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首页> 外文会议>Thirty-Fifth Annual Conference on Information Sciences and Systems Vol.2, Mar 21-23, 2001, Baltimore, Maryland >Distributed Detection using Parley with Soft Decisions
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Distributed Detection using Parley with Soft Decisions

机译：使用具有软决策的Parley进行分布式检测

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摘要

In this paper, we propose an extension to the nth root parley distributed detection algorithm of Swaszek and Willet. Instead of making a single "hard" decision at each sensor node, a two bit quantizer is used to choose the hypothesis and also to provide a confidence measure of this decision. These "soft" decisions are broadcast to all nodes, and they are used to create a stopping rule that reduces the number of parleys. For the Bayesian criterion, the probability of error is unchanged, and it is equal to that of a central processor; for the Neyman-Pearson criterion, the receiver operating curve is essentially the same as that of a central processor. The performance is also compared to that obtained using one bit decision makers and the majority fusion rule. Simulation results are provided for the Gaussian shift in mean problem assuming an ideal channel and the binary symmetric channel.
机译：在本文中，我们提出了对Swaszek和Willet的第n个根parley分布式检测算法的扩展。代替在每个传感器节点上做出单个“硬”决定，而是使用两位量化器来选择假设，并提供对该决定的置信度。这些“软”决策被广播到所有节点，它们被用来创建一个减少规则的停止规则。对于贝叶斯准则，错误的概率是不变的，它等于中央处理器的概率。对于Neyman-Pearson准则，接收器的工作曲线与中央处理器的曲线基本相同。还将性能与使用一位决策者和多数融合规则获得的性能进行比较。假设理想通道和二进制对称通道，为均值问题的高斯平移提供了仿真结果。

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