Robust adaptive beamforming under quadratic constraint in the presence of mismatches

The performances of the conventional adaptive beamforming algorithms degrade severely in the presence of even slight mismatches between the actual and presumed array responses to the desired signal. Similar types of degradation can occur when the signal array response is known exactly, but the training sample size is small. In this paper, based on explicit modeling of uncertainties in the desired signal array response and data covariance matrix, we propose robust adaptive beamforming algorithm under a quadratic inequality constraint with gradient-descent method. It is shown that the proposed algorithm belongs to the class of diagonal loading approaches, but the diagonal loading term can be precisely calculated based on the given level of uncertainties in the signal array response and data covariance matrix. Our proposed robust adaptive beamforming algorithm provides a significantly improved robustness against the signal steering vector mismatches and small training sample size, has a low complexity cost and makes the mean output array SINR consistently close to the optimal one. Computer simulation results demonstrate substantial performance improvement of our proposed algorithm as compared with the conventional adaptive beamforming algorithm.