Kernel Least Mean $p$-Power Algorithm

Abstract

This letter proposes a novel kernel least mean $p$ -power (KLMP) algorithm for nonlinear system identification in the presence of additive non-Gaussian impulsive noises, modeled by a symmetric $\alpha$ -stable distribution with heavy tail. The KLMP algorithm based on the fractional lower order statistics error criterion can effectively scale down the dynamic recursive weight coefficients affected by the impulsive estimation error to avoid the significant performance degradation. Simulation results demonstrate that the proposed algorithm has favorable convergence properties than the classical kernel least-mean-square algorithm using a conventional error criterion in the non-Gaussian impulsive environment.