Multistream quickest change detection: Asymptotic optimality under a sparse signal
Georgios Fellouris, George V. Moustakides, Venugopal V. Veeravalli
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.
BibTeX
@inproceedings{icassp2017_multistreamquick,
title = {Multistream quickest change detection: Asymptotic optimality under a sparse signal},
author = {Georgios Fellouris and George V. Moustakides and Venugopal V. Veeravalli},
booktitle = {ICASSP 2017},
year = {2017}
}