Distributed robust Kalman filtering with unknown and noisy parameters in sensor networks
type: Item
instance of: scholarly article
title: Distributed robust Kalman filtering with unknown and noisy parameters in sensor networks
zbMATH Open document ID: 1417.93308
DOI: 10.1155/2018/7954263
author: Dong-Hua Chen
author: Ya Zhang
author: Cheng-Lin Liu
author: Yang-Yang Chen
published in: Discrete Dynamics in Nature and Society
publication date: 2019-02-20T00:00:00Z
review text: Summary: This paper investigates the distributed filtering for discrete-time-invariant systems in sensor networks where each sensor's measuring system may not be observable, and each sensor can just obtain partial system parameters with unknown coefficients which are modeled by Gaussian white noises. A fully distributed robust Kalman filtering algorithm consisting of two parts is proposed. One is a consensus Kalman filter to estimate the system parameters. It is proved that the mean square estimation errors for the system parameters converge to zero if and only if, for any one system parameter, its accessible node subset is globally reachable. The other is a consensus robust Kalman filter to estimate the system state based on the system matrix estimations and covariances. It is proved that the mean square estimation error of each sensor is upper-bounded by the trace of its covariance. An explicit sufficient stability condition of the algorithm is further provided. A numerical simulation is given to illustrate the results.
Mathematics Subject Classification ID: 93E11
Mathematics Subject Classification ID: 62M20
zbMATH DE Number: 7026689
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/7954263
OpenAlex ID: W2902773765
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Wikidata QID: Q128892908
description: scientific article; zbMATH DE number 7026689