PNNL

Abstract

A novel algorithm approach to evaluating data from PVT-based Radiation Portal Monitor (RPM) systems is established. Time series of data from RPMs are evaluated for the presence of sources of interest by comparing the background to the vehicle spectrum at each successive time step, isolating the contribution of anomalous radiation. At each time in the data sequence, a “spectral distance” index is calculated using this method. This method may dramatically reduce systematic fluctuations due to background attenuation by a vehicle (the so-called “shadow shielding” effect), and allow for time-series matched filtering for discrimination of compact anomalous sources. This is attempted by using a wavelet filter function of similar size to the expected source profile on the output of the spectral distance method. Performance of this method is shown by analysis (injection studies) of a number of real drive-through data sets taken at a U.S. port of entry. Spectra from isotopes of interest are injected into the data set, and the resultant “benign” and “injected” data sets are analyzed with gross-counting, spectral distance, and spatial algorithms. The combination of spectral and spatial analysis methods showed a significant increase to detection performance.