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Article: Recursive Least M-estimate (RLM) adaptive filter for robust filtering in impulse noise

TitleRecursive Least M-estimate (RLM) adaptive filter for robust filtering in impulse noise
Authors
Issue Date2000
PublisherI E E E. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=97
Citation
Ieee Signal Processing Letters, 2000, v. 7 n. 11, p. 324-326 How to Cite?
AbstractThis paper proposes a recursive least M-estimate (RLM) algorithm for robust adaptive filtering in impulse noise. It employs an M-estimate cost function, which is able to suppress the effect of impulses on the filter weights. Simulation results showed that the RLM algorithm performs better than the conventional RLS, NRLS, and OSFKF algorithms when the desired and input signals are corrupted by impulses. Its initial convergence, steady-state error, computational complexity, and robustness to sudden system change are comparable to the conventional RLS algorithm in the presence of Gaussian noise alone.
Persistent Identifierhttp://hdl.handle.net/10722/42829
ISSN
2015 Impact Factor: 1.661
2015 SCImago Journal Rankings: 1.072
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorZou, Yen_HK
dc.contributor.authorChan, SCen_HK
dc.contributor.authorNg, TSen_HK
dc.date.accessioned2007-03-23T04:32:58Z-
dc.date.available2007-03-23T04:32:58Z-
dc.date.issued2000en_HK
dc.identifier.citationIeee Signal Processing Letters, 2000, v. 7 n. 11, p. 324-326en_HK
dc.identifier.issn1070-9908en_HK
dc.identifier.urihttp://hdl.handle.net/10722/42829-
dc.description.abstractThis paper proposes a recursive least M-estimate (RLM) algorithm for robust adaptive filtering in impulse noise. It employs an M-estimate cost function, which is able to suppress the effect of impulses on the filter weights. Simulation results showed that the RLM algorithm performs better than the conventional RLS, NRLS, and OSFKF algorithms when the desired and input signals are corrupted by impulses. Its initial convergence, steady-state error, computational complexity, and robustness to sudden system change are comparable to the conventional RLS algorithm in the presence of Gaussian noise alone.en_HK
dc.format.extent91816 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.publisherI E E E. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=97en_HK
dc.relation.ispartofIEEE Signal Processing Lettersen_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.titleRecursive Least M-estimate (RLM) adaptive filter for robust filtering in impulse noiseen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1070-9908&volume=7&issue=11&spage=324&epage=326&date=2000&atitle=A+recursive+least+M-estimate+(RLM)+adaptive+filter+for+robustfiltering+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.doi10.1109/97.873571en_HK
dc.identifier.scopuseid_2-s2.0-0034319939en_HK
dc.identifier.hkuros50853-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0034319939&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume7en_HK
dc.identifier.issue11en_HK
dc.identifier.spage324en_HK
dc.identifier.epage326en_HK
dc.identifier.isiWOS:000089796000008-
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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