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DC Field | Value | Language |
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dc.contributor.author | Chen, D | - |
dc.contributor.author | Yu, Y | - |
dc.contributor.author | Xu, L | - |
dc.contributor.author | Liu, X | - |
dc.date.accessioned | 2016-07-18T13:58:39Z | - |
dc.date.available | 2015-01-01 | - |
dc.date.available | 2016-07-18T13:58:39Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Discrete Dynamics in Nature and Society, Article ID 809734, (2015) | en_US |
dc.identifier.issn | 1026-0226 | - |
dc.identifier.issn | 1607-887X | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/12962 | - |
dc.description.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. | en_US |
dc.description.sponsorship | This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grants 11271103 and 11301118. | en_US |
dc.language.iso | en | en_US |
dc.title | Kalman filtering for discrete stochastic systems with multiplicative noises and random two-step sensor delays | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1155/2015/809734 | - |
dc.relation.isPartOf | Discrete Dynamics in Nature and Society | - |
pubs.publication-status | Published | - |
pubs.volume | 2015 | - |
Appears in Collections: | Dept of Mathematics Research Papers |
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Fulltext.pdf | 2.35 MB | Adobe PDF | View/Open |
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