@inproceedings{853f36daa485407886929fe9afe89772,
title = "Multistream quickest change detection: Asymptotic optimality under a sparse signal",
abstract = "In multichannel sequential change detection, multiple sensors monitor a system in which an abrupt change occurs at some unknown time and is perceived by an unknown subset of sensors. The goal is to detect this change quickly, while controlling the rate of false alarms. In the traditional asymptotic analysis of this problem, the false alarm rate goes to 0 while all other parameters remain fixed. We argue that this framework is not very informative, as the corresponding asymptotic optimality property cannot differentiate between universal and parsimonious rules. We propose an asymptotic framework in which the number of sensors also goes to infinity, and we show that in this context universal rules may fail to be asymptotically optimal when the number of streams is not very small. On the other hand, parsimonious rules are shown to be asymptotically optimal under reasonable sparsity conditions.",
keywords = "CUSUM, Multichannel, Multisensor, Sequential Change Detection, Sparse",
author = "Georgios Fellouris and Moustakides, {George V.} and Veeravalli, {Venu V.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7953397",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6444--6447",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
address = "United States",
}