Quickest change detection with unknown post-change distribution

Tze Siong Lau, Wee Peng Tay, Venugopal V. Veeravalli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper considers the problem of quickest detection of a change in distribution under the assumption that the pre-change distribution π is known, and the post-change distribution μ is unknown and belongs to a general class of distributions. Using the knowledge of the pre-change distribution π, the sample space is partitioned into equiprobable intervals and the number of samples falling into each of these intervals is monitored to detect the change. A test statistic that approximates the generalized likelihood ratio test is proposed. A recursive update scheme to compute the statistic efficiently and an approximation of the average run-length to false alarm are also derived. Simulations show that our approach is comparable in performance to two other non-parametric quickest change detection methods if the change is either a shift in distribution mean or variance, respectively. But our method significantly outperforms them if these distribution change assumptions are violated.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3924-3928
Number of pages5
ISBN (Electronic)9781509041176
DOIs
StatePublished - Jun 16 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: Mar 5 2017Mar 9 2017

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
CountryUnited States
CityNew Orleans
Period3/5/173/9/17

Keywords

  • ARL approximation
  • GLRT
  • Quickest change detection
  • non-parametric
  • unknown post-change distribution

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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