Multiple Hypothesis Testing Framework for Spatial Signals

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.

Automated summary

In this study, a general framework based on multiple hypothesis testing is developed to identify regions with spatially interesting, different or adversarial behavior. The framework utilizes a discrete spatial grid and a large-scale sensor network to acquire measurements, and involves estimating local false discovery rates and interpolating them to assign locations to regions associated with different hypotheses.