Networked Decision Making for Poisson Processes: Application to nuclear detection
Abstract
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.
Cite
@article{arxiv.1210.1464,
title = {Networked Decision Making for Poisson Processes: Application to nuclear detection},
author = {Chetan D. Pahlajani and Ioannis Poulakakis and Herbert G. Tanner},
journal= {arXiv preprint arXiv:1210.1464},
year = {2012}
}