Methods for optimization of the signature-based radiation scanning approach for detection of nitrogen-rich explosives

Abstract

The signature-based radiation scanning (SBRS) technique can be used to rapidly detect nitrogen-rich explosives at standoff distances. This technique uses a template-matching procedure that produces a figure-of-merit (FOM) whose value is used to distinguish between inert and explosive materials. The present study develops a tiered-filter implementation of the signature-based radiation scanning technique, which reduces the number of templates needed. This approach starts by calculating a normalized FOM between signatures from an unknown target and an explosive template through stages or tiers (nitrogen first, then oxygen, then carbon, and finally hydrogen). If the normalized FOM is greater than a specified cut-off value for any of the tiers, the target signatures are considered not to match that specific template and the process is repeated for the next explosive template until all of the relevant templates have been considered. If a target’s signatures match all the tiers of a single template, then the target is assumed to contain an explosive. The tiered filter approach uses eight elements to construct artificial explosive-templates that have the function of representing explosives cluttered with real materials. The feasibility of the artificial template approach to systematically build a library of templates that successfully differentiates explosive targets from inert ones in the presence of clutter and under different geometric configurations was explored. In total, 10 different geometric configurations were simulated and analyzed using the MCNP5 code. For each configuration, 51 different inert materials were used as inert samples and as clutter in front of the explosive cyclonite (RDX). The geometric configurations consisted of different explosive volumes, clutter thicknesses, and distances of the clutter from the neutron source. Additionally, an objective function was developed to optimize the parameters that maximize the sensitivity and specificity of the method.