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Non-linear smoothers for discrete-time, finite-state Markov chains
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
2013 (English)In: IEEE International Symposium on Information Theory: Proceedings, 2013, 2099-2103 p.Conference paper, Published paper (Refereed)
Abstract [en]

The problem of enhancing the quality of system state estimates is considered for a special class of dynamical systems. Specifically, a system characterized by a discrete-time, finite-state Markov chain state and observed via conditionally Gaussian measurements is assumed. The associated mean vectors and covariance matrices are tightly intertwined with the system state and a control input selected by a controller. Exploiting an innovations approach, finite-dimensional, non-linear approximate MMSE smoothing estimators are derived for the Markov chain system state. The resulting smoothers are driven by a control policy determined by a stochastic dynamic programming algorithm, which minimizes the MSE filtering error, and was proposed in our earlier work. An application of the smoothers derived in this paper is presented for the problem of physical activity detection in wireless body sensing networks, which illustrates the performance enhancement due to smoothing.

Place, publisher, year, edition, pages
2013. 2099-2103 p.
Series
IEEE International Symposium on Information Theory - Proceedings, ISSN 2157-8095
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-139142DOI: 10.1109/ISIT.2013.6620596Scopus ID: 2-s2.0-84890408087ISBN: 9781479904464 (print)OAI: oai:DiVA.org:kth-139142DiVA: diva2:684910
Conference
2013 IEEE International Symposium on Information Theory, ISIT 2013; Istanbul, Turkey, 7-12 July 2013
Note

QC 20140108

Available from: 2014-01-08 Created: 2014-01-07 Last updated: 2014-01-23Bibliographically approved

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