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Article: A recursive frequency estimator using linear prediction and a Kalman-filter-based iterative algorithm

TitleA recursive frequency estimator using linear prediction and a Kalman-filter-based iterative algorithm
Authors
KeywordsIterative method
Kalman filter
Linear prediction
Recursive frequency estimation
Issue Date2008
PublisherIEEE.
Citation
IEEE Transactions On Circuits And Systems Ii: Express Briefs, 2008, v. 55 n. 6, p. 576-580 How to Cite?
AbstractThis paper proposes a new Kalman-filter-based recursive frequency estimator for discrete-time multicomponent sinusoidal signals whose frequencies may be time-varying. The frequency estimator is based on the linear prediction approach and it employs the Kalman filter to track the linear prediction coefficients (LPCs) recursively. Frequencies of the sinusoids can then be computed using the estimated LPCs. Due to the coloredness of the linear prediction error, an iterative algorithm is employed to estimate the covariance matrix of the prediction error and the LPCs alternately in the Kalman filter in order to improve the tracking performance. Simulation results show that the proposed Kalman-filter-based iterative frequency estimator can achieve better tracking results than the conventional recursive least-squares-based estimators. © 2008 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/57460
ISSN
2015 Impact Factor: 1.136
2015 SCImago Journal Rankings: 0.747
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorZhang, ZGen_HK
dc.contributor.authorChan, SCen_HK
dc.contributor.authorTsui, KMen_HK
dc.date.accessioned2010-04-12T01:37:18Z-
dc.date.available2010-04-12T01:37:18Z-
dc.date.issued2008en_HK
dc.identifier.citationIEEE Transactions On Circuits And Systems Ii: Express Briefs, 2008, v. 55 n. 6, p. 576-580en_HK
dc.identifier.issn1549-7747en_HK
dc.identifier.urihttp://hdl.handle.net/10722/57460-
dc.description.abstractThis paper proposes a new Kalman-filter-based recursive frequency estimator for discrete-time multicomponent sinusoidal signals whose frequencies may be time-varying. The frequency estimator is based on the linear prediction approach and it employs the Kalman filter to track the linear prediction coefficients (LPCs) recursively. Frequencies of the sinusoids can then be computed using the estimated LPCs. Due to the coloredness of the linear prediction error, an iterative algorithm is employed to estimate the covariance matrix of the prediction error and the LPCs alternately in the Kalman filter in order to improve the tracking performance. Simulation results show that the proposed Kalman-filter-based iterative frequency estimator can achieve better tracking results than the conventional recursive least-squares-based estimators. © 2008 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE Transactions on Circuits and Systems II: Express Briefsen_HK
dc.rights©2008 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.subjectIterative methoden_HK
dc.subjectKalman filteren_HK
dc.subjectLinear predictionen_HK
dc.subjectRecursive frequency estimationen_HK
dc.titleA recursive frequency estimator using linear prediction and a Kalman-filter-based iterative algorithmen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1057-7130&volume=55&issue=6&spage=576&epage=580&date=2008&atitle=A+recursive+frequency+estimator+using+linear+prediction+and+a+Kalman-filter-based+iterative+algorithmen_HK
dc.identifier.emailZhang, ZG:zgzhang@eee.hku.hken_HK
dc.identifier.emailChan, SC:scchan@eee.hku.hken_HK
dc.identifier.emailTsui, KM:kmtsui@eee.hku.hken_HK
dc.identifier.authorityZhang, ZG=rp01565en_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.identifier.authorityTsui, KM=rp00181en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/TCSII.2007.916837en_HK
dc.identifier.scopuseid_2-s2.0-46649118451en_HK
dc.identifier.hkuros151009-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-46649118451&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume55en_HK
dc.identifier.issue6en_HK
dc.identifier.spage576en_HK
dc.identifier.epage580en_HK
dc.identifier.isiWOS:000257074600017-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridZhang, ZG=8597618700en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridTsui, KM=7101671591en_HK

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