分布式检测的可扩展多水平量化

G. Gul, Michael Basler
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引用次数: 1

摘要

在分布式传感器网络中,多个传感器向融合中心发送其观测值的汇总信息以进行最终决策,提出了一种可扩展的传感器观测值多级量化算法。该算法直接最小化网络的整体错误概率,而不需要最小化伪目标函数(如概率分布之间的距离)。该问题的表述可以考虑融合中心的全局最优误差最小化和每个传感器的逐人最优量化。该算法的复杂度是准线性的。实验结果表明,该方案优于目前的技术水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scalable Multilevel Quantization for Distributed Detection
A scalable algorithm is derived for multilevel quantization of sensor observations in distributed sensor networks, which consist of a number of sensors transmitting a summary information of their observations to the fusion center for a final decision. The proposed algorithm is directly minimizing the overall error probability of the network without resorting to minimizing pseudo objective functions such as distances between probability distributions. The problem formulation makes it possible to consider globally optimum error minimization at the fusion center and a person-by-person optimum quantization at each sensor. The complexity of the algorithm is quasi-linear for i.i.d. sensors. Experimental results indicate that the proposed scheme is superior in comparison to the current state-of-the-art.
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