Multi-sensor optimal information fusion Kalman filter
type: Item
instance of: scholarly article
title: Multi-sensor optimal information fusion Kalman filter
zbMATH Open document ID: 1075.93037
published in: Automatica
publication date: 2004-11-23T00:00:00Z
review text: The result of the maximum likelihood fusion criterion under the normal density function is re-derived as an optimal information fusion criterion weighted by matrices in the linear minimum variance sense. Based on the fusion criterion, an optimal information fusion decentralized Kalman filter with fault tolerance and robustness properties is given for discrete time-varying linear stochastic control systems with multiple sensors and correlated noises. It has a two-layer fusion structure. The first fusion layer has a netted parallel structure to determine the cross covariance between every pair of faultless sensors at each time step. The second fusion layer is the fusion center that fuses the estimates and variances of all local subsystems, and the cross covariance among the local subsystems from the first fusion layer to determine the optimal matrix weights and yield the optimal fusion filter. Simulation examples are given to illustrate that the information fusion decentralized filter with a two-layer fusion structure has a better fault tolerance and robustness properties when a sensor is faulty.
Mathematics Subject Classification ID: 93E11
Mathematics Subject Classification ID: 93C35
zbMATH DE Number: 2117915
zbMATH Keywords: multisensor information fusion
zbMATH Keywords: maximum likelihood
zbMATH Keywords: Kalman filter
zbMATH Keywords: fault tolerance
zbMATH Keywords: cross covariance
zbMATH Keywords: decentralized filter
author: Shuli Sun
author: Zili Deng
reviewed by: Tzvetan Semerdjiev
MaRDI profile type: Publication
full work available at URL: j.automatica.2004.01.014
OpenAlex ID: W2064183516
cites work: Q4010408
cites work: On the track-to-track correlation problem
cites work: Decentralized structures for parallel Kalman filtering
cites work: A survey of design methods for failure detection in dynamic systems
DOI: 10.1016/J.AUTOMATICA.2004.01.014
description: scientific article; zbMATH DE number 2117915