  This paper addresses a detection problem where several spatially distributed
sensors independently observe a time-inhomogeneous stochastic process. The task
is to decide between two hypotheses regarding the statistics of the observed
process at the end of a fixed time interval. In the proposed method, each of
the sensors transmits once to a fusion center a locally processed summary of
its information in the form of a likelihood ratio. The fusion center then
combines these messages to arrive at an optimal decision in the Neyman-Pearson
framework. The approach is motivated by applications arising in the detection
of mobile radioactive sources, and offers a pathway toward the development of
novel fixed- interval detection algorithms that combine decentralized
processing with optimal centralized decision making.
