Toward Uncertainty Aware Quickest Change Detection

James Zachary Hare, Lance Kaplan, Venugopal V. Veeravalli

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

Abstract

We study the problem of Quickest Change Detection (QCD) where the parameters of both the pre- and post-change distributions are completely unknown or known within a second-order distribution generated from training data. We propose the use of the Uncertain Likelihood Ratio (ULR) test statistic, which is designed from a Bayesian perspective in contrast with the traditional frequentist approach, i.e., the Generalized Likelihood Ratio (GLR) test. The ULR test utilizes a ratio of posterior predictive distributions, which incorporates parameter uncertainty into the likelihood estimates when there is a lack of or limited availability of training samples. Through an empirical study, we show that the proposed test outperforms the GLR test, while achieving similar results as the classical CUSUM algorithm as the number of training samples goes to infinity.

Original languageEnglish (US)
Title of host publicationProceedings of 2021 IEEE 24th International Conference on Information Fusion, FUSION 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781737749714
StatePublished - 2021
Event24th IEEE International Conference on Information Fusion, FUSION 2021 - Sun City, South Africa
Duration: Nov 1 2021Nov 4 2021

Publication series

NameProceedings of 2021 IEEE 24th International Conference on Information Fusion, FUSION 2021

Conference

Conference24th IEEE International Conference on Information Fusion, FUSION 2021
Country/TerritorySouth Africa
CitySun City
Period11/1/2111/4/21

Keywords

  • Limited Training Data
  • Quickest Change Detection
  • Uncertain Likelihood Ratio

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing
  • Information Systems and Management

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