The ability to search for radiation sources is of interest to the Homeland Security community. The hope is to find any radiation sources which may pose a reasonable chance for harm in a terrorist act. The best chance of success for search operations generally comes with fielding as many detection systems as possible. In doing this, the hoped for encounter with the threat source will inevitably be buried in an even larger number of encounters with non-threatening radiation sources commonly used for many medical and industrial use. The problem then becomes effectively filtering the non-threatening sources, and presenting the human-in-the-loop with a modest list of potential threats. Our approach is to field a collection of detection systems which utilize soft-sensing algorithms for the purpose of discriminating potential threat and non-threat objects, based on a variety of machine learning techniques.
@article{arxiv.1908.11207,
title = {Threat determination for radiation detection from the Remote Sensing Laboratory},
author = {William P. Ford and Emma Hague and Tom McCullough and Eric Moore and Johanna Turk},
journal= {arXiv preprint arXiv:1908.11207},
year = {2021}
}