  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.
