Quickest Change Detection with Confusing Change
Abstract
In the problem of quickest change detection (QCD), a change occurs at some unknown time in the distribution of a sequence of independent observations. This work studies a QCD problem where the change is either a bad change, which we aim to detect, or a confusing change, which is not of our interest. Our objective is to detect a bad change as quickly as possible while avoiding raising a false alarm for pre-change or a confusing change. We identify a specific set of pre-change, bad change, and confusing change distributions that pose challenges beyond the capabilities of standard Cumulative Sum (CuSum) procedures. Proposing novel CuSum-based detection procedures, S-CuSum and J-CuSum, leveraging two CuSum statistics, we offer solutions applicable across all kinds of pre-change, bad change, and confusing change distributions. For both S-CuSum and J-CuSum, we provide analytical performance guarantees and validate them by numerical results. Furthermore, both procedures are computationally efficient as they only require simple recursive updates.
Keywords
Cite
@article{arxiv.2405.00842,
title = {Quickest Change Detection with Confusing Change},
author = {Yu-Zhen Janice Chen and Jinhang Zuo and Venugopal V. Veeravalli and Don Towsley},
journal= {arXiv preprint arXiv:2405.00842},
year = {2024}
}