Diffusion Maximum Correntropy Criterion Algorithms for Robust Distributed Estimation

TL;DR

This paper introduces robust diffusion adaptive estimation algorithms based on the maximum correntropy criterion (MCC) for distributed estimation in impulsive noise environments, outperforming traditional methods.

Contribution

It develops novel MCC-based diffusion algorithms for distributed estimation, providing convergence analysis and improved robustness over existing methods in non-Gaussian noise.

Findings

Outperforms diffusion least mean p-power (DLMP) algorithms.

Outperforms diffusion minimum error entropy (DMEE) algorithms.

Provides convergence analysis for the proposed algorithms.

Abstract

Robust diffusion adaptive estimation algorithms based on the maximum correntropy criterion (MCC), including adaptation to combination MCC and combination to adaptation MCC, are developed to deal with the distributed estimation over network in impulsive (long-tailed) noise environments. The cost functions used in distributed estimation are in general based on the mean square error (MSE) criterion, which is desirable when the measurement noise is Gaussian. In non-Gaussian situations, such as the impulsive-noise case, MCC based methods may achieve much better performance than the MSE methods as they take into account higher order statistics of error distribution. The proposed methods can also outperform the robust diffusion least mean p-power(DLMP) and diffusion minimum error entropy (DMEE) algorithms. The mean and mean square convergence analysis of the new algorithms are also carried out.