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Asynchronous distributed detection

机译：异步分布式检测

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Binary parallel distributed-detection architectures employ a bank of local detectors to observe a common volume of surveillance, and form binary local decisions about the existence or nonexistence of a target in that volume. The local decisions are transmitted to a central detector, the data fusion center (DEC), which integrates them to a global target or no target decision. Most studies of distributed-detection systems assume that the local detectors are synchronized. In practice local decisions are made asynchronously and the DFC has to update its global decision continually. In this study the number of local decisions observed by the central detector within any observation period is Poisson distributed. An optimal fusion rule is developed and the sufficient statistic is shown to be a weighted sum of the local decisions collected by the DFC within the observation interval. The weights are functions of the individual local detector performance probabilities (i.e., probabilities of false alarm and detection). In this respect the decision rule is similar to the one developed by Chair and Varshney for the synchronized system. Unlike the Chair-Varshney rule, however, the DFC's decision threshold in the asynchronous system is time varying. Exact expressions and asymptotic approximations are developed for the detection performance with the optimal rule. These expressions allow performance prediction and assessment of tradeoffs in realistic decision fusion architectures which operate over modern communication networks
机译：二进制并行分布式检测体系结构使用一组本地检测器来观察常见的监视量，并形成关于该量中目标存在或不存在的二进制本地决策。本地决策将传输到中央检测器，即数据融合中心（DEC），该中心将其集成到全局目标或无目标决策中。分布式检测系统的大多数研究都假定本地检测器是同步的。实际上，本地决策是异步进行的，DFC必须不断更新其全局决策。在这项研究中，中央观测器在任何观测期内观测到的局部决策数量都是泊松分布。制定了最佳融合规则，并且足够的统计量显示为DFC在观察间隔内收集的本地决策的加权总和。权重是各个本地检测器性能概率（即，虚警和检测概率）的函数。在这方面，决策规则类似于Chair和Varshney为同步系统开发的决策规则。但是，与Chair-Varshney规则不同，异步系统中DFC的决策阈值是随时间变化的。精确的表达式和渐近近似被开发用于具有最佳规则的检测性能。这些表达式允许在现代通信网络上运行的现实决策融合体系结构中进行性能预测和权衡评估

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