Improved Radioisotope Identification and Classification Algorithm

Sonar and speech techniques have been investigated to improve functionality and enable handheld and other man-portable, mobile, and portal systems to positively detect and identify illicit nuclear materials, with minimal data and with minimal false positives and false negatives. RadSonar isotope detection and identification is an algorithm development project funded by NA-22 and employing the resources of Savannah River National Laboratory and three University Laboratories (JHU-APL, UT-ARL, and UW-APL). Algorithms have been developed that improve the probability of detection and decrease the number of false positives and negatives. Two algorithms have been developed and tested. The first algorithm uses support vector machine (SVM) classifiers to determine the most prevalent nuclide(s) in a spectrum. It then uses a constrained weighted least squares fit to estimate and remove the contribution of these nuclide(s) to the spectrum, iterating classification and fitting until there is nothing of significance left. If any Special Nuclear Materials (SNMs) were detected in this process, a second tier of more stringent classifiers are used to make the final SNM alert decision. The second algorithm is looking at identifying existing feature sets that would be relevant in the radioisotope identification context. The underlying philosophy here is to identify parallels between the physics and/or the structures present in the data for the two applications (speech analysis and gamma spectroscopy). The expectation is that similar approaches may work in both cases. The mel-frequency cepstral representation of spectra is widely used in speech, particularly for two reasons: approximation of the response of the human ear, and simplicity of channel effect separation (in this context, a \"channel\" is a method of signal transport that affects the signal, examples being vocal tract shape, room echoes, and microphone response). Measured and simulated gamma-ray spectra from a hand-held Radioisotope Identification Device were used to evaluate the algorithms. This paper will present and discuss results of the Test and Evaluation performed on two algorithms produced from the project.