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Conference Paper: Minimum variance spectral estimation-based time frequency analysis for nonstationary time-series

TitleMinimum variance spectral estimation-based time frequency analysis for nonstationary time-series
Authors
Issue Date2007
Citation
Proceedings - Ieee International Symposium On Circuits And Systems, 2007, p. 1815-1818 How to Cite?
AbstractThis paper introduces two new time-frequency analysis methods originated from the minimum variance spectral estimation (MVSE) for nonstationary time-series. First, a windowed MVSE (WMVSE) extends the conventional MVSE by windowing the observation data to obtain a timefrequency distribution for the time-series. Moreover, the window lengths are selected adaptively by the intersection of confidence intervals (ICI) rule to improve the time-frequency resolution. Secondly, a new recursive MVSE (RMVSE) is developed to process the input samples recursively at a lower arithmetic complexity for online time-frequency analysis. Simulation results show that the proposed WMVSE with adaptive windows offers better frequency resolutions than the Fourier-transformed-based time-frequency distributions, and the RMVSE has a good performance when tracking sinusoidal signals. © 2007 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/99084
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorChan, SCen_HK
dc.contributor.authorZhang, ZGen_HK
dc.contributor.authorTsui, KMen_HK
dc.date.accessioned2010-09-25T18:15:11Z-
dc.date.available2010-09-25T18:15:11Z-
dc.date.issued2007en_HK
dc.identifier.citationProceedings - Ieee International Symposium On Circuits And Systems, 2007, p. 1815-1818en_HK
dc.identifier.issn0271-4310en_HK
dc.identifier.urihttp://hdl.handle.net/10722/99084-
dc.description.abstractThis paper introduces two new time-frequency analysis methods originated from the minimum variance spectral estimation (MVSE) for nonstationary time-series. First, a windowed MVSE (WMVSE) extends the conventional MVSE by windowing the observation data to obtain a timefrequency distribution for the time-series. Moreover, the window lengths are selected adaptively by the intersection of confidence intervals (ICI) rule to improve the time-frequency resolution. Secondly, a new recursive MVSE (RMVSE) is developed to process the input samples recursively at a lower arithmetic complexity for online time-frequency analysis. Simulation results show that the proposed WMVSE with adaptive windows offers better frequency resolutions than the Fourier-transformed-based time-frequency distributions, and the RMVSE has a good performance when tracking sinusoidal signals. © 2007 IEEE.en_HK
dc.languageengen_HK
dc.relation.ispartofProceedings - IEEE International Symposium on Circuits and Systemsen_HK
dc.titleMinimum variance spectral estimation-based time frequency analysis for nonstationary time-seriesen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailChan, SC:scchan@eee.hku.hken_HK
dc.identifier.emailTsui, KM:kmtsui@eee.hku.hken_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.identifier.authorityTsui, KM=rp00181en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-34548856260en_HK
dc.identifier.hkuros140427en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34548856260&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage1815en_HK
dc.identifier.epage1818en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridZhang, ZG=15039888400en_HK
dc.identifier.scopusauthoridTsui, KM=7101671591en_HK

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