能量和带宽约束下相关随机过程的分布式检测

Juan Augusto Maya, L. Vega, C. Galarza, A. Altieri
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引用次数: 2

摘要

分析了一个基于无线传感器网络的二元假设检验问题。在Neyman-Pearson框架下,利用大偏差理论(LDT)计算了一种检测相关圆对称复高斯过程的分布式方案的概率误差指数。使用模拟方案,传感器通过多址通道(MAC)多次传输其测量值的缩放版本,以到达融合中心(FC),其任务是确定过程是否存在。在分析中,我们考虑了每个节点传输的能量约束。我们表明,当检测相关高斯过程时,所提出的分布式方案需要相对较少的MAC通道来实现集中的误差指数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Distributed detection of correlated random processes under energy and bandwidth constraints
We analyze a binary hypothesis testing problem built on a wireless sensor network (WSN). Using Large Deviation Theory (LDT), we compute the probability error exponents of a distributed scheme for detecting a correlated circularly-symmetric complex Gaussian process under the Neyman-Pearson framework. Using an analog scheme, the sensors transmit scaled versions of their measurements several times through a multiple access channel (MAC) to reach the fusion center (FC), whose task is to decide whether the process is present or not. In the analysis, we consider the energy constraint on each node transmission. We show that the proposed distributed scheme requires relatively few MAC channel uses to achieve the centralized error exponents when detecting correlated Gaussian processes.
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