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Feature extraction for x-ray diffraction-based explosive detection using the neural tree network

Detection of explosive materials from X-ray diffraction spectra makes use of the fact that different crystalline materials exhibit characteristic diffraction patterns composed of peaks at different energy locations. The position of the peaks in the spectra are (ideally) invariant for a given material, as are the relative heights of the peak, though to a lesser degree. However, the presence of absorbing materials may alter the measured heights of the peaks, or even eliminate certain peaks altogether. Furthermore, lower signal-to-noise ratios in the spectra, due to short exposure/scanning times, lead to further distortion of the spectra. In this paper we present a feature set which offers some degree of robustness in the presence of such distortions.