Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/13063
Full metadata record
DC FieldValueLanguage
dc.contributor.authorDate, P-
dc.contributor.authorSingh, A-
dc.contributor.authorBhaumik, S-
dc.date.accessioned2016-08-08T13:34:50Z-
dc.date.available2016-08-08T13:34:50Z-
dc.date.issued2016-
dc.identifier.citationIET Control Theory & Applications, 2016en_US
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/13063-
dc.descriptionThis paper is a postprint of a paper submitted to and accepted for publication in IET Control Theory & Applications and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library.en_US
dc.description.abstractThe filtering of nonlinear continuous-discrete systems is widely applicable in real-life and extensive literature is available to deal with such problems. However, all of these approaches are constrained with the assumption that the current measurement is available at every time step, although delay in measurement is natural in real-life applications. To deal with this problem, we re-derive the conventional Bayesian approximation framework for solving the continuous-discrete filtering problems. In practice, the delay is often smaller than one sampling time, which is the main case considered here. During the filtering of such systems, the actual time of correspondence should be known for a measurement received at the kth time instant. In this paper, a simple and intuitively justified cost function is used to decide the time to which the measurement at kth time instant actually corresponds. The performance of the proposed filter is compared with a conventional filter based on numerical integration which ignores random delays for a continuousdiscrete tracking problem. We show that the conventional filter fails to track the target while the modification proposed in this paper successfully deals with random delays. The proposed method may be seen as a valuable addition to the tools available for continuous-discrete filtering in nonlinear systems.en_US
dc.language.isoenen_US
dc.subjectNonlinear filteringen_US
dc.subjectContinuous-discrete Bayesian approximation framework for nonlinear filteringen_US
dc.subjectDelayed measurementsen_US
dc.subjectNegative Gaussian log-likelihooden_US
dc.subjectIntegrationen_US
dc.titleA new algorithm for continuous-discrete filtering with randomly delayed measurementsen_US
dc.typeArticleen_US
dc.relation.isPartOfIET Control Theory & Applications-
pubs.publication-statusAccepted-
Appears in Collections:Dept of Mathematics Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdf2.13 MBAdobe PDFView/Open


Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.