Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152708
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dc.contributor.authorLau, Tze Siongen_US
dc.contributor.authorTay, Wee Pengen_US
dc.date.accessioned2021-09-20T07:36:24Z-
dc.date.available2021-09-20T07:36:24Z-
dc.date.issued2021-
dc.identifier.citationLau, T. S. & Tay, W. P. (2021). Asymptotically optimal sampling policy for quickest change detection with observation-switching cost. IEEE Transactions On Signal Processing, 69, 1332-1346. https://dx.doi.org/10.1109/TSP.2021.3057258en_US
dc.identifier.issn1053-587Xen_US
dc.identifier.urihttps://hdl.handle.net/10356/152708-
dc.description.abstractWe consider the problem of quickest change detection (QCD) in a signal where its observations are obtained using a set of actions, and switching from one action to another comes with a cost. The objective is to design a stopping rule consisting of a sampling policy to determine the sequence of actions used to observe the signal and a stopping time to quickly detect for the change, subject to a constraint on the average observation-switching cost. We propose an open-loop sampling policy of finite window size and a generalized likelihood ratio (GLR) Cumulative Sum (CuSum) stopping time for the QCD problem. We show that the GLR CuSum stopping time is asymptotically optimal with a properly designed sampling policy and formulate the design of this sampling policy as a quadratic programming problem. We prove that it is sufficient to consider policies of window size not more than one when designing policies of finite window size and propose several algorithms that solve this optimization problem with theoretical guarantees. Finally, we apply our approach to the problem of QCD of a partially observed graph signal and empirically demonstrate the performance of our proposed stopping times.en_US
dc.description.sponsorshipAgency for Science, Technology and Research (A*STAR)en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.language.isoenen_US
dc.relationMOE2018-T2-2- 019en_US
dc.relationRIE2020en_US
dc.relationA19D6a0053en_US
dc.relation.ispartofIEEE Transactions on Signal Processingen_US
dc.rights© 2021 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. The published version is available at: https://doi.org/10.1109/TSP.2021.3057258.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleAsymptotically optimal sampling policy for quickest change detection with observation-switching costen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doi10.1109/TSP.2021.3057258-
dc.description.versionAccepted versionen_US
dc.identifier.scopus2-s2.0-85100831096-
dc.identifier.volume69en_US
dc.identifier.spage1332en_US
dc.identifier.epage1346en_US
dc.subject.keywordsQuickest Change Detectionen_US
dc.subject.keywordsSampling Policyen_US
dc.description.acknowledgementThis work was supported in part by the Singapore Ministry of Education Academic Research Fund Tier 2 under Grant MOE2018-T2-2- 019 and in part by A*STAR under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund - Pre Positioning (IAF-PP) under Grant A19D6a0053.en_US
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