Distributed Kalman filtering for time-varying discrete sequential systems

Discrete sequential system (DSS) consisting of different dynamical subsystems is a sequentially-connected dynamical system, and has found applications in many fields such as automation processes and series systems. However, few results are focused on the state estimation of DSSs. In this paper, the...

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Main Authors: Chen, Bo, Hu, Guoqiang, Ho, Danied W. C., Yu, Li
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2020
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Online Access:https://hdl.handle.net/10356/138770
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1387702020-05-12T08:30:16Z Distributed Kalman filtering for time-varying discrete sequential systems Chen, Bo Hu, Guoqiang Ho, Danied W. C. Yu, Li School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Distributed Kalman Filtering Stability Analysis Discrete sequential system (DSS) consisting of different dynamical subsystems is a sequentially-connected dynamical system, and has found applications in many fields such as automation processes and series systems. However, few results are focused on the state estimation of DSSs. In this paper, the distributed Kalman filtering problem is studied for time-varying DSSs with Gaussian white noises. A locally optimal distributed estimator is designed in the linear minimum variance sense, and a stability condition is derived such that the mean square error of the distributed estimator is bounded. An illustrative example is given to demonstrate the effectiveness of the proposed methods. NRF (Natl Research Foundation, S’pore) 2020-05-12T08:30:16Z 2020-05-12T08:30:16Z 2019 Journal Article Chen, B., Hu, G., Ho, D. W. C., & Yu, L. (2019). Distributed Kalman filtering for time-varying discrete sequential systems. Automatica, 99, 228-236. doi:10.1016/j.automatica.2018.10.025 0005-1098 https://hdl.handle.net/10356/138770 10.1016/j.automatica.2018.10.025 2-s2.0-85056282512 99 228 236 en Automatica © 2018 Elsevier Ltd. All rights reserved.
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Distributed Kalman Filtering
Stability Analysis
spellingShingle Engineering::Electrical and electronic engineering
Distributed Kalman Filtering
Stability Analysis
Chen, Bo
Hu, Guoqiang
Ho, Danied W. C.
Yu, Li
Distributed Kalman filtering for time-varying discrete sequential systems
description Discrete sequential system (DSS) consisting of different dynamical subsystems is a sequentially-connected dynamical system, and has found applications in many fields such as automation processes and series systems. However, few results are focused on the state estimation of DSSs. In this paper, the distributed Kalman filtering problem is studied for time-varying DSSs with Gaussian white noises. A locally optimal distributed estimator is designed in the linear minimum variance sense, and a stability condition is derived such that the mean square error of the distributed estimator is bounded. An illustrative example is given to demonstrate the effectiveness of the proposed methods.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Chen, Bo
Hu, Guoqiang
Ho, Danied W. C.
Yu, Li
format Article
author Chen, Bo
Hu, Guoqiang
Ho, Danied W. C.
Yu, Li
author_sort Chen, Bo
title Distributed Kalman filtering for time-varying discrete sequential systems
title_short Distributed Kalman filtering for time-varying discrete sequential systems
title_full Distributed Kalman filtering for time-varying discrete sequential systems
title_fullStr Distributed Kalman filtering for time-varying discrete sequential systems
title_full_unstemmed Distributed Kalman filtering for time-varying discrete sequential systems
title_sort distributed kalman filtering for time-varying discrete sequential systems
publishDate 2020
url https://hdl.handle.net/10356/138770
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