the data covariance matrix, a covariance matrix reconstruction method using the orthogonal steer vectors obtained by the Gram Schmidt algorithm along with a diagonal loading is employed. Simulation results show the superiority of the proposed method in the improvement of beam pattern, angle estimation of interferences, and output SINR level, compared to the counterparts.