Quickest Change-Point Detection Over Multiple Data Streams via Sequential Observations
Quickest Change-Point Detection Over Multiple Data Streams via Sequential Observations
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DOI：
10.1109/icassp.2018.8461647
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Jun Geng;L. Lai
中科院分区：
文献类型：
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作者：
Jun Geng;L. Lai
考虑了快速检测发生在多个独立数据流之一上的异常事件的问题。在所考虑的问题中，初始时所有数据流都处于正态状态，并由概率分布$P_{0}$产生。在某个未知的时间，发生了一个不寻常的事件，一个数据流的分布被修改为$P_{1}$，而其余的分布保持不变。观察者一次只能观察一个数据流。观测器通过顺序观察，设计在线停止规则和数据流切换规则，以最小化检测延迟，即异常事件发生与报警时间之间的时间差，同时控制虚警率。在非贝叶斯最快速检测框架下对该问题进行建模，提出了一种基于CUSUM统计量的检测方法。我们证明了这种检测方法是渐近最优的。
The problem of quickly detecting the occurrence of an unusual event that happens on one of multiple independent data streams is considered. In the considered problem, all data streams at the initial are under normal state and are generated by probability distribution $P_{0}$. At some unknown time, an unusual event happens and the distribution of one data stream is modified to $P_{1}$ while the distributions of the rest remain unchange. The observer can only observe one data stream at one time. With his sequential observations, the observer wants to design an online stopping rule and a data stream switching rule to minimize the detection delay, namely the time difference between the occurrence of the unusual event and the time of raising an alarm, while keeping the false alarm rate under control. We model the problem under non-Bayesian quickest detection framework, and propose a detection procedure based on the CUSUM statistic. We show that this proposed detection procedure is asymptotically optimal.