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Optimization of distributed detection systems under the minimum average misclassification risk criterion

机译:在最小平均误分类风险标准下优化分布式检测系统

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摘要

A common model for distributed detection systems is that of several separated sensors each of which measures some observable, quantizes it, and communicates to a fusion center the quantized observation. The fusion center collects the quantized observations and takes the decision. The article deals with the design of the quantizers and of the fusion center under a rate constraint. The system of interest allows soft nonbreakpoint quantizers and nonindependent observations. Our finding is that locally optimal design of the distributed detection system is feasible via alternate minimization of the average misclassification risk.
机译:分布式检测系统的通用模型是几个分离的传感器的模型,每个传感器测量一些可观察到的东西,对其进行量化,然后将量化的观测结果传达给融合中心。融合中心收集量化的观测值并做出决定。本文讨论了在速率约束下量化器和融合中心的设计。感兴趣的系统允许软非断点量化器和非独立观测。我们的发现是,通过交替最小化平均错误分类风险,可以对分布式检测系统进行局部优化设计。

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