Sequential (Quickest) Change Detection: Classical Results and New Directions

被引：62
作者：
Xie L. [1 ]
Zou S. [2 ]
Xie Y. [1 ]
Veeravalli V.V. [3 ]
机构：
[1] H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA
[2] Department of Electrical Engineering, University at Buffalo, The State University of New York, Buffalo, 14260, NY
[3] Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, 61801, IL
来源：
IEEE Journal on Selected Areas in Information Theory | 2021年 / 2卷 / 02期
关键词：
change point detection; network applications; Sequential analysis; sequential detection; time series;
D O I：
10.1109/JSAIT.2021.3072962
中图分类号：
学科分类号：
摘要：
Online detection of changes in stochastic systems, referred to as sequential change detection or quickest change detection, is an important research topic in statistics, signal processing, and information theory, and has a wide range of applications. This survey starts with the basics of sequential change detection, and then moves on to generalizations and extensions of sequential change detection theory and methods. We also discuss some new dimensions that emerge at the intersection of sequential change detection with other areas, along with a selection of modern applications and remarks on open questions. © 2020 IEEE.
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页码：494 / 514
页数：20
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