Robust Adaptive Beamforming Using a Bayesian Approach

When adaptive arrays are applied to practical problems, the performances of adaptive beamforming algorithms are known to degrade severely in the presence of uncertainty in the signal look direction. Such uncertainty in source direction-of-arrival (DoA) may often occur in practical situations because some of underlying assumptions on the environment, sources, or sensor array can be violated and this may cause a mismatch between the look direction of the beamformer and the actual DoA of the signal. In this paper, we propose a novel robust adaptive beamforming with a Bayesian approach. The proposed algorithm is based on explicit modeling of uncertainties in the desired signal array response, which provides sufficiently robustness to uncertainty in source DoA, enhances the array system performance under nonideal conditions and makes the mean output array SINR consistently close to the optimal one. Computer simulation results are presented, which show that the proposed algorithm yields significantly better performance as compared with the traditional adaptive beamforming algorithms