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Multiple Hypothesis Testing Framework for Spatial Signals
IEEE Transactions on Signal and Information Processing over Networks ( IF 3.2 ) Pub Date : 2022-07-14 , DOI: 10.1109/tsipn.2022.3190735
Martin Golz 1 , Abdelhak M. Zoubir 1 , Visa Koivunen 2
Affiliation  

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.

中文翻译:

空间信号的多重假设检验框架

识别空间有趣、不同或对抗行为的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。在这项工作中,我们开发了一个源自多个假设检验的通用框架来识别这些区域。为被监控的环境假设一个离散的空间网格。识别与不同假设相关的空间网格点,同时将错误发现率控制在预先指定的水平。使用大规模传感器网络获取测量结果。我们提出了一种新颖的、数据驱动的方法来估计基于矩谱方法的局部错误发现率。我们的方法与潜在物理现象的特定空间传播模型无关。它依赖于广泛适用的密度模型来进行局部汇总统计。在传感器之间,基于插值的局部错误发现率将位置分配给与不同假设相关联的区域。我们方法的好处通过在空间传播无线电波中的应用来说明。
更新日期:2022-07-14
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