Robust constrained-LMS adaptive beamforming algorithm

Abstract

The existing adaptive beamforming algorithms are known to degrade if some of underlying assumptions on the environment, sources, or sensor array become violated and this may cause 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. In this paper, on the basis of constrained-LMS (CLMS) algorithm, we propose a robust constrained-LMS (RCLMS) algorithm. Our robust constrained-LMS algorithm provides excellent robustness against the desired signal mismatches, offers fast convergence rate and makes the mean output array SINR consistently close to the optimal one. Computer simulations show better performance of our RCLMS algorithm as compared with the classical CLMS algorithm.