Robust distributed extended Kalman filter based on adaptive multi-kernel mixture maximum correntropy for non-Gaussian systems

TL;DR

This paper introduces a robust distributed extended Kalman filter that uses an adaptive multi-kernel mixture maximum correntropy to improve state estimation accuracy in non-Gaussian noise environments, reducing manual parameter tuning.

Contribution

It proposes a novel multi-kernel mixture correntropy criterion and an adaptive distributed Kalman filter that enhances robustness and reduces manual parameter tuning in non-Gaussian systems.

Findings

Improved accuracy in non-Gaussian noise scenarios

Reduced communication overhead via consensus averaging

Validated effectiveness in power system and vehicle state estimation

Abstract

As one of the most advanced variants in the correntropy family, the multi-kernel correntropy criterion demonstrates superior accuracy in handling non-Gaussian noise, particularly with multimodal distributions. However, current approaches suffer from key limitations-namely, reliance on a single type of sensitive Gaussian kernel and the manual selection of free parameters. To address these issues and further boost robustness, this paper introduces the concept of multi-kernel mixture correntropy (MKMC), along with its key properties. MKMC employs a flexible kernel function composed of a mixture of two Students t-Cauchy functions with adjustable (non-zero) means. Building on this criterion within multi-sensor networks, we propose a robust distributed extended Kalman filter-AMKMMC-RDEKF based on adaptive multi-kernel mixture maximum correntropy. To reduce communication overhead, a consensus averaging strategy is incorporated. Furthermore, an adaptive mechanism is introduced to mitigate the impact of manually tuned free parameters. At the same time, the computational complexity and convergence ability of the proposed algorithm are analyzed. The effectiveness of the proposed algorithm is validated through challenging scenarios involving power system and land vehicle state estimation.