Robust adaptive beamforming via quasi-signal subspace estimation for covariance matrix reconstruction

Abstract

In this paper, a novel narrowband interference-plus-noise covariance matrix (INCM) reconstruction method is proposed for robust adaptive beamforming (RAB). To enhance the effectiveness of the INCM reconstruction, we offer a prior selection of the dimension of a quasi-signal subspace (QSS) based on the signal subspace proximity employing two subsample covariance matrices (SSCMs). The QSS herein can provide estimations of all relevant factors of interference for the INCM. Moreover, by employing the eigenvectors of the SSCM, the noise power can be estimated precisely through a nonlinear regularization scheme. Finally, the signal-of-interest steering vector is corrected by an optimization problem exploiting the reconstructed INCM. The proposed RAB method can capitalize on the benefits of the QSS without prior knowledge of the number of sources. The results of the simulations demonstrate the effectiveness of the proposed method in terms of various mismatches.