Constrained complex correntropy applied to adaptive beamforming in non-Gaussian noise environment

This paper introduces a novel constrained maximum complex correntropy criterion (CMCCC) for adaptive beamforming. The work addresses the reception of the desired signal in the presence of non-Gaussian noise sources by leveraging the CMCCC within the beamforming framework. It is essential to highlight that correntropy, a similarity function that can extract high-order statistical insights from data, has found application in various domains as a cost function, particularly excelling in non-Gaussian noise environments. One recent application involves its utilization in the realm of adaptive beamforming. However, due to the restriction of correntropy to real-valued data, straightforward application to beamforming scenarios involving complex-valued measurements was not feasible. This paper introduces an approach tailored to handle complex-valued data. We provide an analysis of the mean square convergence of the proposed algorithm and derive the stability condition for convergence. Our simulation results indicate the effectiveness and superiority of the proposed CMCCC method, which maintains robustness against impulsive outliers while achieving superior performance compared to conventional adaptive beamformers.