  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.
