Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/6690
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dc.contributor.authorHu, J-
dc.contributor.authorWang, Z-
dc.contributor.authorGao, H-
dc.contributor.authorStergioulas, LK-
dc.date.accessioned2012-09-21T08:40:58Z-
dc.date.available2012-09-21T08:40:58Z-
dc.date.issued2012-
dc.identifier.citationAutomatica, 48(9): 2007 - 2015, Sep 2012en_US
dc.identifier.issn0005-1098-
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S0005109812002555en
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/6690-
dc.descriptionCopyright @ 2012 Elsevieren_US
dc.description.abstractIn this paper, the extended Kalman filtering problem is investigated for a class of nonlinear systems with multiple missing measurements over a finite horizon. Both deterministic and stochastic nonlinearities are included in the system model, where the stochastic nonlinearities are described by statistical means that could reflect the multiplicative stochastic disturbances. The phenomenon of measurement missing occurs in a random way and the missing probability for each sensor is governed by an individual random variable satisfying a certain probability distribution over the interval [0,1]. Such a probability distribution is allowed to be any commonly used distribution over the interval [0,1] with known conditional probability. The aim of the addressed filtering problem is to design a filter such that, in the presence of both the stochastic nonlinearities and multiple missing measurements, there exists an upper bound for the filtering error covariance. Subsequently, such an upper bound is minimized by properly designing the filter gain at each sampling instant. It is shown that the desired filter can be obtained in terms of the solutions to two Riccati-like difference equations that are of a form suitable for recursive computation in online applications. An illustrative example is given to demonstrate the effectiveness of the proposed filter design scheme.en_US
dc.description.sponsorshipThis work was supported in part by the National 973 Project under Grant 2009CB320600, National Natural Science Foundation of China under Grants 61028008, 61134009 and 60825303, the State Key Laboratory of Integrated Automation for the Process Industry (Northeastern University) of China, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectNonlinear systemsen_US
dc.subjectExtended Kalman filteren_US
dc.subjectStochastic nonlinearitiesen_US
dc.subjectMultiple missing measurementsen_US
dc.subjectRecursive filteren_US
dc.subjectRiccati-like difference equationen_US
dc.titleExtended Kalman filtering with stochastic nonlinearities and multiple missing measurementsen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.automatica.2012.03.027-
pubs.organisational-data/Brunel-
pubs.organisational-data/Brunel/Brunel Active Staff-
pubs.organisational-data/Brunel/Brunel Active Staff/School of Info. Systems, Comp & Maths-
pubs.organisational-data/Brunel/Brunel Active Staff/School of Info. Systems, Comp & Maths/IS and Computing-
pubs.organisational-data/Brunel/University Research Centres and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups/Centre for Systems and Synthetic Biology-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups/Centre for Information and Knowledge Management-
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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