Robust Capon beamforming in the presence of mismatches

Provided that the array steering vector corresponding to the signal of interest is accurately known, the Capon beamformer has better resolution and much better interference rejection capability than the data-independent beamformer. However, when mismatches between the actual and presumed array responses to the desired signal may frequently occur in practical situations, the performance of the Capon beamformer is known to degrade substantially. The similar type of performance degradation can occur when the signal steering vector is known exactly but the training sample size is small. In this paper, we propose a novel approach to robust Capon beamforming. The proposed robust Capon beamformer provides excellent robustness against some types of mismatches, offers faster convergence rate and makes the mean output array SINR consistently close to the optimal one. Computer simulations show better performance of our proposed robust Capon beamformer than that of the standard Capon beamformer.