PNNL

Abstract

Algorithms for detecting illicit gamma radiation sources must respond to a wide variety of threat signatures and ignore a wider variety of clutter. Previously, we described the Nuisance-rejection Spectral Comparison Ratio Anomaly Detection (N-SCRAD) method for indicating the presence of threat sources during mobile search operations. Our method relies on the formulation of radiation alarm test statistics applied to detector counts in numerous spectral regions of interest and our test statistics are standardized by local estimates of background variability. In this work we present a new automated procedure for selecting test statistics and specifying the regions of interest based on minimizing detectable count rates of prototype threat spectra at a specified false positive rate using simulated annealing. The new procedure can be applied to configuring the detection algorithm when the prototype threats are many and diverse without significantly compromising the detection sensitivity to any one of them. N-SCRAD makes use of estimates of background variability obtained during a search from prior measurements of background. In applications such estimates made purely from past measurements sometimes do not extrapolate well to current conditions. In this work we present a new method for correcting those estimates to current conditions of the total count rate. We demonstrate that the correction lowers detection thresholds for a specified false positive rate, enabling greater sensitivity to targets. The two proposed improvements to N-SCRAD maximize detection power for the expected variety of possible threats while reducing or maintaining the false positive rate.