Robust adaptive beamforming based on subspace method

Abstract

The performance of adaptive beamformers is susceptible to the steering vector mismatches and covariance matrix uncertainties. In contrast to traditional subspace methods, the proposed subspace method is robust against model mismatches. The signal-plus-interference covariance matrix (SICM) is reconstructed by projecting the enhanced covariance matrix onto the signal-plus-interference subspace which is formed using the property of covariance matrix. The array steering vector is adjusted by projecting the presumed steering vector onto the signal subspace which is structured using the major eigenvectors of the enhanced covariance matrix. The major eigenvectors are selected when the correlation coefficient between the presumed steering vector and the eigenvector exceeds a certain threshold. By combining the reconstructed SICM with the adjusted array steering vector, the proposed beamformer can provide robust performance. In comparison with many existing robust techniques, the proposed method performs well in both high and low signal-to-noise ratio scenarios. Several examples are carried out to illustrate the superiority of our method.