Robust adaptive beamforming via sparse covariance matrix estimation and subspace projection

In this paper, a new beamformer with improved robustness against the small sample size is proposed. This beamformer first employs the modified sparse Bayesian learning (SBL) algorithm to obtain an accurate estimate of the covariance matrix. To further improve the robustness, subspace projection is implemented subsequently. In addition, due to the inherent decorrelation capability of the SBL algorithm, the proposed beamformer is enabled to suppress correlated or even coherent interferences without preprocessing. Numerical simulation results show that the proposed beamformer outperforms several existing methods with small sample support.