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Article: A New Local Polynomial Modeling Based Variable Forgetting Factor and Variable Regularized PAST Algorithm for Subspace Tracking

TitleA New Local Polynomial Modeling Based Variable Forgetting Factor and Variable Regularized PAST Algorithm for Subspace Tracking
Authors
KeywordsLocal polynomial modeling (LPM)
projection approximation subspace tracking (PAST)
subspace trackingvariable forgetting factor (VFF)
variable regularization (VR)
Issue Date2018
PublisherIEEE. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7
Citation
IEEE Transactions on Aerospace and Electronic Systems, 2018, v. 54 n. 3, p. 1530-1544 How to Cite?
AbstractThis paper proposes a new local polynomial modeling based variable forgetting factor (VFF) and variable regularized (VR) projection approximation subspace tracking (PAST) algorithm, which is based on a novel VR-VFF recursive least squares (RLS) algorithm with multiple outputs. The subspace to be estimated is modeled as a local polynomial model so that a new locally optimal forgetting factor (LOFF) can be obtained by minimizing the resulting mean square deviation of the RLS algorithm after using the projection approximation. An l 2 -regularization term is also incorporated to the LOFF-PAST algorithm to reduce the estimation variance of the subspace during signal fading. The proposed LOFF-VR-PAST algorithm can be implemented by the conventional RLS algorithm as well as the numerically more stable QR decomposition. Applications of the proposed algorithms to subspace-based direction-of-arrival estimation under stationary and nonstationary environments are presented to validate their effectiveness. Simulation results show that the proposed algorithms offer improved performance over the conventional PAST algorithm and a comparable performance to the Kalman filter with variable measurement subspace tracking algorithm, which requires a considerably higher arithmetic complexity. The new LOFF-VR-RLS algorithm may also be applicable to other RLS problems involving multiple outputs.
Persistent Identifierhttp://hdl.handle.net/10722/294062
ISSN
2020 Impact Factor: 4.102
2020 SCImago Journal Rankings: 1.137
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChan, SC-
dc.contributor.authorTAN, HJ-
dc.contributor.authorLIN, JQ-
dc.contributor.authorLiao, B-
dc.date.accessioned2020-11-23T08:25:47Z-
dc.date.available2020-11-23T08:25:47Z-
dc.date.issued2018-
dc.identifier.citationIEEE Transactions on Aerospace and Electronic Systems, 2018, v. 54 n. 3, p. 1530-1544-
dc.identifier.issn0018-9251-
dc.identifier.urihttp://hdl.handle.net/10722/294062-
dc.description.abstractThis paper proposes a new local polynomial modeling based variable forgetting factor (VFF) and variable regularized (VR) projection approximation subspace tracking (PAST) algorithm, which is based on a novel VR-VFF recursive least squares (RLS) algorithm with multiple outputs. The subspace to be estimated is modeled as a local polynomial model so that a new locally optimal forgetting factor (LOFF) can be obtained by minimizing the resulting mean square deviation of the RLS algorithm after using the projection approximation. An l 2 -regularization term is also incorporated to the LOFF-PAST algorithm to reduce the estimation variance of the subspace during signal fading. The proposed LOFF-VR-PAST algorithm can be implemented by the conventional RLS algorithm as well as the numerically more stable QR decomposition. Applications of the proposed algorithms to subspace-based direction-of-arrival estimation under stationary and nonstationary environments are presented to validate their effectiveness. Simulation results show that the proposed algorithms offer improved performance over the conventional PAST algorithm and a comparable performance to the Kalman filter with variable measurement subspace tracking algorithm, which requires a considerably higher arithmetic complexity. The new LOFF-VR-RLS algorithm may also be applicable to other RLS problems involving multiple outputs.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7-
dc.relation.ispartofIEEE Transactions on Aerospace and Electronic Systems-
dc.rightsIEEE Transactions on Aerospace and Electronic Systems. 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.subjectLocal polynomial modeling (LPM)-
dc.subjectprojection approximation subspace tracking (PAST)-
dc.subjectsubspace trackingvariable forgetting factor (VFF)-
dc.subjectvariable regularization (VR)-
dc.titleA New Local Polynomial Modeling Based Variable Forgetting Factor and Variable Regularized PAST Algorithm for Subspace Tracking-
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/TAES.2018.2797780-
dc.identifier.scopuseid_2-s2.0-85041012859-
dc.identifier.hkuros319246-
dc.identifier.volume54-
dc.identifier.issue3-
dc.identifier.spage1530-
dc.identifier.epage1544-
dc.identifier.isiWOS:000435189600035-
dc.publisher.placeUnited States-
dc.identifier.issnl0018-9251-

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