Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152708
Title: Asymptotically optimal sampling policy for quickest change detection with observation-switching cost
Authors: Lau, Tze Siong
Tay, Wee Peng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Lau, 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.2021.3057258
Project: MOE2018-T2-2- 019
RIE2020
A19D6a0053
Journal: IEEE Transactions on Signal Processing
Abstract: We 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.
URI: https://hdl.handle.net/10356/152708
ISSN: 1053-587X
DOI: 10.1109/TSP.2021.3057258
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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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