A novel robust adaptive beamformer based on worst-case linear optimization

Recently, a novel robust adaptive beamformers with constraints on the array magnitude response based on linear optimization is proposed. The optimization is carried out on the autocorrelation of the array weight instead on the array weight as the conventional beamformers. This kind of adaptive beamformer is able to flexibly control the robust region with a specific ripple. In practice, adaptive beamformers suffer from not only steering direction error, but also many other imperfections. In order to make an adaptive beamformer robust against all kinds of array imperfections, in this paper, we propose a new beamformer based on worst-case optimization. The resultant design possesses superior robustness against arbitrary array imperfections. With the proposed method, a large robust response region and a high signal-to-interference-plus-noise ratio (SINR) enhancement can be achieved.