An Efficient and Robust Algorithm for Uniform Linear Array Adaptive Beamforming

Adaptive beamformers are sensitive to model mismatch when the desired signal is present in training snap-shots. Hence, removing the desired signal from the training snapshots and reconstructing interference-plus-noise covariance matrix (INCM) are the key to improve the performance of adaptive beamforming. In this paper, we first divide the uniform linear array (ULA) into two sub-aperture arrays, and remove the desired signal according to phase relationship between two sub-aperture array signal. The INCM of sub-aperture array is reconstructed by estimating interference subspace and powers. The weight vector of sub-aperture array is calculated by minimum variance distortion-less response (MVDR). Then, we analyze the mapping relationship between the sub-aperture array and the full-aperture array to derive the extension transformation matrix. The weight vector of full-aperture array is obtained by the extension transformation of the sub-aperture array weight vector. Requiring no prior information of the direction of arrival (DOA) of interference, the proposed algorithm is robust to model mismatch and coherent interference. The superiority and effectiveness of the proposed algorithm are verified by simulation analysis.