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Article: Robust M-estimate adaptive filtering

TitleRobust M-estimate adaptive filtering
Authors
Issue Date2001
Citation
Iee Proceedings: Vision, Image And Signal Processing, 2001, v. 148 n. 4, p. 289-294 How to Cite?
AbstractAn M-estimate adaptive filter for robust adaptive filtering in impulse noise is proposed. Instead of using the conventional least-square cost function, a new cost function based on an M-estimator is used to suppress the effect of impulse noise on the filter weights. The resulting optimal weight vector is governed by an M-estimate normal equation. A recursive least M-estimate (RLM) adaptive algorithm and a robust threshold estimation method are derived for solving this equation. The mean convergence performance of the proposed algorithm is also analysed using the modified Huber function (a simple but good approximation to the Hampel's three-parts-redescending M-estimate function) and the contaminated gaussian noise model. Simulation results show that the proposed RLM algorithm has better performance than other recursive least squares (RLS) like algorithms under either contaminated gaussian or alpha-stable noise environment. The initial convergence, steady-state error, robustness to system change and computational complexity are also found to be comparable to the conventional RLS algorithm under gaussian noise alone.
Persistent Identifierhttp://hdl.handle.net/10722/74077
ISSN
2006 Impact Factor: 0.461
2009 SCImago Journal Rankings: 0.450
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorZou, Yen_HK
dc.contributor.authorChan, SCen_HK
dc.contributor.authorNg, TSen_HK
dc.date.accessioned2010-09-06T06:57:34Z-
dc.date.available2010-09-06T06:57:34Z-
dc.date.issued2001en_HK
dc.identifier.citationIee Proceedings: Vision, Image And Signal Processing, 2001, v. 148 n. 4, p. 289-294en_HK
dc.identifier.issn1350-245Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/74077-
dc.description.abstractAn M-estimate adaptive filter for robust adaptive filtering in impulse noise is proposed. Instead of using the conventional least-square cost function, a new cost function based on an M-estimator is used to suppress the effect of impulse noise on the filter weights. The resulting optimal weight vector is governed by an M-estimate normal equation. A recursive least M-estimate (RLM) adaptive algorithm and a robust threshold estimation method are derived for solving this equation. The mean convergence performance of the proposed algorithm is also analysed using the modified Huber function (a simple but good approximation to the Hampel's three-parts-redescending M-estimate function) and the contaminated gaussian noise model. Simulation results show that the proposed RLM algorithm has better performance than other recursive least squares (RLS) like algorithms under either contaminated gaussian or alpha-stable noise environment. The initial convergence, steady-state error, robustness to system change and computational complexity are also found to be comparable to the conventional RLS algorithm under gaussian noise alone.en_HK
dc.languageengen_HK
dc.relation.ispartofIEE Proceedings: Vision, Image and Signal Processingen_HK
dc.titleRobust M-estimate adaptive filteringen_HK
dc.typeArticleen_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.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1049/ip-vis:20010316en_HK
dc.identifier.scopuseid_2-s2.0-0035435611en_HK
dc.identifier.hkuros71945en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0035435611&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume148en_HK
dc.identifier.issue4en_HK
dc.identifier.spage289en_HK
dc.identifier.epage294en_HK
dc.identifier.isiWOS:000171521500012-
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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