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Conference Paper: A new Kalman filter-based algorithm for adaptive coherence analysis of non-stationary multichannel time series

TitleA new Kalman filter-based algorithm for adaptive coherence analysis of non-stationary multichannel time series
Authors
KeywordsElectronics
Issue Date2006
PublisherIEEE.
Citation
Proceedings - Ieee International Symposium On Circuits And Systems, 2006, p. 125-128 How to Cite?
AbstractThis paper proposes a new Kalman filter-based algorithm for multichannel autoregressive (AR) spectrum estimation and adaptive coherence analysis with variable number of measurements. A stochastically perturbed k -order difference equation constraint model is used to describe the dynamics of the AR coefficients and the intersection of confidence intervals (ICI) rule is employed to determine the number of measurements adaptively to improve the timefrequency resolution of the AR spectrum and coherence function. Simulation results show that the proposed algorithm achieves a better time-frequency resolution than conventional algorithms for non-stationary signals. © 2006 IEEE.
DescriptionIEEE International Symposium on Circuits and Systems, Island of Kos, Greece, 21-24 May 2006
Persistent Identifierhttp://hdl.handle.net/10722/45929
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorZhang, ZGen_HK
dc.contributor.authorChan, SCen_HK
dc.date.accessioned2007-10-30T06:38:42Z-
dc.date.available2007-10-30T06:38:42Z-
dc.date.issued2006en_HK
dc.identifier.citationProceedings - Ieee International Symposium On Circuits And Systems, 2006, p. 125-128en_HK
dc.identifier.issn0271-4310en_HK
dc.identifier.urihttp://hdl.handle.net/10722/45929-
dc.descriptionIEEE International Symposium on Circuits and Systems, Island of Kos, Greece, 21-24 May 2006-
dc.description.abstractThis paper proposes a new Kalman filter-based algorithm for multichannel autoregressive (AR) spectrum estimation and adaptive coherence analysis with variable number of measurements. A stochastically perturbed k -order difference equation constraint model is used to describe the dynamics of the AR coefficients and the intersection of confidence intervals (ICI) rule is employed to determine the number of measurements adaptively to improve the timefrequency resolution of the AR spectrum and coherence function. Simulation results show that the proposed algorithm achieves a better time-frequency resolution than conventional algorithms for non-stationary signals. © 2006 IEEE.en_HK
dc.format.extent1310065 bytes-
dc.format.extent27162 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofProceedings - IEEE International Symposium on Circuits and Systemsen_HK
dc.rights©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectElectronicsen_HK
dc.titleA new Kalman filter-based algorithm for adaptive coherence analysis of non-stationary multichannel time seriesen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0271-4302&volume=&spage=125&epage=128&date=2006&atitle=A+new+Kalman+filter-based+algorithm+for+adaptive+coherence+analysis+of+non-stationary+multichannel+time+seriesen_HK
dc.identifier.emailChan, SC:scchan@eee.hku.hken_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ISCAS.2006.1692538en_HK
dc.identifier.scopuseid_2-s2.0-34547340467en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34547340467&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage125en_HK
dc.identifier.epage128en_HK
dc.identifier.scopusauthoridZhang, ZG=15039888400en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK

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