Multislot and Multistream Quickest Change Detection in Statistically Periodic Processes

被引：0
作者：
Banerjee, Taposh [1 ]
Gurram, Prudhvi [2 ,3 ]
Whipps, Gene [3 ]
机构：
[1] Univ Texas San Antonio, San Antonio, TX 78249 USA
[2] Booz Allen Hamilton, Mclean, VA 22102 USA
[3] CCDC Army Res Lab, Adelphi, MD 20783 USA
来源：
2020 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY (ISIT) | 2020年
基金：
美国国家科学基金会;
关键词：
D O I：
10.1109/isit44484.2020.9174470
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Mixture-based algorithms are proposed for detecting a change in the distribution of a statistically periodic process in multislot and multistream settings. In the multislot change detection problem, the distribution of the observed process can change in any subset of time slots in each period. In the multistream change detection problem, there are parallel streams of observations, and the change can affect an arbitrary subset of streams. It is shown that the algorithms are asymptotically optimal in a Bayesian setting.
引用
收藏
页码：1147 / 1152
页数：6
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