Threat determination for radiation detection from the Remote Sensing Laboratory

TL;DR

This paper discusses a method for improving radiation source detection by using multiple detection systems with machine learning algorithms to distinguish threats from benign sources, aiding homeland security efforts.

Contribution

It introduces a collection of detection systems employing soft-sensing algorithms and machine learning to effectively filter non-threatening radiation sources in field operations.

Findings

Enhanced discrimination between threat and non-threat sources

Integration of machine learning improves detection accuracy

Reduces false alarms in radiation search operations

Abstract

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.