Fully automatic robust adaptive beamforming via Principal Component Regression

In this paper, a novel robust adaptive beamformer based on principal component regression (PCR) is derived. Unlike many existing methods, the proposed method is completely automatic (or so-called parameter-free), which means, it do not need the choice of user parameters. The performance of our approach is illustrated by numerical simulations and compared to other robust adaptive beamformers. The simulation results show that our method is robust against errors on the steering vector and the sample covariance matrix, and meanwhile gives high signal-to-interference-plus-noise ratio (SINR).