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Track Fusion Fractional Kalman Filter for the Multisensor Descriptor Fractional Systems

The purpose of this study was to investigate the state estimation problem for the multisensor descriptor fractional systems. Firstly, the descriptor fractional order system was transformed into two nondescriptor fractional order subsystem based on the singular value decomposition method; then, the d...

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Detalles Bibliográficos
Autores principales: Zhang, Bo, Shen, Haibin, Yan, Guangming, Sun, Xiaojun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9433210/
https://www.ncbi.nlm.nih.gov/pubmed/36059401
http://dx.doi.org/10.1155/2022/9637801
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author Zhang, Bo
Shen, Haibin
Yan, Guangming
Sun, Xiaojun
author_facet Zhang, Bo
Shen, Haibin
Yan, Guangming
Sun, Xiaojun
author_sort Zhang, Bo
collection PubMed
description The purpose of this study was to investigate the state estimation problem for the multisensor descriptor fractional systems. Firstly, the descriptor fractional order system was transformed into two nondescriptor fractional order subsystem based on the singular value decomposition method; then, the descriptor fractional Kalman filters for the subsystems were proposed based on projection theory, which effectively solved the state estimation problem of the descriptor fractional order system with singular matrix; on this basis, the track fusion fractional Kalman filter of the multisensor descriptor fractional system is proposed by using the track fusion algorithm. The state estimation accuracy of multisensor descriptor fractional order systems is greatly improved. Simulation results show the effectiveness of the proposed algorithm.
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spelling pubmed-94332102022-09-01 Track Fusion Fractional Kalman Filter for the Multisensor Descriptor Fractional Systems Zhang, Bo Shen, Haibin Yan, Guangming Sun, Xiaojun Comput Intell Neurosci Research Article The purpose of this study was to investigate the state estimation problem for the multisensor descriptor fractional systems. Firstly, the descriptor fractional order system was transformed into two nondescriptor fractional order subsystem based on the singular value decomposition method; then, the descriptor fractional Kalman filters for the subsystems were proposed based on projection theory, which effectively solved the state estimation problem of the descriptor fractional order system with singular matrix; on this basis, the track fusion fractional Kalman filter of the multisensor descriptor fractional system is proposed by using the track fusion algorithm. The state estimation accuracy of multisensor descriptor fractional order systems is greatly improved. Simulation results show the effectiveness of the proposed algorithm. Hindawi 2022-08-24 /pmc/articles/PMC9433210/ /pubmed/36059401 http://dx.doi.org/10.1155/2022/9637801 Text en Copyright © 2022 Bo Zhang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhang, Bo
Shen, Haibin
Yan, Guangming
Sun, Xiaojun
Track Fusion Fractional Kalman Filter for the Multisensor Descriptor Fractional Systems
title Track Fusion Fractional Kalman Filter for the Multisensor Descriptor Fractional Systems
title_full Track Fusion Fractional Kalman Filter for the Multisensor Descriptor Fractional Systems
title_fullStr Track Fusion Fractional Kalman Filter for the Multisensor Descriptor Fractional Systems
title_full_unstemmed Track Fusion Fractional Kalman Filter for the Multisensor Descriptor Fractional Systems
title_short Track Fusion Fractional Kalman Filter for the Multisensor Descriptor Fractional Systems
title_sort track fusion fractional kalman filter for the multisensor descriptor fractional systems
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9433210/
https://www.ncbi.nlm.nih.gov/pubmed/36059401
http://dx.doi.org/10.1155/2022/9637801
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AT shenhaibin trackfusionfractionalkalmanfilterforthemultisensordescriptorfractionalsystems
AT yanguangming trackfusionfractionalkalmanfilterforthemultisensordescriptorfractionalsystems
AT sunxiaojun trackfusionfractionalkalmanfilterforthemultisensordescriptorfractionalsystems