Evaluation of Coded Aperture Radiation Detectors Using a Bayesian Approach

Abstract

We investigate the utility of coded aperture (CA) for roadside radiation threat detection applications. With coded aperture, information in the form of photon quantity is traded for directional information. Whether and in what scenarios this trade-off is beneficial is the focus of this study. We quantify the impact of a masking approach by comparing performance with an unmasked approach in terms of both detection and localization of a roadside nuclear threat. We simulate many instances of a drive-by scenario via Monte Carlo using empirical observations from the RadMap data to obtain background photons and synthetic injection of threat source. Simulation results suggest that the CA detector suffers significant loss of detection probability for weak sources, but only slightly for moderate source intensities. The masked approach also demonstrates consistent improvement in localization performance across all source intensities investigated. From these experiments we can begin to draw boundaries around the problem space in which CA detectors provide positive utility.