The problem of identifying regions of spatially interesting, different or adversarial behavior is inherent to many practical applications involving distributed multisensor systems. In this work, we develop a general framework stemming from multiple hypothesis testing to identify such regions. A discrete spatial grid is assumed for the monitored environment. The spatial grid points associated with different hypotheses are identified while controlling the false discovery rate at a pre-specified level. Measurements are acquired using a large-scale sensor network. We propose a novel, data-driven method to estimate local false discovery rates based on the spectral method of moments. Our method is agnostic to specific spatial propagation models of the underlying physical phenomenon. It relies on a broadly applicable density model for local summary statistics. In between sensors, locations are assigned to regions associated with different hypotheses based on interpolated local false discovery rates. The benefits of our method are illustrated by applications to spatially propagating radio waves.

翻译：确定空间上有趣、不同或敌对行为区域的问题,是涉及分布式多传感器系统的许多实际应用所固有的问题。在这项工作中,我们根据多种假设测试制定了一个总框架,以确定此类区域。为监测环境设想了一个离散的空间网格;在预先确定水平控制虚假发现率的同时,确定了与不同假设有关的空间网格点;利用大型传感器网络进行了测量;我们提出了一个新的、数据驱动的方法,根据光谱光谱波法估计当地虚假发现率。我们的方法对潜在的物理现象的具体空间传播模型是不可知的。我们的方法依靠一种广泛适用的密度模型来进行当地简要统计。在传感器之间,位置被指定给与不同假设有关的区域,这些假设以相互推导的当地虚假发现率为基础。我们方法的优点是通过对空间传播无线电波的应用来说明。