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Article: Least mean M -estimate algorithms for robust adaptive filtering in impulse noise

TitleLeast mean M -estimate algorithms for robust adaptive filtering in impulse noise
Authors
KeywordsAdaptive filter
Impulse noise suppression
Least mean jvf-estimate algorithm (LMM)
Orthogonal transform
Robust statistics
System identification
Issue Date2000
PublisherIEEE.
Citation
Ieee Transactions On Circuits And Systems Ii: Analog And Digital Signal Processing, 2000, v. 47 n. 12, p. 1564-1569 How to Cite?
AbstractThis paper proposes two gradient-based adaptive algorithms, called the least mean M-estimate and the transform domain least mean M -estimate (TLMM) algorithms, for robust adaptive filtering in impulse noise. A robust M -estimator is used as the objective function to suppress the adverse effects of impulse noise on the filter weights. They have a computational complexity of order O(N) and can be viewed, respectively, as the generalization of the least mean square and the transform-domain least mean square algorithms. A robust method for estimating the required thresholds in the M -estimator is also given. Simulation results show that the TLMM algorithm, in particular, is more robust and effective than other commonly used algorithms in suppressing the adverse effects of the impulses. © 2000 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/42865
ISSN
2006 Impact Factor: 0.922
2007 SCImago Journal Rankings: 1.092
References

 

DC FieldValueLanguage
dc.contributor.authorZou, Yen_HK
dc.contributor.authorChan, SCen_HK
dc.contributor.authorNg, TSen_HK
dc.date.accessioned2007-03-23T04:33:40Z-
dc.date.available2007-03-23T04:33:40Z-
dc.date.issued2000en_HK
dc.identifier.citationIeee Transactions On Circuits And Systems Ii: Analog And Digital Signal Processing, 2000, v. 47 n. 12, p. 1564-1569en_HK
dc.identifier.issn1057-7130en_HK
dc.identifier.urihttp://hdl.handle.net/10722/42865-
dc.description.abstractThis paper proposes two gradient-based adaptive algorithms, called the least mean M-estimate and the transform domain least mean M -estimate (TLMM) algorithms, for robust adaptive filtering in impulse noise. A robust M -estimator is used as the objective function to suppress the adverse effects of impulse noise on the filter weights. They have a computational complexity of order O(N) and can be viewed, respectively, as the generalization of the least mean square and the transform-domain least mean square algorithms. A robust method for estimating the required thresholds in the M -estimator is also given. Simulation results show that the TLMM algorithm, in particular, is more robust and effective than other commonly used algorithms in suppressing the adverse effects of the impulses. © 2000 IEEE.en_HK
dc.format.extent211988 bytes-
dc.format.extent28672 bytes-
dc.format.extent8772 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/msword-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processingen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2000 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.subjectAdaptive filteren_HK
dc.subjectImpulse noise suppressionen_HK
dc.subjectLeast mean jvf-estimate algorithm (LMM)en_HK
dc.subjectOrthogonal transformen_HK
dc.subjectRobust statisticsen_HK
dc.subjectSystem identificationen_HK
dc.titleLeast mean M -estimate algorithms for robust adaptive filtering in impulse noiseen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1057-7130&volume=47&issue=12&spage=1564&epage=1569&date=2000&atitle=Least+mean+M-estimate+algorithms+for+robust+adaptive+filtering+in+impulse+noiseen_HK
dc.identifier.emailChan, SC:scchan@eee.hku.hken_HK
dc.identifier.emailNg, TS:tsng@eee.hku.hken_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.identifier.authorityNg, TS=rp00159en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/82.899657en_HK
dc.identifier.scopuseid_2-s2.0-0034460731en_HK
dc.identifier.hkuros58649-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0034460731&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume47en_HK
dc.identifier.issue12en_HK
dc.identifier.spage1564en_HK
dc.identifier.epage1569en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridZou, Y=7402166847en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridNg, TS=7402229975en_HK

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