DISTRIBUTED DETECTION OF A NUCLEAR RADIOACTIVE SOURCE BASED ON A HIERARCHICAL SOURCE MODEL

Detection of a nuclear radioactive source is considered using a parallel sensor network architecture and a fusion center. A Poisson-Gamma hierarchical model is used to represent the distribution of the count data received by the sensors. Local sensors are assumed to be single threshold binary quantizers that send a vector of sensor decisions over time to the fusion center for global decision-making. Using the developed count model, a generalized likelihood ratio test (GLRT) using a restricted range MLE (RMLE) is proposed to declare the global decision. The performance improvement resulting from using the restricted range MLE over the unrestricted MLE while implementing the GLRT is depicted using simulated as well as real data collected from a test-bed using radiation sensors. Using bootstrap, 95% confidence bounds on the ROC curves, evaluated using real data, are obtained.