Misspecified and Asymptotically Minimax Robust Quickest Change Detection
Misspecified and Asymptotically Minimax Robust Quickest Change Detection
复制标题
错误指定和渐近极小极大稳健最快变化检测
DOI：
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发表时间：
2017
影响因子：
5.4
通讯作者：
J. Ford
中科院分区：
文献类型：
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作者：
Timothy L. Molloy;J. Ford
我们调查的一个未知的变化，在一个随机过程中产生的独立和同分布的意见的分布的最快检测。我们制定了新的界限上的误指定的累积和（CIMUM）规则的性能，并提出了极大极小的强大版本的流行的Lorden和Pollak标准与多项式（或高阶矩）检测延迟处罚。通过利用我们的研究结果误指定的EQUIUM规则，我们确定解决方案，我们强大的最快的变化检测问题的渐近制度的几个误报。在以前的强大的最快的变化检测治疗相反，我们的渐近结果保持在宽松的条件下的不确定性集可能的变化前和变化后的分布。我们在模拟中说明了我们的结果，并将其应用于在低信噪比设置（即，弱目标机动检测）。
We investigate the quickest detection of an unknown change in the distribution of a stochastic process generating independent and identically distributed observations. We develop new bounds on the performance of misspecified cumulative sum (CUSUM) rules, and pose minimax robust versions of the popular Lorden and Pollak criteria with polynomial (or higher order moment) detection delay penalties. By exploiting our results for misspecified CUSUM rules, we identify solutions to our robust quickest change detection problems in the asymptotic regime of few false alarms. In contrast to previous robust quickest change detection treatments, our asymptotic results hold under relaxed conditions on the uncertainty sets of possible prechange and postchange distributions. We illustrate our results in simulations and apply them to the problem of detecting target manoeuvres in low signal-to-noise ratio settings (i.e., dim-target manoeuvre detection).