Physical Sciences Inc. has developed a standoff deep ultraviolet (DUV) Raman sensor for the detection of explosive residues. The sensor is based on a solid-state DUV excitation source coupled with a Spatial Heterodyne Spectrometer receiver. The sensor measures Raman signals across a ~830–2680 cm-1 spectral range from a 2.6 cm2 interrogation area from a 1 m standoff in a single snapshot with a 17 cm-1 spectral resolution. Acquired spectra are processed through an on-board deep learning spectral correlation algorithm that provides real-time target identification. Developmental testing of the sensor has been conducted in a laboratory environment against explosive simulants including potassium chlorate, ammonium nitrate, and urea in bulk form as well as residues deposited on various substrates including plastic, glass, and metals. These measurements have demonstrated the system’s ability to measure Raman spectra and identify targets in 1 to 120 seconds.
A machine learning based approach has been developed to classify Raman spectroscopic data. The algorithm is based on a one dimensional neural network (1D-CNN) architecture which is trained with synthetic data that can incorporate sensor specific characteristics such as spectral range, spectral resolution and noise. The synthetic spectra are based on high SNR measurements which are then augmented by mixing target and background signatures. The CNN is trained to consider target representations in the presence of certain background materials including glass and HDPE. These additional target representations allow the CNN to make detections for materials taken through a container. Within this paper the performance of CNNs trained for Raman sensor systems has been evaluated using real data collected using the ThermoFisher FirstDefender. The evaluation data consists of various target chemicals (including explosives) and interferents (including household materials) collected through glass and plastic vials. The data was acquired with a controlled range of collection settings, including integration time and laser power, available on the unit. The performance of the 1D-CNN approach has demonstrated high classification accuracies, high probability of detection and low false alarm rates. Specifically, these metrics have been calculated as a function of signal to noise ratio. Additionally, a sensitivity analysis was conducted using an acetonitrile standard diluted in water which demonstrates the CNN’s capability of detecting all dilutions of acetonitrile down to weight concentrations of <1%. This sensitivity analysis was mirrored using a mixture of potassium chlorate and Vaseline. The CNN demonstrated detections down to 10% by weight of potassium chlorate.
A platform for building sensor specific machine learning detection algorithms has been developed to classify spectroscopic data. The algorithms are focused on long wave infrared reflectance (LWIR) and Raman spectroscopies. The classification algorithm is based on a one dimensional (1D) convolutional neural network (CNN) architecture. Training data is generated using an appropriate signal model that is combined with sensor specific characteristics such as spectral range, spectral resolution, and noise. Within this paper, the performance of trained CNNs for both LWIR and Raman sensor systems has been evaluated. The evaluation uses both real and synthetic data to benchmark the performance in terms of the discriminant signal. The evaluation data consists of various chemical representations and varied noise levels. The performance of the 1D CNN approach has demonstrated high classification accuracies on data with low discriminant signals. Specifically, the CNNs have demonstrated a classification accuracy <90% for infrared reflectance data down to a wavelength averaged discriminant SNR<1. For Raman systems, we have demonstrated classification accuracies <90% for data with a peak discriminant SNR of approximately 6.
Physical Sciences Inc. has developed an ultra-compact shortwave infrared (SWIR) staring mode hyperspectral imaging (HSI) sensor with an additional visible full motion video (FMV) capability. The innovative HSI design implements a programmable micro-electromechanical system entrance slit that breaks the interdependence between vehicle speed, frame rate, and spatial resolution of conventional push-broom systems and enables staring-mode operation without cooperative motion of the host vehicle or aircraft. The FMV and HSI components fit within 1000 cm3, weigh a total of 2.1 lbs., and draw 15 W of power. The sensor mechanical design is compatible with gimbal-based deployment allowing for easy integration into ground vehicles or aircrafts. The FMV is capable of achieving NIRS-6 imagery over a 6°×6° field-of-view (FOV) at a 1500 ft. standoff. The SWIR HSI covers a spectral range of 900-1605 nm with a 15 nm spectral resolution, and interrogates a 5°×5° FOV per 1.6 s with a 2.18 mrad instantaneous FOV (1 m ground sample distance at 1500 ft.). A series of outdoor tests at standoffs up to 300 ft. have been conducted that demonstrate the payload’s capability to acquire HSI information. The payload has direct utility towards diverse remote sensing applications such as vegetation monitoring, geological mapping, surveillance, etc. The data product utility is demonstrated through the spectral identification of materials (e.g. foam and cloth) placed in the sensor’s FOV.
There is an on-going need for sensor technologies capable of providing non-contact chemical detection and identification in the defense community. Here, we present the development of a standoff deep ultraviolet (DUV) Raman sensor for the detection of explosive residues. The sensor is based on a solid-state DUV excitation source coupled with a Spatial Heterodyne Spectrometer (SHS) receiver. The sensor is designed to detect Raman signals from a 4 cm2 area surface at a 1 m standoff. Detection and identification is achieved by correlating measured Raman signatures with high fidelity library spectra. The DUV excitation enables operation in a solar blind spectral region, leverages v4 cross section scaling and resonance enhancement of Raman signatures, and minimizes the impact of sample fluorescence. The SHS receiver provides a ~100× higher etendue than conventional slit-based spectrometers in a compact and rugged form factor, allowing for high performance field use. This work describes the system design and architecture of the Raman sensor prototype. Developmental standoff Raman measurements with the sensor using bulk liquid and solid samples are presented. Traceability to detection at the µg/cm2 scale is demonstrated and future improvements to increase system standoff are discussed.
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/m2 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.
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