A Kernel-Width Adaption Diffusion Maximum Correntropy Algorithm

Abstract

Impulsive noises are widely existing in various systems like noise cancellation system and wireless communication systems, where adaptive filtering (AF) is always employed to identify specific systems. Additionally, the impulsive noises will affect the performance for estimating these systems, resulting in slow convergence or worse identification accuracy. In this paper, a diffusion maximum correntropy criterion (DMCC) algorithm with adaption kernel width is proposed, denoting as DMCCadapt algorithm, to find out a solution for dynamically choosing the kernel width. The DMCCadapt algorithm chooses small kernel width at initial stage to improve its convergence speed rate, and uses large kernel width at completion stage to reduce its steady-state error. To render the proposed DMCCadapt algorithm suitable for sparse system identifications, the DMCCadapt algorithm based on proportional coefficient adjustment is realized and named as diffusion proportional maximum correntropy criterion (DPMCCadapt). The theoretical analysis and simulation results are presented to show that the DPMCCadapt and DMCCadapt algorithms have better convergence than the traditional diffusion AF algorithms under impulse noise and sparse systems.