Bayesian Quickest Detection in Sensor Arrays
Bayesian Quickest Detection in Sensor Arrays
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DOI：
10.1080/07474946.2012.719437
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发表时间：
2012-10
期刊：
影响因子：
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通讯作者：
M. Ludkovski
中科院分区：
文献类型：
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作者：
M. Ludkovski
摘要研究了传感器阵列的贝叶斯最快检测问题。一个潜在的信号被假定为逐渐传播通过一个网络的几个传感器，触发级联的相互依赖的变点。决策者的目标是集中融合所有可用信息，以找到最小化贝叶斯风险的最佳检测规则。我们开发了一个易于处理的连续时间制定这个问题的情况下，传感器收集点过程的观察和监测所产生的变化强度和类型的观察事件。我们的方法使用的非线性滤波和最佳停止的方法，并借给自己一个有效的数值方案，结合粒子滤波与基于蒙特卡洛的方法来动态规划。所建立的模型和算法都用大量的数值算例进行了说明。
Abstract We study Bayesian quickest detection problems with sensor arrays. An underlying signal is assumed to gradually propagate through a network of several sensors, triggering a cascade of interdependent change-points. The aim of the decision maker is to centrally fuse all available information to find an optimal detection rule that minimizes Bayes risk. We develop a tractable continuous-time formulation of this problem focusing on the case of sensors collecting point process observations and monitoring the resulting changes in intensity and type of observed events. Our approach uses methods of nonlinear filtering and optimal stopping and lends itself to an efficient numerical scheme that combines particle filtering with a Monte Carlo–based approach to dynamic programming. The developed models and algorithms are illustrated with plenty of numerical examples.