Distributed fusion filter over lossy wireless sensor networks with the presence of non-Gaussian noise

TL;DR

This paper develops a distributed state estimation method for wireless sensor networks that effectively handles non-Gaussian noise and packet loss caused by DoS attacks and environmental factors, ensuring reliable data fusion.

Contribution

It introduces a generalized packet drop model and extends the maximum correntropy Kalman filter to a distributed form with packet drop handling, which is novel for non-Gaussian noise and lossy networks.

Findings

The proposed DM-MCKF-DPD algorithm converges under certain conditions.

Simulations demonstrate improved estimation accuracy in lossy, noisy environments.

The method is computationally feasible for practical WSN applications.

Abstract

The information transmission between nodes in a wireless sensor networks (WSNs) often causes packet loss due to denial-of-service (DoS) attack, energy limitations, and environmental factors, and the information that is successfully transmitted can also be contaminated by non-Gaussian noise. The presence of these two factors poses a challenge for distributed state estimation (DSE) over WSNs. In this paper, a generalized packet drop model is proposed to describe the packet loss phenomenon caused by DoS attacks and other factors. Moreover, a modified maximum correntropy Kalman filter is given, and it is extended to distributed form (DM-MCKF). In addition, a distributed modified maximum correntropy Kalman filter incorporating the generalized data packet drop (DM-MCKF-DPD) algorithm is provided to implement DSE with the presence of both non-Gaussian noise pollution and packet drop. A sufficient condition to ensure the convergence of the fixed-point iterative process of the DM-MCKF-DPD algorithm is presented and the computational complexity of the DM-MCKF-DPD algorithm is analyzed. Finally, the effectiveness and feasibility of the proposed algorithms are verified by simulations.