Quickest Change Detection with Confusing Change

Yu-Zhen Janice Chen; Jinhang Zuo; Venugopal V. Veeravalli; Don Towsley

doi:10.1109/IEEECONF60004.2024.10942912

Quickest Change Detection with Confusing Change

Yu-Zhen Janice Chen
, Jinhang Zuo
, Venugopal V. Veeravalli
, Don Towsley

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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. © 2024 IEEE.

Original language	English
Title of host publication	Conference Record of The Fifty-Eighth Asilomar Conference on Signals, Systems & Computers
Editors	Michael B. Matthews
Publisher	IEEE
Pages	1768-1773
Number of pages	6
ISBN (Electronic)	9798350354058
ISBN (Print)	9798350354065
DOIs	https://doi.org/10.1109/IEEECONF60004.2024.10942912
Publication status	Published - Oct 2024
Externally published	Yes
Event	58th Asilomar Conference on Signals, Systems and Computers (ACSSC 2024) - Virtual, Pacific Grove, United States Duration: 27 Oct 2024 → 30 Oct 2024

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393
ISSN (Electronic)	2576-2303

Conference

Conference	58th Asilomar Conference on Signals, Systems and Computers (ACSSC 2024)
Place	United States
City	Pacific Grove
Period	27/10/24 → 30/10/24

Funding

The authors would like to thank Lance Kaplan for invaluable insights and engaging discussions throughout the development of this paper. This research is supported by the Army Research Laboratory under Cooperative Agreement W911NF-17-2-0196 (IoBT CRA).

Access Output

10.1109/IEEECONF60004.2024.10942912

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

Chen, Y.-Z. J., Zuo, J., Veeravalli, V. V., & Towsley, D. (2024). Quickest Change Detection with Confusing Change. In M. B. Matthews (Ed.), Conference Record of The Fifty-Eighth Asilomar Conference on Signals, Systems & Computers (pp. 1768-1773). (Conference Record - Asilomar Conference on Signals, Systems and Computers). IEEE. https://doi.org/10.1109/IEEECONF60004.2024.10942912

@inproceedings{5d204b51fc7548afabb272dfaa640061,

title = "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. {\textcopyright} 2024 IEEE.",

author = "Chen, \{Yu-Zhen Janice\} and Jinhang Zuo and Veeravalli, \{Venugopal V.\} and Don Towsley",

year = "2024",

month = oct,

doi = "10.1109/IEEECONF60004.2024.10942912",

language = "English",

isbn = "9798350354065",

series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

publisher = "IEEE",

pages = "1768--1773",

editor = "Matthews, \{Michael B.\}",

booktitle = "Conference Record of The Fifty-Eighth Asilomar Conference on Signals, Systems \& Computers",

address = "United States",

note = "58th Asilomar Conference on Signals, Systems and Computers (ACSSC 2024) ; Conference date: 27-10-2024 Through 30-10-2024",

}

Chen, Y-ZJ, Zuo, J, Veeravalli, VV & Towsley, D 2024, Quickest Change Detection with Confusing Change. in MB Matthews (ed.), Conference Record of The Fifty-Eighth Asilomar Conference on Signals, Systems & Computers. Conference Record - Asilomar Conference on Signals, Systems and Computers, IEEE, pp. 1768-1773, 58th Asilomar Conference on Signals, Systems and Computers (ACSSC 2024), Pacific Grove, California, United States, 27/10/24. https://doi.org/10.1109/IEEECONF60004.2024.10942912

Quickest Change Detection with Confusing Change. / Chen, Yu-Zhen Janice; Zuo, Jinhang; Veeravalli, Venugopal V. et al.
Conference Record of The Fifty-Eighth Asilomar Conference on Signals, Systems & Computers. ed. / Michael B. Matthews. IEEE, 2024. p. 1768-1773 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Quickest Change Detection with Confusing Change

AU - Chen, Yu-Zhen Janice

AU - Zuo, Jinhang

AU - Veeravalli, Venugopal V.

AU - Towsley, Don

PY - 2024/10

Y1 - 2024/10

N2 - 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. © 2024 IEEE.

AB - 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. © 2024 IEEE.

UR - https://www.scopus.com/pages/publications/105002693572

UR - https://www.scopus.com/pages/publications/105002693572#tab=impact

U2 - 10.1109/IEEECONF60004.2024.10942912

DO - 10.1109/IEEECONF60004.2024.10942912

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9798350354065

T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers

SP - 1768

EP - 1773

BT - Conference Record of The Fifty-Eighth Asilomar Conference on Signals, Systems & Computers

A2 - Matthews, Michael B.

PB - IEEE

T2 - 58th Asilomar Conference on Signals, Systems and Computers (ACSSC 2024)

Y2 - 27 October 2024 through 30 October 2024

ER -

Quickest Change Detection with Confusing Change

Abstract

Publication series

Conference

Funding

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this