This PDF file contains the front matter associated with SPIE Proceedings Volume 10183, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and Conference Committee listing.

This manuscript describes a mobile stand-off detection and identification of trace amounts of hazardous materials, specifically explosives. The technique utilizes an array of tunable infrared quantum cascade lasers as an illumination source which spans wavelengths from 6 to 11 μm, operated at eye-safe power levels. This spectral range enables excitation of a wide variety of absorption bands present in analytes of interest. The laser is modulated to produce a 50% duty cycle, square wave pulses, and control the frequency of irradiation. The backscatter and photo-thermal signals from samples are measured via an IR focal plane array, which allows for the observation of spatial, temporal, and thermal surface processes. A discussion of how these signals are collected and processed for use in identification of hazardous materials is presented.

Hyperspectral imaging (HSI) is a valuable tool for the detection and analysis of targets located within complex backgrounds. HSI can detect threat materials on environmental surfaces, where the concentration of the target of interest is often very low and is typically found within complex scenery. Unfortunately, current generation HSI systems have size, weight, and power limitations that prohibit their use for field-portable and/or real-time applications. Current generation systems commonly provide an inefficient area search rate, require close proximity to the target for screening, and/or are not capable of making real-time measurements.

ChemImage Sensor Systems (CISS) is developing a variety of real-time, wide-field hyperspectral imaging systems that utilize shortwave infrared (SWIR) absorption and Raman spectroscopy. SWIR HSI sensors provide wide-area imagery with at or near real time detection speeds. Raman HSI sensors are being developed to overcome two obstacles present in standard Raman detection systems: slow area search rate (due to small laser spot sizes) and lack of eye-safety. SWIR HSI sensors have been integrated into mobile, robot based platforms and handheld variants for the detection of explosives and chemical warfare agents (CWAs). In addition, the fusion of these two technologies into a single system has shown the feasibility of using both techniques concurrently to provide higher probability of detection and lower false alarm rates.

This paper will provide background on Raman and SWIR HSI, discuss the applications for these techniques, and provide an overview of novel CISS HSI sensors focusing on sensor design and detection results.

We report on a standoff chemical detection system using widely tunable external-cavity quantum cascade lasers (ECQCLs) to illuminate target surfaces in the mid infrared (λ = 7.4 – 10.5 μm). Hyperspectral images (hypercubes) are acquired by synchronously operating the EC-QCLs with a LN₂-cooled HgCdTe camera. The use of rapidly tunable lasers and a high-frame-rate camera enables the capture of hypercubes with 128 x 128 pixels and >100 wavelengths in <0.1 s. Furthermore, raster scanning of the laser illumination allowed imaging of a 100-cm² area at 5-m standoff. Raw hypercubes are post-processed to generate a hypercube that represents the surface reflectance relative to that of a diffuse reflectance standard. Results will be shown for liquids (e.g., silicone oil) and solid particles (e.g., caffeine, acetaminophen) on a variety of surfaces (e.g., aluminum, plastic, glass). Signature spectra are obtained for particulate loadings of RDX on glass of <1 μg/cm².

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.

The complex optical refractive index contains the optical constants, n(ῦ)and k(ῦ), which correspond to the dispersion and absorption of light within a medium, respectively. By obtaining the optical constants one can in principle model most optical phenomena in media and at interfaces including reflection, refraction and dispersion. We have developed improved protocols based on the use of multiple path lengths to determine the optical constants for dozens of liquids, including organic and organophosphorous compounds. Detailed description of the protocols to determine the infrared indices will be presented, along with preliminary results using the constants with their applications to optical modeling.

We present a data-driven machine learning approach to detect drug- and explosives-precursors using colorimetric sensor technology for air-sampling. The sensing technology has been developed in the context of the CRIM-TRACK project. At present a fully- integrated portable prototype for air sampling with disposable sensing chips and automated data acquisition has been developed. The prototype allows for fast, user-friendly sampling, which has made it possible to produce large datasets of colorimetric data for different target analytes in laboratory and simulated real-world application scenarios. To make use of the highly multi-variate data produced from the colorimetric chip a number of machine learning techniques are employed to provide reliable classification of target analytes from confounders found in the air streams. We demonstrate that a data-driven machine learning method using dimensionality reduction in combination with a probabilistic classifier makes it possible to produce informative features and a high detection rate of analytes. Furthermore, the probabilistic machine learning approach provides a means of automatically identifying unreliable measurements that could produce false predictions. The robustness of the colorimetric sensor has been evaluated in a series of experiments focusing on the amphetamine pre-cursor phenylacetone as well as the improvised explosives pre-cursor hydrogen peroxide. The analysis demonstrates that the system is able to detect analytes in clean air and mixed with substances that occur naturally in real-world sampling scenarios. The technology under development in CRIM-TRACK has the potential as an effective tool to control trafficking of illegal drugs, explosive detection, or in other law enforcement applications.

By using fixed narrow band pass optical filtering and scanning the laser excitation wavelength, hyperspectral Raman imaging could be achieved. Experimental, proof-of-principle results from the Chemical Warfare Agent (CWA) tabun (GA) as well as the common CWA simulant tributyl phosphate (TBP) on different surfaces/substrates are presented and discussed.

