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Probabilistic Convergence of Kalman Filtering over Nonstationary Fading Channels
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-9940-5929
2014 (English)In: Decision and Control (CDC), 2014 IEEE 53rd Annual Conference on, IEEE conference proceedings, 2014, , 6 p.3783-3788 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we consider state estimation using a Kalman filter of a linear time-invariant process with nonstationary intermittent observations caused by packet losses. The packet loss process is modeled as a sequence of independent, but not necessarily identical Bernoulli random variables. Under this model, we show how the probabilistic convergence of the trace of the prediction error covariance matrices, which is denoted as Tr(Pk), depends on the statistical property of the nonstationary packet loss process. A series of sufficient and/or necessary conditions for the convergence of supk≥n Tr(Pk) and infk≥n Tr(Pk) are derived. In particular, for one-step observable linear system, a sufficient and necessary condition for the convergence of infk≥n Tr(Pk) is provided.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014. , 6 p.3783-3788 p.
Series
Proceedings of the IEEE Conference on Decision and Control, ISSN 0191-2216
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-165264DOI: 10.1109/CDC.2014.7039978Scopus ID: 2-s2.0-84988043446ISBN: 78-1-4799-7746-8 (print)OAI: oai:DiVA.org:kth-165264DiVA: diva2:807730
Conference
The 53rd IEEE Conference on Decision and Control,15-17 Dec. 2014,Los Angeles, CA
Note

QC 20150522

Available from: 2015-04-24 Created: 2015-04-24 Last updated: 2016-11-02Bibliographically approved

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Johansson, Karl Henrik

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