Consensus‐based unscented Kalman filtering over sensor networks with communication protocols
This article is concerned with the consensus‐based distributed filtering problem for a class of general nonlinear systems over sensor networks with communication protocols. In order to avoid data collisions, the stochastic protocol and the Round‐Robin protocol are respectively introduced to schedule...
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| Published in | International journal of robust and nonlinear control Vol. 31; no. 13; pp. 6349 - 6368 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Bognor Regis
Wiley Subscription Services, Inc
10.09.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1049-8923 1099-1239 |
| DOI | 10.1002/rnc.5614 |
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| Abstract | This article is concerned with the consensus‐based distributed filtering problem for a class of general nonlinear systems over sensor networks with communication protocols. In order to avoid data collisions, the stochastic protocol and the Round‐Robin protocol are respectively introduced to schedule the data transmission between each node and its neighboring ones. A consensus‐based unscented Kalman filtering (UKF) algorithm is developed for the purpose of estimating the system states over sensor networks subject to communication protocols. Moreover, the exponential boundedness of estimation error in mean square is proved for the proposed algorithm. Finally, compared with the extended Kalman filtering, an experimental simulation example is provided to validate the effectiveness of the consensus‐based UKF algorithm. |
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| AbstractList | This article is concerned with the consensus‐based distributed filtering problem for a class of general nonlinear systems over sensor networks with communication protocols. In order to avoid data collisions, the stochastic protocol and the Round‐Robin protocol are respectively introduced to schedule the data transmission between each node and its neighboring ones. A consensus‐based unscented Kalman filtering (UKF) algorithm is developed for the purpose of estimating the system states over sensor networks subject to communication protocols. Moreover, the exponential boundedness of estimation error in mean square is proved for the proposed algorithm. Finally, compared with the extended Kalman filtering, an experimental simulation example is provided to validate the effectiveness of the consensus‐based UKF algorithm. |
| Author | Huai, Wuxiang Niu, Yichun Gao, Ming Sheng, Li |
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| Cites_doi | 10.1002/rnc.4706 10.1016/j.sysconle.2014.05.001 10.1109/TCYB.2015.2409373 10.1109/TII.2020.3015004 10.1016/j.jfranklin.2020.04.012 10.1002/rnc.5033 10.1016/j.automatica.2005.10.004 10.1109/TSMCB.2012.2236647 10.1016/j.automatica.2016.09.009 10.1016/j.automatica.2016.01.071 10.1109/JPROC.2003.823141 10.1109/TAC.2017.2774601 10.1016/j.neunet.2018.08.015 10.1049/iet-cta.2016.1054 10.1002/rnc.4479 10.1016/j.automatica.2013.11.042 10.1109/TNNLS.2016.2524621 10.1016/j.inffus.2019.06.026 10.1002/rnc.4535 10.1109/TCST.2018.2847290 10.1016/j.neucom.2018.08.087 10.1109/TAC.2013.2263650 10.1109/9.754809 10.1109/TNNLS.2016.2580601 10.1002/rnc.4002 10.1109/TAC.2013.2277621 10.1109/TSMC.2018.2876203 10.1007/s12555-013-0076-y 10.1002/rnc.3790 10.1016/j.ins.2019.10.053 10.1109/TCYB.2017.2685425 10.1016/j.isatra.2018.11.012 10.1109/TCNS.2015.2512326 10.1109/TAC.2019.2929817 |
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| Copyright | 2021 John Wiley & Sons Ltd. 2021 John Wiley & Sons, Ltd. |
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| Notes | Funding information Fundamental Research Funds for the Central Universities of China, 19CX02044A; 20CX02309A; National Natural Science Foundation of China, 61773400; 62033008; 62073339; Natural Science Foundation of Shandong Province of China, ZR2020YQ49; Opening Fund of National Engineering Laboratory of Offshore Geophysical and Exploration Equipment, Shandong Provincial Key Program of Research and Development, 2019GGX101046 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
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| SubjectTerms | Algorithms Communication consensus‐based distributed filtering Data transmission Kalman filters Networks Nonlinear systems Round‐Robin protocol Schedules sensor networks Sensors stochastic protocol unscented Kalman filtering |
| Title | Consensus‐based unscented Kalman filtering over sensor networks with communication protocols |
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