An improved classification scheme for standoff detection of explosives via Raman spectroscopy

Abstract

Raman spectroscopy is a laser-based vibrational technique that can provide spectral signatures unique to a multitude of compounds. The technique is gaining widespread interest as a method for detecting hidden explosives due to its sensitivity and ease of use. In this work, we present a computationally efficient classification scheme for accurate standoff identification of several common explosives using visible-range Raman spectroscopy. Using real measurements, we evaluate and modify a recent correlation-based approach to classify Raman spectra from various both harmful and commonplace substances. The results show that the proposed approach can, at a distance of 30 meters, or more, successfully classify measured Raman spectra from several explosive substances, including Nitromethane, TNT, DNT, Hydrogen Peroxide, TATP and Ammonium Nitrate.