Adaptive Beamforming Based on Linearly Constrained Maximum Correntropy Learning Algorithm

Abstract

The Gaussian noise profile has been demonstrated<br><br>to be an inaccurate model in several antenna beamforming<br><br>problems. Many available beamformers are based on secondorder<br><br>statistics and their efficiency degrades significantly due<br><br>to impulsive noise existed in the received signal. Hence, a<br><br>demand exists for attention to address beamforming problems<br><br>under nonGaussian noise environments. According to the robust<br><br>performance of information theoretic learning (ITL) criteria in<br><br>nonGaussian environments, we propose a linearly constrained<br><br>version of maximum correntropy learning algorithm in order<br><br>to solve beamforming problem in presence of nonGaussian and<br><br>impulsive noises. Simulation results of the proposed adaptive<br><br>beamformer are provided to illustrate its accurate and resistant<br><br>performance in comparison with conventional second-ordermoment-<br><br>based beamformers.