Variable Kernel Width Algorithm in Constrained Maximum Complex Correntropy Criterion for Adaptive Beamforming

This paper introduces a novel adaptive beamforming approach for complex-valued data based on the variable kernel width maximum complex correntropy criterion. The performance of algorithms based on the maximum complex correntropy criterion is susceptible to the choice of kernel width. If the kernel width is too small, the algorithm may not effectively handle noise and outliers, leading to poor performance. Conversely, if the kernel width is too large, the criterion may smooth over essential details in the data, resulting in loss of information and suboptimal performance. To address this challenge, we propose a method termed constrained maximum complex correntropy with variable kernel width that dynamically adjusts the kernel width based on the maximum complex correntropy criterion, allowing for effective suppression of interference while enhancing the reception of signals from desired directions. The proposed technique is evaluated through simulations and compared against existing methods in various scenarios, such as impulsive, Gaussian, and Laplace noise, demonstrating its effectiveness in improving signal quality and robustness in challenging environments. This adaptive beamforming approach shows promise for applications in wireless communication, radar systems, and other signal-processing domains where robustness to interference and noise environments is critical.