File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: A new Kalman filter-based power spectral density estimation for nonstationary pressure signals

TitleA new Kalman filter-based power spectral density estimation for nonstationary pressure signals
Authors
Zhang, ZGLau, WYChan, SC
KeywordsElectronics
Issue Date2006
PublisherIEEE.
Citation
Proceedings - Ieee International Symposium On Circuits And Systems, 2006, p. 1619-1622 How to Cite?
DOI: http://dx.doi.org/10.1109/ISCAS.2006.1692911
AbstractThis paper presents a new Kalman filter-based power spectral density estimation (PSD) algorithm for nonstationary pressure signals. The pressure signal is assumed to be an autoregressive (AR) process, and a stochastically perturbed difference equation constraint model is used to describe the dynamics of the AR coefficients. The proposed Kalman filter frame uses variable number of measurements to estimate the time-varying AR coefficients and yield the PSD estimation with better time-frequency resolution. Simulation results show that the proposed algorithm achieves a better time-frequency resolution than conventional algorithms for nonstationary pressure signals. © 2006 IEEE.
DescriptionIEEE International Symposium on Circuits and Systems, Island of Kos, Greece, 21-24 May 2006
Persistent Identifierhttp://hdl.handle.net/10722/45925
ISSN
0271-4310
2020 SCImago Journal Rankings: 0.229
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorZhang, ZGen_HK
dc.contributor.authorLau, WYen_HK
dc.contributor.authorChan, SCen_HK
dc.date.accessioned2007-10-30T06:38:37Z-
dc.date.available2007-10-30T06:38:37Z-
dc.date.issued2006en_HK
dc.identifier.citationProceedings - Ieee International Symposium On Circuits And Systems, 2006, p. 1619-1622en_HK
dc.identifier.issn0271-4310en_HK
dc.identifier.urihttp://hdl.handle.net/10722/45925-
dc.descriptionIEEE International Symposium on Circuits and Systems, Island of Kos, Greece, 21-24 May 2006-
dc.description.abstractThis paper presents a new Kalman filter-based power spectral density estimation (PSD) algorithm for nonstationary pressure signals. The pressure signal is assumed to be an autoregressive (AR) process, and a stochastically perturbed difference equation constraint model is used to describe the dynamics of the AR coefficients. The proposed Kalman filter frame uses variable number of measurements to estimate the time-varying AR coefficients and yield the PSD estimation with better time-frequency resolution. Simulation results show that the proposed algorithm achieves a better time-frequency resolution than conventional algorithms for nonstationary pressure signals. © 2006 IEEE.en_HK
dc.format.extent1247084 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.-
dc.subjectElectronicsen_HK
dc.titleA new Kalman filter-based power spectral density estimation for nonstationary pressure signalsen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0271-4302&volume=&spage=1619&epage=1622&date=2006&atitle=A+new+Kalman+filter-based+power+spectral+density+estimation+for+nonstationary+pressure+signalsen_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.2006.1692911en_HK
dc.identifier.scopuseid_2-s2.0-34547350758en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34547350758&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage1619en_HK
dc.identifier.epage1622en_HK
dc.identifier.scopusauthoridZhang, ZG=15039888400en_HK
dc.identifier.scopusauthoridLau, WY=13608386400en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.issnl0271-4310-

Export via OAI-PMH Interface in XML Formats

OR

Export to Other Non-XML Formats