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Decision making in sensor networks observing poisson processes

This paper addresses a detection problem where several spatially distributed sensors independently observe a time-inhomogeneous stochastic process. The task is to decide at the end of a fixed time interval between two hypotheses regarding the statistics of the observed process. 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 it serves as a first step toward the development of novel fixed-interval detection algorithms that combine decentralized processing with optimal centralized decision making.