State Estimation of Wireless Sensor Networks in the Presence of Data Packet Drops and Non-Gaussian Noise

TL;DR

This paper introduces a novel distributed maximum correntropy Kalman filter that effectively handles data packet drops and non-Gaussian noise in wireless sensor networks, improving state estimation accuracy and robustness.

Contribution

It proposes the DMCKF-DPD algorithm, a new distributed Kalman filter that accounts for packet drops and impulsive noise, with proven convergence and moderate computational complexity.

Findings

The DMCKF-DPD outperforms traditional filters in non-Gaussian noise environments.

The algorithm maintains accuracy despite intermittent observations due to packet drops.

Convergence conditions are established, ensuring reliable performance.

Abstract

Distributed Kalman filter approaches based on the maximum correntropy criterion have recently demonstrated superior state estimation performance to that of conventional distributed Kalman filters for wireless sensor networks in the presence of non-Gaussian impulsive noise. However, these algorithms currently fail to take account of data packet drops. The present work addresses this issue by proposing a distributed maximum correntropy Kalman filter that accounts for data packet drops (i.e., the DMCKF-DPD algorithm). The effectiveness and feasibility of the algorithm are verified by simulations conducted in a wireless sensor network with intermittent observations due to data packet drops under a non-Gaussian noise environment. Moreover, the computational complexity of the DMCKF-DPD algorithm is demonstrated to be moderate compared with that of a conventional distributed Kalman filter, and we provide a sufficient condition to ensure the convergence of the proposed algorithm.