Name
Title | ||
|---|---|---|
Distributed Maximum Correntropy Linear And Nonlinear Filters For Systems With Non-Gaussian Noises |
Abstract | ||
|---|---|---|
In this paper, we investigate the distributed state estimation of non-Gaussian systems, where every sensor only exchanges information within its neighborhoods in the absence of a fusion center. Taking advantage of the Gaussian correntropy in processing non-Gaussian signals, we first derive a centralised maximum correntropy Kalman filter for linear multi-sensor systems, and then obtain its information form with some approximations. After that, a distributed maximum correntropy information filter is designed to approximate the centralised information filter using the consensus average method, and its extension to nonlinear systems is also provided based on statistical linearization. Simulation results on the typical target tracking example over a sensor network are given to illustrate the effectiveness of the proposed algorithms. (c) 2020 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.sigpro.2020.107937 | SIGNAL PROCESSING |
Keywords | DocType | Volume |
Distributed state estimation, Maximum correntropy criterion, Non-Gaussian noise, Consensus average | Journal | 182 |
ISSN | Citations | PageRank |
0165-1684 | 2 | 0.36 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guoqing Wang | 1 | 75 | 17.84 |
| Ning Li | 2 | 2 | 1.38 |
| Yonggang Zhang | 3 | 87 | 16.11 |