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Article: A New Diffusion Variable Spatial Regularized QRRLS Algorithm

TitleA New Diffusion Variable Spatial Regularized QRRLS Algorithm
Authors
KeywordsDiffusion adaptive algorithm
variable spatial regularization
performance analysis
Issue Date2020
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=97
Citation
IEEE Signal Processing Letters, 2020, v. 27, p. 995-999 How to Cite?
AbstractThis paper develops a framework for the design of diffusion adaptive algorithms, where a network of nodes aim to estimate system parameters from the collected distinct local data stream. We explore the time and spatial knowledge of system responses and model their evolution in both time and spatial domain. A weighted maximum a posteriori probability (MAP) is used to derive an adaptive estimator, where recent data has more influence on statistics via weighting factors. The resulting recursive least squares (RLS) local estimate can be implemented by the QR decomposition (QRD). To mediate the distinct spatial information incorporation within neighboring estimates, a variable spatial regularization (VSR) parameter is introduced. The estimation bias and variance of the proposed algorithm are analyzed. A new diffusion VSR QRRLS (Diff-VSR-QRRLS) algorithm is derived that balances the bias and variance terms. Simulations are carried out to illustrate the effectiveness of the theoretical analysis and evaluate the performance of the proposed algorithm.
Persistent Identifierhttp://hdl.handle.net/10722/308300
ISSN
2023 Impact Factor: 3.2
2023 SCImago Journal Rankings: 1.271
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChu, Y-
dc.contributor.authorChan, SC-
dc.contributor.authorZhou, Y-
dc.contributor.authorWu, M-
dc.date.accessioned2021-11-12T13:45:19Z-
dc.date.available2021-11-12T13:45:19Z-
dc.date.issued2020-
dc.identifier.citationIEEE Signal Processing Letters, 2020, v. 27, p. 995-999-
dc.identifier.issn1070-9908-
dc.identifier.urihttp://hdl.handle.net/10722/308300-
dc.description.abstractThis paper develops a framework for the design of diffusion adaptive algorithms, where a network of nodes aim to estimate system parameters from the collected distinct local data stream. We explore the time and spatial knowledge of system responses and model their evolution in both time and spatial domain. A weighted maximum a posteriori probability (MAP) is used to derive an adaptive estimator, where recent data has more influence on statistics via weighting factors. The resulting recursive least squares (RLS) local estimate can be implemented by the QR decomposition (QRD). To mediate the distinct spatial information incorporation within neighboring estimates, a variable spatial regularization (VSR) parameter is introduced. The estimation bias and variance of the proposed algorithm are analyzed. A new diffusion VSR QRRLS (Diff-VSR-QRRLS) algorithm is derived that balances the bias and variance terms. Simulations are carried out to illustrate the effectiveness of the theoretical analysis and evaluate the performance of the proposed algorithm.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=97-
dc.relation.ispartofIEEE Signal Processing Letters-
dc.rightsIEEE Signal Processing Letters. Copyright © IEEE.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectDiffusion adaptive algorithm-
dc.subjectvariable spatial regularization-
dc.subjectperformance analysis-
dc.titleA New Diffusion Variable Spatial Regularized QRRLS Algorithm-
dc.typeArticle-
dc.identifier.emailChan, SC: scchan@eee.hku.hk-
dc.identifier.authorityChan, SC=rp00094-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/LSP.2020.2999883-
dc.identifier.scopuseid_2-s2.0-85087504355-
dc.identifier.hkuros329433-
dc.identifier.volume27-
dc.identifier.spage995-
dc.identifier.epage999-
dc.identifier.isiWOS:000543957500003-
dc.publisher.placeUnited States-

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