Paper
16 May 2018 Using infrared backscatter imaging spectroscopy to detect trace explosives at standoff distances
Author Affiliations +
Abstract
We are developing a stand-off technique for the detection of trace amounts of explosive materials. The motivation behind this work is to prevent loss of life and injury to military and civilian personal by detecting threats at distance. The matured technique will allow for the facile identification of possible threats with minimum user effort and enough time to take appropriate action. This manuscript illustrates the results from our infrared backscatter imaging spectroscopy mobile stand-off method to detect trace amounts of explosive materials under laboratory conditions. The described technique uses tunable quantum cascade lasers, with full spectral coverage from 6-11 μm, to illuminate a target and an infrared focal plane array to collect the backscattered signal into hyperspectral images cubes. The quantum cascade lasers are operated under eye safe levels which allows for safe and stealthy probing of objects, vehicles, and even people. Experiments are performed on tilted substrates to simulate real world conditions where it is unlikely to collect the specular reflections. The collected hyperspectral image cubes contains spectral, spatial, and temporal information that can be fed to a detection algorithm.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Christopher J. Breshike, Christopher A. Kendziora, Robert Furstenberg, Viet Nguyen, Andrew Kusterbeck, and R. Andrew McGill "Using infrared backscatter imaging spectroscopy to detect trace explosives at standoff distances", Proc. SPIE 10629, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XIX, 106290N (16 May 2018); https://doi.org/10.1117/12.2304002
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Backscatter

FT-IR spectroscopy

Diffuse reflectance spectroscopy

Explosives

Infrared spectroscopy

Signal detection

Infrared imaging

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