We present our latest experimental results on V-agent Raman scattering in the middle UV. The Raman scattering was examined using a pulsed tunable laser based spectrometer system. Neat droplets of the agents were placed on a silicon surface and irradiated with sequences of laser pulses. The Raman scattering was examined as a function of laser wavelength and accumulated exposure with a reduced level of exposure per pulse compared to our earlier investigations.

Photoacoustic/photothermal spectroscopy is an established technique for trace detection of chemicals and explosives. Normally high-sensitive microphone or PZT sensor is used to detect the signal in photoacoustic cell. In recent years, laser Doppler vibrometer (LDV) is proposed to remote-sense photoacoustic signal on various substrates. It is a highsensitivity sensor with a displacement resolution of <10pm. In this research, the photoacoustic effect of various chemicals and explosives is excited by a quantum cascade laser (QCL) at their absorbance peak. A home-developed differential LDV at 1550nm wavelength is applied to detect the vibration signal at 100m. A differential configuration is applied to minimize the environment factors, such as environment noise and vibration, air turbulence, etc. and increase the detection sensitivity. The photo-vibrational signal of chemicals and explosives on different substrates are detected. The results show the potential of the proposed technique on detection of trace chemicals and explosives at long standoff distance.

Deep-ultraviolet Raman spectroscopy is a very useful approach for standoff detection of explosive traces. Using two simultaneous excitation wavelengths improves the specificity and sensitivity to standoff explosive detection. The High Technology Foundation developed a highly compact prototype of resonance Raman explosives detector. In this work, we discuss the relative performance of a dual-excitation sensor compared to a single-excitation sensor. We present trade space analysis comparing three representative Raman systems with similar size, weight, and power. The analysis takes into account, cost, spectral resolution, detection/identification time and the overall system benefit.

The development of a wide area, bioaerosol early warning capability employing existing uncooled thermal imaging systems used for persistent perimeter surveillance is discussed. The capability exploits thermal imagers with other available data streams including meteorological data and employs a recursive Bayesian classifier to detect, track, and classify observed thermal objects with attributes consistent with a bioaerosol plume. Target detection is achieved based on similarity to a phenomenological model which predicts the scene-dependent thermal signature of bioaerosol plumes. Change detection in thermal sensor data is combined with local meteorological data to locate targets with the appropriate thermal characteristics. Target motion is tracked utilizing a Kalman filter and nearly constant velocity motion model for cloud state estimation. Track management is performed using a logic-based upkeep system, and data association is accomplished using a combinatorial optimization technique. Bioaerosol threat classification is determined using a recursive Bayesian classifier to quantify the threat probability of each tracked object. The classifier can accept additional inputs from visible imagers, acoustic sensors, and point biological sensors to improve classification confidence. This capability was successfully demonstrated for bioaerosol simulant releases during field testing at Dugway Proving Grounds. Standoff detection at a range of 700m was achieved for as little as 500g of anthrax simulant. Developmental test results will be reviewed for a range of simulant releases, and future development and transition plans for the bioaerosol early warning platform will be discussed.

Large area (> m²) position-sensitive readout of scintillators is important for passive/active gamma and neutron imaging for counter-terrorism applications. The goal of the LAMPA project is to provide a novel, affordable, large-area photodetector (8” x 8”) by replacing the conventional dynodes of photomultiplier tubes (PMTs) with electron multiplier microstructure boards (MSBs) that can be produced using industrial manufacturing techniques. The square, planar format of the LAMPA assemblies enables tiling of multiple units to support large area applications. The LAMPA performance objectives include comparable gain, noise, timing, and energy resolution relative to conventional PMTs, as well as spatial resolution in the few mm range. The current LAMPA prototype is a stack of 8” x 8” MSBs made commercially by chemical etching of a molybdenum substrate and coated with hydrogen-terminated boron-doped diamond for high secondary emission yield (SEY). The layers of MSBs are electrically isolated using ceramic standoffs. Field-shaping grids are located between adjacent boards to achieve good transmission of electrons from one board to the next. The spacing between layers and the design of the microstructure pattern and grids were guided by simulations performed using an electro-optics code. A position sensitive anode board at the back of the stack of MSBs provides 2-D readout. This presentation discusses the trade studies performed in the design of the MSBs, the measurements of SEY from various electro-emissive materials, the electro-optics simulations conducted, the design of the 2-D readout, and the mechanical aspects of the LAMPA design, in order to achieve a gain of > 10⁴ in an 8-stage stack of MSBs, suitable for use with various scintillators when coupled to an appropriate photocathode.

In the past, chemical incidents or attacks have often involved mixtures of chemicals or fractional formulations of toxic compounds. Terrorist groups are also likely to generate new toxic chemical agents. These situations involve unknown compounds and thus may be undetectable using traditional methods. Indeed, standoff gas detection with infrared devices traditionally relies on the comparison between measured signal and a library of signals included in a database. Observing the gas absorption in infrared band III (LWIR 8-14 μm), our multispectral infrared camera is used to detect gas clouds up to a range of several kilometers, to provide identification of gas type and to follow the motion of the cloud in real time. The approach described in this paper develops an algorithm that enables the device to detect gas even if the measured signature is not in the database – a pattern-matching-free algorithm. This detection process has been evaluated in the laboratory and subjected to significant experimental feedback. The results are a capability to detect unknown gases and gas mixtures.