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An active, standoff, all-phase chemical detection capability has been developed under IARPA’s SILMARILS program. The detection platform utilizes reflectance spectroscopy in the longwave infrared coupled with an automated detection algorithm that implements physics-based reflectance models for planar chemical films, particulate in the solid and liquid phase, and vapors. Target chemicals include chemical warfare agents, toxic industrial chemicals, and explosives. The platform employs broadband Fabry-Perot quantum cascade lasers with a spectrally selective detector to interrogate target surfaces at tens of meter standoff. A statistical F-test in a noise whitened space is used for detection and discrimination over a large target spectral library in high clutter environments. The capability is described with an emphasis on the physical reflectance models used to predict spectral reflectivity signatures as a function of surface contaminant presentation and loading. Developmental test results from a breadboard version of the detector platform are presented. Specifically, solid and liquid surface contaminants were detected and identified from a library of 325 compounds down to 30 μg/cm2 surface loading at a 5 m standoff. Vapor detection was demonstrated via topographic backscatter.

Advances towards the development of a longwave infrared quantum cascade laser (QCL) based standoff and proximal surface contaminant detection platform are presented with emphasis on developmental test results. The detection platform utilizes reflectance spectroscopy with application to optically thick and thin materials in film and particulate forms including solid and liquid phase chemical warfare agents, toxic industrial chemicals and materials, and explosives. The platform employs an ensemble of broadband Fabry-Perot QCLs with a spectrally selective detector to interrogate target surfaces at 1 to 10s of m standoff. A version of a Subspace Adaptive Cosine Estimator is used for detection and discrimination in high clutter environments. Through speckle reduction, a noise equivalent reflectivity of 0.1% was demonstrated enabling detection limits approaching 0.1 μg/cm2 for optically thin films and 2% fill factor for optically thick particulates. The design, build, and validation of a breadboard version of the QCL-based surface contaminant detector are summarized. Results from developmental testing of contaminated substrates in standoff (5 m range) and proximal (~1 m range) configurations are presented. The test substrates were prepared by the government and Physical Sciences, Inc. and include solid and liquid contaminants at varying surface loadings. Future improvements including an expanded spectral range are discussed.

Progress towards the development of a longwave infrared quantum cascade laser (QLC) based standoff surface contaminant detection platform is presented. The detection platform utilizes reflectance spectroscopy with application to optically thick and thin materials including solid and liquid phase chemical warfare agents, toxic industrial chemicals and materials, and explosives. The platform employs an ensemble of broadband QCLs with a spectrally selective detector to interrogate target surfaces at 10s of m standoff. A version of the Adaptive Cosine Estimator (ACE) featuring class based screening is used for detection and discrimination in high clutter environments. Detection limits approaching 0.1 μg/cm2 are projected through speckle reduction methods enabling detector noise limited performance. The design, build, and validation of a breadboard version of the QCL-based surface contaminant detector are discussed. Functional test results specific to the QCL illuminator are presented with specific emphasis on speckle reduction.

Sensor technologies capable of detecting low vapor pressure liquid surface contaminants, as well as solids, in a noncontact fashion while on-the-move continues to be an important need for the U.S. Army. In this paper, we discuss the development of a long-wave infrared (LWIR, 8-10.5 μm) spatial heterodyne spectrometer coupled with an LWIR illuminator and an automated detection algorithm for detection of surface contaminants from a moving vehicle. The system is designed to detect surface contaminants by repetitively collecting LWIR reflectance spectra of the ground. Detection and identification of surface contaminants is based on spectral correlation of the measured LWIR ground reflectance spectra with high fidelity library spectra and the system’s cumulative binary detection response from the sampled ground. We present the concepts of the detection algorithm through a discussion of the system signal model. In addition, we present reflectance spectra of surfaces contaminated with a liquid CWA simulant, triethyl phosphate (TEP), and a solid simulant, acetaminophen acquired while the sensor was stationary and on-the-move. Surfaces included CARC painted steel, asphalt, concrete, and sand. The data collected was analyzed to determine the probability of detecting 800 μm diameter contaminant particles at a 0.5 g/m 2 areal density with the SHSCAD traversing a surface.

Liquid-contaminated surfaces generally require more sophisticated radiometric modeling to numerically describe surface properties. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) Model utilizes radiative transfer modeling to generate synthetic imagery. Within DIRSIG, a micro-scale surface property model (microDIRSIG) was used to calculate numerical bidirectional reflectance distribution functions (BRDF) of geometric surfaces with applied concentrations of liquid contamination. Simple cases where the liquid contamination was well described by optical constants on optically at surfaces were first analytically evaluated by ray tracing and modeled within microDIRSIG. More complex combinations of surface geometry and contaminant application were then incorporated into the micro-scale model. The computed microDIRSIG BRDF outputs were used to describe surface material properties in the encompassing DIRSIG simulation. These DIRSIG generated outputs were validated with empirical measurements obtained from a Design and Prototypes (D&P) Model 102 FTIR spectrometer. Infrared spectra from the synthetic imagery and the empirical measurements were iteratively compared to identify quantitative spectral similarity between the measured data and modeled outputs. Several spectral angles between the predicted and measured emissivities differed by less than 1 degree. Synthetic radiance spectra produced from the microDIRSIG/DIRSIG combination had a RMS error of 0.21-0.81 watts/(m2−sr−μm) when compared to the D&P measurements. Results from this comparison will facilitate improved methods for identifying spectral features and detecting liquid contamination on a variety of natural surfaces.