Detection of Radioactive Sources in Urban Scenes Using Bayesian Aggregation of Data from Mobile Spectrometers

Abstract

Mobile radiation detector systems aim to help identify dangerous sources of radiation while minimizing frequency of false alarms caused by non-threatening nuisance sources prevalent in cluttered urban scenes. We develop methods for spatially aggregating evidence from multiple spectral observations to simultaneously detect and infer properties of threatening radiation sources. Our Bayesian Aggregation (BA) framework allows sensor fusion across multiple measurements to boost detection capability of a radioactive point source, providing several key innovations previously unexplored in the literature. Our method learns the expected Signal-to-Noise Ratio (SNR) trend as a function of source exposure using Bayesian nonparametrics to enable robust detection. The method scales well in spatial search by leveraging conditional independence and locality in Bayesian updates. The framework also allows modeling of source parameters such as intensity or type to enable property characterization of detected sources. Approaches for incorporating modeling information into BA are compared and benchmarked with respect to other data fusion techniques.