Kalman Filtering for Discrete Stochastic Systems with Multiplicative Noises and Random Two-Step Sensor Delays
This paper is concerned with the optimal Kalman filtering problem for a class of discrete stochastic systems with multiplicative noises and random two-step sensor delays. Three Bernoulli distributed random variables with known conditional probabilities are introduced to characterize the phenomena of...
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| Published in | Discrete Dynamics in Nature and Society Vol. 2015; no. 2015; pp. 824 - 834-289 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Cairo, Egypt
Hindawi Limiteds
01.01.2015
Hindawi Publishing Corporation John Wiley & Sons, Inc Hindawi Limited |
| Subjects | |
| Online Access | Get full text |
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| Abstract | This paper is concerned with the optimal Kalman filtering problem for a class of discrete stochastic systems with multiplicative noises and random two-step sensor delays. Three Bernoulli distributed random variables with known conditional probabilities are introduced to characterize the phenomena of the random two-step sensor delays which may happen during the data transmission. By using the state augmentation approach and innovation analysis technique, an optimal Kalman filter is constructed for the augmented system in the sense of the minimum mean square error (MMSE). Subsequently, the optimal Kalman filtering is derived for corresponding augmented system in initial instants. Finally, a simulation example is provided to demonstrate the feasibility and effectiveness of the proposed filtering method. |
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| AbstractList | This paper is concerned with the optimal Kalman filtering problem for a class of discrete stochastic systems with multiplicative noises and random two-step sensor delays. Three Bernoulli distributed random variables with known conditional probabilities are introduced to characterize the phenomena of the random two-step sensor delays which may happen during the data transmission. By using the state augmentation approach and innovation analysis technique, an optimal Kalman filter is constructed for the augmented system in the sense of the minimum mean square error (MMSE). Subsequently, the optimal Kalman filtering is derived for corresponding augmented system in initial instants. Finally, a simulation example is provided to demonstrate the feasibility and effectiveness of the proposed filtering method. |
| Audience | Academic |
| Author | Chen, Dongyan Xu, Long Liu, Xiaohui Yu, Yonglong |
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| CitedBy_id | crossref_primary_10_3390_s16060847 crossref_primary_10_1109_TCSII_2016_2551548 crossref_primary_10_3390_math11204292 crossref_primary_10_1016_j_automatica_2020_109154 crossref_primary_10_1109_TSMC_2019_2907620 crossref_primary_10_1155_2017_1570719 crossref_primary_10_1080_03081079_2017_1341501 crossref_primary_10_1016_j_inffus_2016_06_008 crossref_primary_10_1155_2015_398605 crossref_primary_10_1007_s10915_022_01994_6 crossref_primary_10_1080_00207160_2018_1437264 crossref_primary_10_3390_s17051151 crossref_primary_10_1016_j_sigpro_2016_02_014 crossref_primary_10_1088_1742_6596_1745_1_012114 crossref_primary_10_3390_math5030045 |
| Cites_doi | 10.1115/1.3662552 10.1080/21642583.2013.864249 10.1109/tac.2013.2241492 10.1115/1.3152698 10.1016/j.sigpro.2014.07.003 10.1016/j.apm.2008.12.008 10.1109/TCSII.2008.921576 10.1109/tcbb.2009.5 10.1016/j.nonrwa.2005.06.009 10.1080/21642583.2014.900657 10.1155/2014/716716 10.1016/j.neucom.2013.12.045 10.1115/1.3152699 10.1109/tsp.2011.2164071 10.1080/00207721.2010.550402 10.1109/TIE.2014.2320215 10.1016/j.neucom.2013.05.010 10.1080/00207179.2012.756149 10.3724/sp.j.1004.2012.00349 10.1016/j.sysconle.2012.01.005 10.1080/03081079.2014.892251 10.1016/j.automatica.2014.10.026 10.1016/j.ast.2010.10.006 10.1155/2013/646059 10.1016/j.automatica.2011.01.015 10.1016/j.jfranklin.2014.08.008 10.1080/21642583.2014.891268 10.1002/rnc.2869 10.1109/tac.2014.2351951 10.1002/rnc.3065 10.1016/j.ins.2014.09.017 10.1109/TSP.2012.2232660 10.1002/rnc.1785 10.1109/lsp.2014.2362932 10.1016/j.jfranklin.2010.03.004 10.1016/s0005-1098(97)00170-2 |
| ContentType | Journal Article |
| Copyright | Copyright © 2015 Dongyan Chen et al. COPYRIGHT 2015 John Wiley & Sons, Inc. Copyright © 2015 Dongyan Chen et al. 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. |
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| DOI | 10.1155/2015/809734 |
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| SubjectTerms | Algorithms Control theory Data transmission Delay Discrete-time systems Economic models Filtering Kalman filtering Kalman filters Mathematical research Mean square errors Microelectromechanical systems Noise Optimization Random variables Sensors Stochastic processes Stochastic systems |
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| Title | Kalman Filtering for Discrete Stochastic Systems with Multiplicative Noises and Random Two-Step Sensor Delays |
| URI | https://www.airitilibrary.com/Article/Detail/P20160913003-201512-201611280050-201611280050-824-834-289 https://search.emarefa.net/detail/BIM-1060771 https://dx.doi.org/10.1155/2015/809734 https://www.proquest.com/docview/1722858168 https://search.proquest.com/docview/1770280319 https://doaj.org/article/0068882cc46b466393d62f12d9efbca2 |
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