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Conference Paper: Estimation of time-varying autocorrelation and its application to time-frequency analysis of nonstationary signals

TitleEstimation of time-varying autocorrelation and its application to time-frequency analysis of nonstationary signals
Authors
KeywordsAdaptive window selection
Minimum variance spectral estimation
Nonstationary signal
Time varying autocorrelation
Time-frequency analysis
Issue Date2013
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000089
Citation
The 2013 IEEE International Symposium on Circuits and Systems (ISCAS 2013), Beijing, China, 19-23 May 2013. In IEEE International Symposium on Circuits and Systems Proceedings, 2013, p. 1524-1527 How to Cite?
AbstractThis paper introduces a new method for adaptively estimating the time-varying autocorrelation (TV-AC) of nonstationary signals and studies its application to time-frequency analysis. The proposed method employs local estimation with a sliding window having a certain bandwidth to estimate the TV-AC locally. The window bandwidths are selected adaptively by a local plug-in rule to address the bias and variance tradeoff problem. Further, based on the proposed adaptive TV-AC estimation, a new time-frequency analysis method called adaptive windowed minimum variance spectral estimation (AWMVSE) is developed. Simulation results show that the proposed adaptive TV-AC estimation method and AWMVSE method have improved performances over conventional estimators with a fixed window. © 2013 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/189876
ISBN
ISSN

 

DC FieldValueLanguage
dc.contributor.authorFu, Zen_US
dc.contributor.authorZhang, Zen_US
dc.contributor.authorChan, SCen_US
dc.date.accessioned2013-09-17T15:01:03Z-
dc.date.available2013-09-17T15:01:03Z-
dc.date.issued2013en_US
dc.identifier.citationThe 2013 IEEE International Symposium on Circuits and Systems (ISCAS 2013), Beijing, China, 19-23 May 2013. In IEEE International Symposium on Circuits and Systems Proceedings, 2013, p. 1524-1527en_US
dc.identifier.isbn978-1-4673-5762-3-
dc.identifier.issn0271-4302-
dc.identifier.urihttp://hdl.handle.net/10722/189876-
dc.description.abstractThis paper introduces a new method for adaptively estimating the time-varying autocorrelation (TV-AC) of nonstationary signals and studies its application to time-frequency analysis. The proposed method employs local estimation with a sliding window having a certain bandwidth to estimate the TV-AC locally. The window bandwidths are selected adaptively by a local plug-in rule to address the bias and variance tradeoff problem. Further, based on the proposed adaptive TV-AC estimation, a new time-frequency analysis method called adaptive windowed minimum variance spectral estimation (AWMVSE) is developed. Simulation results show that the proposed adaptive TV-AC estimation method and AWMVSE method have improved performances over conventional estimators with a fixed window. © 2013 IEEE.-
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000089-
dc.relation.ispartofIEEE International Symposium on Circuits and Systems Proceedingsen_US
dc.rightsIEEE International Symposium on Circuits and Systems. Proceedings. Copyright © IEEE.-
dc.rights©2013 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.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectAdaptive window selection-
dc.subjectMinimum variance spectral estimation-
dc.subjectNonstationary signal-
dc.subjectTime varying autocorrelation-
dc.subjectTime-frequency analysis-
dc.titleEstimation of time-varying autocorrelation and its application to time-frequency analysis of nonstationary signalsen_US
dc.typeConference_Paperen_US
dc.identifier.emailFu, Z: znfu@eee.hku.hken_US
dc.identifier.emailZhang, Z: zgzhang@eee.hku.hken_US
dc.identifier.emailChan, SC: ascchan@hkucc.hku.hk-
dc.identifier.authorityZhang, Z=rp01565en_US
dc.identifier.authorityChan, SC=rp00094en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ISCAS.2013.6572148-
dc.identifier.scopuseid_2-s2.0-84883361173-
dc.identifier.hkuros223279en_US
dc.identifier.spage1524-
dc.identifier.epage1527-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 131024-

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