Networked Decision Making for Poisson Processes: Application to nuclear detection
This addresses detection challenges in nuclear security by enabling decentralized processing with centralized decision-making, though it appears incremental as it builds on existing Neyman-Pearson methods.
The paper tackles the problem of detecting mobile radioactive sources using spatially distributed sensors observing a time-inhomogeneous stochastic process, achieving an optimal decision in the Neyman-Pearson framework by combining locally processed likelihood ratios at a fusion center.
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.