Modified Gram-Schmidt orthogonalization of covariance matrix adaptive beamforming based on data preprocessing

Abstract

When the desired signal is mixed in the training data, the conventional Gram-Schmidt orthogonalization of covariance matrix (RGS) adaptive beamforming will result in the desired signal cancellation. Therefore, a modified Gram-Schmidt orthogonalization of covariance matrix (MRGS) adaptive beamforming based on data preprocessing is proposed in this paper. In the proposed algorithm, the training data are firstly preprocessed to remove the desired signal, in the following the corresponding covariance matrix is estimated, and the interference subspace is reconstructed by using the Gram-Schmidt orthogonalization of the columns of modified covariance matrix. Finally, the adaptive weight vector is obtained by orthogonally projecting the quiescent weight vector into the interference subspace. Moreover, the adaptive threshold of the preprocessed data is modified correspondingly for more accurate interference subspace estimation. According to the simulations, it is found that the proposed MRGS adaptive beamforming algorithm can improve the performance significantly.