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Article: A Recursive Least M-Estimate Algorithm for Robust Adaptive Filtering in Impulsive Noise: Fast Algorithm and Convergence Performance Analysis

TitleA Recursive Least M-Estimate Algorithm for Robust Adaptive Filtering in Impulsive Noise: Fast Algorithm and Convergence Performance Analysis
Authors
KeywordsAdaptive filter
Contaminated Gaussian distribution
Impulsive noise suppression
Lattice structure
Prior Error Feedback-Least Square Lattice Algorithm
Recursive least M-estimate algorithm
Robust statistics
System identification
Issue Date2004
PublisherIEEE.
Citation
Ieee Transactions On Signal Processing, 2004, v. 52 n. 4, p. 975-991 How to Cite?
AbstractThis paper studies the problem of robust adaptive filtering in impulsive noise environment using a recursive least M-estimate algorithm (RLM). The RLM algorithm minimizes a robust M-estimator-based cost function instead of the conventional mean square error function (MSE). Previous work has showed that the RLM algorithm offers improved robustness to impulses over conventional recursive least squares (RLS) algorithm. In this paper, the mean and mean square convergence behaviors of the RLM algorithm under the contaminated Gaussian impulsive noise model is analyzed. A lattice structure-based fast RLM algorithm, called the Huber Prior Error Feedback-Least Squares Lattice (H-PEF-LSL) algorithm1 is derived. It has an order O(N) arithmetic complexity, where N is the length of the adaptive filter, and can be viewed as a fast implementation of the RLM algorithm based on the modified Huber M-estimate function and the conventional PEF-LSL adaptive filtering algorithm. Simulation results show that the transversal RLM and the H-PEF-LSL algorithms have better performance than the conventional RLS and other RLS-like robust adaptive algorithms tested when the desired and input signals are corrupted by impulsive noise. Furthermore, the theoretical and simulation results on the convergence behaviors agree very well with each other.
Persistent Identifierhttp://hdl.handle.net/10722/42959
ISSN
2015 Impact Factor: 2.624
2015 SCImago Journal Rankings: 2.004
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChan, SCen_HK
dc.contributor.authorZou, YXen_HK
dc.date.accessioned2007-03-23T04:35:31Z-
dc.date.available2007-03-23T04:35:31Z-
dc.date.issued2004en_HK
dc.identifier.citationIeee Transactions On Signal Processing, 2004, v. 52 n. 4, p. 975-991en_HK
dc.identifier.issn1053-587Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/42959-
dc.description.abstractThis paper studies the problem of robust adaptive filtering in impulsive noise environment using a recursive least M-estimate algorithm (RLM). The RLM algorithm minimizes a robust M-estimator-based cost function instead of the conventional mean square error function (MSE). Previous work has showed that the RLM algorithm offers improved robustness to impulses over conventional recursive least squares (RLS) algorithm. In this paper, the mean and mean square convergence behaviors of the RLM algorithm under the contaminated Gaussian impulsive noise model is analyzed. A lattice structure-based fast RLM algorithm, called the Huber Prior Error Feedback-Least Squares Lattice (H-PEF-LSL) algorithm1 is derived. It has an order O(N) arithmetic complexity, where N is the length of the adaptive filter, and can be viewed as a fast implementation of the RLM algorithm based on the modified Huber M-estimate function and the conventional PEF-LSL adaptive filtering algorithm. Simulation results show that the transversal RLM and the H-PEF-LSL algorithms have better performance than the conventional RLS and other RLS-like robust adaptive algorithms tested when the desired and input signals are corrupted by impulsive noise. Furthermore, the theoretical and simulation results on the convergence behaviors agree very well with each other.en_HK
dc.format.extent603182 bytes-
dc.format.extent28672 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/msword-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE Transactions on Signal Processingen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2004 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.subjectContaminated Gaussian distributionen_HK
dc.subjectImpulsive noise suppressionen_HK
dc.subjectLattice structureen_HK
dc.subjectPrior Error Feedback-Least Square Lattice Algorithmen_HK
dc.subjectRecursive least M-estimate algorithmen_HK
dc.subjectRobust statisticsen_HK
dc.subjectSystem identificationen_HK
dc.titleA Recursive Least M-Estimate Algorithm for Robust Adaptive Filtering in Impulsive Noise: Fast Algorithm and Convergence Performance Analysisen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1053-587X&volume=52&issue=4&spage=975&epage=991&date=2004&atitle=A+recursive+least+M-estimate+algorithm+for+robust+adaptive+filtering+in+impulsive+noise:+fast+algorithm+and+convergence+performance+analysisen_HK
dc.identifier.emailChan, SC:scchan@eee.hku.hken_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/TSP.2004.823496en_HK
dc.identifier.scopuseid_2-s2.0-1842586065en_HK
dc.identifier.hkuros90017-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-1842586065&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume52en_HK
dc.identifier.issue4en_HK
dc.identifier.spage975en_HK
dc.identifier.epage991en_HK
dc.identifier.isiWOS:000220358700012-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridZou, YX=7402166847en_HK

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