Evaluation of Coded Aperture Radiation Detectors using a Bayesian Approach

TL;DR

This paper evaluates the effectiveness of coded aperture radiation detectors for roadside threat detection, comparing masked and unmasked approaches through simulations to determine their benefits in detection and localization tasks.

Contribution

It introduces a Bayesian framework to assess the utility of coded aperture techniques in roadside radiation detection scenarios, highlighting when masking improves performance.

Findings

Coded aperture can enhance directional detection in certain scenarios.

Masking impacts detection and localization performance.

Simulation results guide optimal use of coded aperture in threat detection.

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 measure performance over many instances of a drive-by scenario via Monte Carlo simulation based on empirical observations.