Constrained Normalised Maximum Complex Correntropy Criterion for Adaptive Beamforming

This paper proposes the constrained normalized maximum complex correntropy criterion CNMCCC, which introduces a normalization approach to enhance stability and ensure convergence by mitigating the effects of input power variations. Additionally, the algorithm incorporates a set of linear constraints that ensure the desired signal is passed without distortion while minimizing interference from other directions. By optimizing the correntropy between the desired and output signals, the proposed method significantly improves beamforming performance in complex environments where traditional methods are less effective. Through theoretical analysis and simulations, we demonstrate that the beamformer based on the proposed algorithm outperforms classical algorithms in robustness, interference suppression, and convergence speed. The results confirm that it is well-suited for adaptive beamforming applications in non-Gaussian, and interference-prone environments.