Enhancing Test Statistics by Utilizing Data Patterns in Sequential Measurement Strings in Radiation Detection

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Abstract

Operational health physics applications, such as radiological and nuclear monitoring and detection for homeland security or radiation protection purposes, generate time sequences of independent individual measurement data. Statistical algorithms have been developed that utilize the analysis of patterns in data strings to enhance the test statistic for the decision on the absence or presence of a radiological source. Theoretical expectations have been verified in laboratory measurements for various lengths of data strings. Null hypothesis test performance and source detection efficacy have been shown to improve compared to the traditional method of achieving a detection decision by the comparison of a measured value to a fixed decision threshold.

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