Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/TSP.2004.823496
- Scopus: eid_2-s2.0-1842586065
- WOS: WOS:000220358700012
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: A Recursive Least M-Estimate Algorithm for Robust Adaptive Filtering in Impulsive Noise: Fast Algorithm and Convergence Performance Analysis
Title | A Recursive Least M-Estimate Algorithm for Robust Adaptive Filtering in Impulsive Noise: Fast Algorithm and Convergence Performance Analysis |
---|---|
Authors | |
Keywords | Adaptive 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 Date | 2004 |
Publisher | IEEE. |
Citation | Ieee Transactions On Signal Processing, 2004, v. 52 n. 4, p. 975-991 How to Cite? |
Abstract | This 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 Identifier | http://hdl.handle.net/10722/42959 |
ISSN | 2015 Impact Factor: 2.624 2015 SCImago Journal Rankings: 2.004 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chan, SC | en_HK |
dc.contributor.author | Zou, YX | en_HK |
dc.date.accessioned | 2007-03-23T04:35:31Z | - |
dc.date.available | 2007-03-23T04:35:31Z | - |
dc.date.issued | 2004 | en_HK |
dc.identifier.citation | Ieee Transactions On Signal Processing, 2004, v. 52 n. 4, p. 975-991 | en_HK |
dc.identifier.issn | 1053-587X | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/42959 | - |
dc.description.abstract | This 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.extent | 603182 bytes | - |
dc.format.extent | 28672 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.format.mimetype | application/msword | - |
dc.language | eng | en_HK |
dc.publisher | IEEE. | en_HK |
dc.relation.ispartof | IEEE Transactions on Signal Processing | en_HK |
dc.rights | Creative 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.subject | Adaptive filter | en_HK |
dc.subject | Contaminated Gaussian distribution | en_HK |
dc.subject | Impulsive noise suppression | en_HK |
dc.subject | Lattice structure | en_HK |
dc.subject | Prior Error Feedback-Least Square Lattice Algorithm | en_HK |
dc.subject | Recursive least M-estimate algorithm | en_HK |
dc.subject | Robust statistics | en_HK |
dc.subject | System identification | en_HK |
dc.title | A Recursive Least M-Estimate Algorithm for Robust Adaptive Filtering in Impulsive Noise: Fast Algorithm and Convergence Performance Analysis | en_HK |
dc.type | Article | en_HK |
dc.identifier.openurl | http://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+analysis | en_HK |
dc.identifier.email | Chan, SC:scchan@eee.hku.hk | en_HK |
dc.identifier.authority | Chan, SC=rp00094 | en_HK |
dc.description.nature | published_or_final_version | en_HK |
dc.identifier.doi | 10.1109/TSP.2004.823496 | en_HK |
dc.identifier.scopus | eid_2-s2.0-1842586065 | en_HK |
dc.identifier.hkuros | 90017 | - |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-1842586065&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 52 | en_HK |
dc.identifier.issue | 4 | en_HK |
dc.identifier.spage | 975 | en_HK |
dc.identifier.epage | 991 | en_HK |
dc.identifier.isi | WOS:000220358700012 | - |
dc.publisher.place | United States | en_HK |
dc.identifier.scopusauthorid | Chan, SC=13310287100 | en_HK |
dc.identifier.scopusauthorid | Zou, YX=7402166847 | en_HK |