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Conference Paper: Bayesian Kalman filtering, regularization and compressed sampling

TitleBayesian Kalman filtering, regularization and compressed sampling
Authors
Chan, SCLiao, BTsui, KM
Issue Date2011
Citation
Midwest Symposium On Circuits And Systems, 2011 How to Cite?
DOI: http://dx.doi.org/10.1109/MWSCAS.2011.6026658
AbstractBayesian Kalman filter (BKF) is an important tool in signal processing, communications, control and statistics. This paper briefly reviews the principle of BKF for Gaussian mixture and proposes a new and efficient method for real-time implementation. Moreover, the close relationship between conventional KF and regularization theory in estimation is reviewed. Using this framework, the problem of sampling, smoothing and interpolation can be treated in a unified framework. New results on under-sampling using non-uniform samples will be presented. © 2011 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/158729
ISSN
1548-3746
2020 SCImago Journal Rankings: 0.181
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorChan, SCen_US
dc.contributor.authorLiao, Ben_US
dc.contributor.authorTsui, KMen_US
dc.date.accessioned2012-08-08T09:01:03Z-
dc.date.available2012-08-08T09:01:03Z-
dc.date.issued2011en_US
dc.identifier.citationMidwest Symposium On Circuits And Systems, 2011en_US
dc.identifier.issn1548-3746en_US
dc.identifier.urihttp://hdl.handle.net/10722/158729-
dc.description.abstractBayesian Kalman filter (BKF) is an important tool in signal processing, communications, control and statistics. This paper briefly reviews the principle of BKF for Gaussian mixture and proposes a new and efficient method for real-time implementation. Moreover, the close relationship between conventional KF and regularization theory in estimation is reviewed. Using this framework, the problem of sampling, smoothing and interpolation can be treated in a unified framework. New results on under-sampling using non-uniform samples will be presented. © 2011 IEEE.en_US
dc.languageengen_US
dc.relation.ispartofMidwest Symposium on Circuits and Systemsen_US
dc.titleBayesian Kalman filtering, regularization and compressed samplingen_US
dc.typeConference_Paperen_US
dc.identifier.emailChan, SC:scchan@eee.hku.hken_US
dc.identifier.emailTsui, KM:kmtsui@eee.hku.hken_US
dc.identifier.authorityChan, SC=rp00094en_US
dc.identifier.authorityTsui, KM=rp00181en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/MWSCAS.2011.6026658en_US
dc.identifier.scopuseid_2-s2.0-80053629452en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-80053629452&selection=ref&src=s&origin=recordpageen_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridChan, SC=13310287100en_US
dc.identifier.scopusauthoridLiao, B=44661377800en_US
dc.identifier.scopusauthoridTsui, KM=7101671591en_US
dc.identifier.issnl1548-3746-

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