Robust adaptive beamformers: A beampattern shaping approach

Abstract

Adaptive beamformers can degrade drastically in practice because they are highly sensitive to mismatches between the exploited assumptions and the actual characteristics of the sensor array, environment and sources. At low snapshots, the sidelobes can get very high due to errors in the array covariance matrix. This degrades the signal-to-interference-plus-noise-ratio (SINR). Recently, a robust adaptive beamformer based on worst-case optimization (RAB-WC), has reformulated beamforming as a second-order cone programming (SOCP) problem. It exhibits adaptive interference-rejection capability and robustness against large steering direction errors. However, the sidelobes in RAB-WC are indirectly controlled by a regularization factor and it is not clear how the factor value should be selected. In this paper, we propose a new framework based on the use of a set of beampattern shaping constraints. With this framework, the beamformer possesses not only adaptive interference-rejection capability and robustness against large steering direction errors, but also direct sidelobe control simultaneously. Unlike other state-of-the-art robust adaptive beamformers, the proposed beamformer is robust against moving interferences too. The proposed framework is generic and many beamformers can be derived to tackle specific problems.