Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/137341
Title: | Quickest change detection under a nuisance change | Authors: | Lau, Tze Siong Tay, Wee Peng |
Keywords: | Engineering::Electrical and electronic engineering | Issue Date: | 2018 | Source: | Lau, T. S., & Tay, W. P. (2018). Quickest change detection under a nuisance change. Proceedings of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 6643-6647. doi:10.1109/icassp.2018.8462436 | Conference: | 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) | Abstract: | We consider the problem of quickest change detection (QCD) for a signal which may undergo both a nuisance and a critical change. Our goal is to detect the critical change without raising a false alarm over the nuisance change. An optimal sequential change detection procedure is proposed for the Bayesian formulation of our QCD problem. A sequential change detection procedure based on the generalized likelihood ratio test (GLRT) statistic is also proposed for the non-Bayesian formulation. We show that our proposed test statistics can be computed efficiently via respective recursive update schemes. We compare our proposed stopping rules with the naive 2-stage procedures, which attempt to detect the changes using separate optimal stopping procedures (i.e., the Shiryaev procedure in the Bayesian formulation, and the CuSum procedure in the non-Bayesian formulation) for the nuisance and critical changes. Simulations demonstrate that our proposed rules outperform the 2-stage procedures. | URI: | https://hdl.handle.net/10356/137341 | ISBN: | 9781538646588 | DOI: | 10.1109/ICASSP.2018.8462436 | Schools: | School of Electrical and Electronic Engineering | Rights: | © 2018 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/ICASSP.2018.8462436 | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | EEE Conference Papers |
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