Robust Adaptive Beamforming Based on Norm Constraint Regularization Correntropy for Impulsive Interference

Isotropic beamforming method with dual norm constraints is proposed to reduce the impact of pulsed stable interference and maintain the sparsity of the beamformer weights. The realization of motivation is mainly based on the combination of norm regularization constraints and known maximum correlation criterion (MCC) criterion. First, consider the constraints existing in the constrained least mean square (CLMS) algorithm, combined with MCC to construct a linear constrained optimization equation. In addition, in order to reduce the number of active elements in the limited power array system, improve the directivity performance of the beamformer to improve the steady-state sparsity. We introduce a norm penalty equation in the constraint list of the adaptive filter, so that the smaller coefficient is zero and highlights the globally optimal optimization area. The convexity and stability of the norm can eliminate the instability of sparse performance. The convergence and complexity of the algorithm are analyzed, and the stability conditions of convergence are given. Simulation results show that the proposed method is superior to other existing beamforming techniques.