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Fusion of Threshold Rules for Target Detection in Wireless Sensor Networks

机译:无线传感器网络中用于目标检测的阈值规则融合

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摘要

We propose a binary decision fusion rule that reaches a global decision on the presence of a target by integrating local decisions made by multiple sensors. Without requiring a priori probability of target presence, the fusion threshold bounds derived using Chebyshev's inequal-rnity ensure a higher hit rate and lower false alarm rate compared to the weighted averages of individual sensors. The Monte Carlo-based simulation results show that the proposed approach significantly improves target detection performance, and can also be used to guide the actual threshold selection in practical sensor network implementation under certain error rate constraints.
机译:我们提出了一种二进制决策融合规则,该规则通过集成多个传感器做出的局部决策来对目标的存在做出全局决策。与单个传感器的加权平均值相比,无需先验概率即可出现目标,与其他传感器的加权平均值相比,使用切比雪夫不等式得出的融合阈值边界可确保更高的命中率和更低的误报率。基于蒙特卡洛的仿真结果表明,该方法可显着提高目标检测性能,在某些误差率约束下,还可用于指导实际传感器网络实现中的实际阈值选择。

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