A Kernel-Width Adaption Diffusion Maximum Correntropy Algorithm

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 DMCC
_adapt
algorithm, to find out a solution for dynamically choosing the kernel width. The DMCC
_adapt
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 DMCC
_adapt
algorithm suitable for sparse system identifications, the DMCC
_adapt
algorithm based on proportional coefficient adjustment is realized and named as diffusion proportional maximum correntropy criterion (DPMCC
_adapt
). The theoretical analysis and simulation results are presented to show that the DPMCC
_adapt
and DMCC
_adapt
algorithms have better convergence than the traditional diffusion AF algorithms under impulse noise and sparse systems.