Robust recursive least squares adaptive beamforming algorithm

Abstract

When adaptive arrays are applied to practical problems, the performance degradation of adaptive beamforming techniques may become even more pronounced than in ideal cases because some of the underlying assumptions on the environment, sources, or sensor array can be violated and this may cause a mismatch between the presumed and actual signal steering vectors. In fact, the performances of existing adaptive array algorithms are known to degrade substantially in the presence of even slight mismatches between the actual and presumed array responses to the desired signal. Similar types of degradation can take place when the signal array response is known precisely but the training sample size is small. On the basis of the recursive least squares (RLS) algorithm, we propose a novel approach to robust adaptive beamforming. Our robust RLS adaptive beamforming algorithm provides excellent robustness against signal steering vector mismatches and small training sample size, offers faster convergence rate, makes the mean output array SINR consistently close to the optimal one, and improves the unitary mismatch. Computer simulations demonstrate a visible performance gain of the proposed robust RLS algorithm.