Data-efficient minimax quickest change detection
Data-efficient minimax quickest change detection
复制标题
数据高效的极小极大最快变化检测
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
V. Veeravalli
中科院分区：
文献类型：
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作者：
T. Banerjee;V. Veeravalli
在文献[1]中，在虚警概率和观测代价的约束下，给出了一种快速检测随机变量序列分布变化的贝叶斯双阈值算法。该算法被证明是渐近最优的，并有良好的权衡曲线。本文将文[1]中的结果推广到更实际的极大极小情形。受文献[1]中算法结构的启发，提出了一种基于观测量和观测量的算法，称为DE-观测量和观测量，可用于开关观测控制，并在虚警约束下尽可能快地检测变化。结果表明，DE-DENUM算法继承了文献[1]中算法的优良性质，它也是渐近最优的，并且具有良好的折衷曲线。数值结果表明，该算法提供了一个显着的节省观测成本的分数采样的天真的方法。
In [1], a Bayesian two-threshold algorithm was obtained for quickest detection of a change in the distribution of a sequence of random variables, subject to constraints of probability of false alarm and observation cost. This algorithm was shown to be asymptotically optimal and to have good trade-off curves. In this paper, the results in [1] are extended to the more practically relevant minimax setting. Motivated by the structure of the algorithm developed in [1], a CUSUM based algorithm, called DE-CUSUM is proposed, which can be used for on-off observation control and to detect change as quickly as possible subject to a false alarm constraint. It is shown that the DE-CUSUM algorithm inherits the good qualities of the algorithm in [1], i.e., it is also asymptotically optimal and has good trade-off curves. Numerical results show that the DE-CUSUM algorithm provides a substantial savings in the observation cost over the naive approach of fractional sampling.