Spectral Sensing Research for Surface and Air Monitoring in Chemical, Biological and Radiological Defense and Security Applications

The following sections are included:

• FOREWORD

• CONTENTS

Two primary goals of infrared spectroscopic detection are chemical identification and quantification. In order to accomplish these goals, a comprehensive and quantitative spectral library suitable for digital manipulation is required. To a large degree, the contents of such a library depend on the application. Since the primary application of the PNNL/DOE spectral library is for environmental monitoring, we have focused our efforts on hazardous pollutants, as well as a large variety of natural and anthropogenic chemicals. As a spin-off project and in collaboration with Dugway Proving Ground, we also had the opportunity to analyze a limited set of chemical warfare agents (CWAs). An example of such data appears below.

An approach for the passive standoff detection of surface contaminants by differential polarization FTIR spectrometry is proposed. The surface radiance modeling associated with the method is given. Unpolarized and polarized sensing measurements obtained with the CATSI sensor for the standoff detection of liquid agent VX deposited on high-reflectivity surfaces are presented. The analysis of results indicates that the differential polarization approach is well suited to mitigate sky radiance drifts, which favours unambiguous surface contaminant detections. An experimental and modeling study initiated to address the spectral polarization phenomenology is outlined. The design of an optimized FTIR sensor for differential polarization spectrometry measurements is discussed.

In order to assess the differences between background clutter using the CATSI instrument in direct (single-beam) and differential (double-beam) mode, a survey of background measurements was undertaken. Measurements include samples of sky, mountains, forest, buildings, roads and snow during springtime in the long wave infrared using both single-beam and double-beam interferometry. It is found that background distribution and statistics in these two modes are significantly different, with the differential mode presenting less variation than the direct mode. This may impact the ability to detect atmospheric contaminants. This analysis was performed in order to better understand the difference between operating a standard and a differential FTIR instrument.

We report advances in the signal processing of Multicomponent Raman Spectra of particulate matter. We evaluate laboratory and ambient samples collected in field experiments in Canada (during the Pacific 2001 Experiment, Vancouver, BC and at ALERT station, Nunavut, 2002). We discuss methodologies for signal processing the Raman spectra: de-noising and de-peaking, baseline reduction, and identification of chemical fingerprints. The ambient samples were collected near the surface in different environmental conditions during field experiments. In this article we compare and assess the methodologies performances and differences.

In the last few years, a number of researchers including our collaboration have assembled databases of terahertz (THz) time-domain spectroscopy (TDS) absorption spectra from bulk explosives. While this was a necessary and important step in demonstrating the feasibility of THz TDS for explosives detection, the goal of our research is to demonstrate selectivity of THz spectra from the clutter of background spectra coming from the substrate such as soil or sand. We have investigated THz TDS reflection spectra from sand with different grain sizes as well as from metallic powders in order to distinguish between the signals reflected from the rough surfaces compared to distributed reflections at finite depths in the granular material. With marker materials such as tartaric acid, which have absorption features in the 1-2 THz range, we have investigated the reflection spectra of granular substrates with marker chemicals, and compared this to reflection and transmission spectra of solid materials prepared in polyethylene sample pellets. In principle, the same experiments can then be performed using TNT, RDX, HMX and PETN, which all have characteristic features in the 0.5-8 THz frequency range. Absolute molecular absorption coefficients can be measured as well, and we include here preliminary values for RDX. A full analysis will be reported elsewhere.

The objective of this paper is to show that explosives may potentially be detected by passive standoff FTIR radiometry. It is demonstrated that many explosives exhibit a signature (fingerprint) in the longwave infrared (LWIR) region (i.e., 8 – 14 μm). Simulations using the radiative transfer model, MODTRAN4, clearly suggest that such materials can be identified when a thermal contrast exists between the material and its environment. The explosives considered in this study include octogen (HMX), trinitrotoluene (TNT), cyclonite (RDX), and the plastic explosives, C-4 and Detasheet-C. In addition, passive FTIR measurements of HMX have been performed in the field at standoff distances up to 60 m. The development of a passive standoff detection capability based on FTIR radiometry may be a potentially useful addition to the arsenal of measurement techniques that currently exist for the detection and identification of explosive threats.

Principal Components Analysis (PCA) is a common anomaly detection tool that was used in this work to detect organic and organophosphate analytes on soils using mid-infrared reflectionabsorption spectroscopy. Detection is hindered by large variability in sample-to-sample soil reflectivity that is due to the random nature of the soil particle packing. Extended multiplicative scatter correction (EMSC) and Savitzky-Golay derivative preprocessing were examined as methods to reduce this variability and enhance detection capability. Second derivative preprocessing provided results that were at least as good as EMSC for detection and the simplicity of the derivative methodology makes it an attractive preprocessing approach. Typically, PCA is applied to all spectral channels and results from detection events are interrogated to identify a potential cause. In this work, PCA models were developed for specific wavenumber ranges corresponding to functional group frequencies with the objective of providing some classification capability. It was found that detection of CH₂, CH₃ and P=O stretching bands was possible; however, results for a −CH₂ scissors band was less encouraging and detection of O−H stretch, −C−C− skeletal stretch, and PO−C stretch modes was poor. Some limited classification capability may be possible, but it would be difficult to make a unique assignment of the analytes present using the strategies studied.

This paper presents a novel wavelet and support vector machine (SVM) based method for hyperspectral image classification. A 1-D wavelet transform is applied to the pixel spectra, followed by feature extraction and SVM classification. Contrary to the traditional method of using pixel spectra with SVM classifier, our approach not only reduces the dimension of the input pixel feature vector but also improves the classification accuracy. Texture energy features computed in the spectral dimension are mapped using polynomial kernels and used for training the SVM classifier. Results with AVIRIS and other hyperspectral images for land cover and benthic habitat classification are presented. The accuracy of the method with limited training sets and computational burden is assessed.

In this paper we assess the effect that clustering pixels into spectrally-similar background types, for example, soil, vegetation, and water in hyperspectral visible/near-IR/SWIR imagery, prior to applying a detection methodology has on material detection statistics. Specifically, we examine the effects of data segmentation on two statistically-based detection metrics, the Subspace Generalized Likelihood Ratio Test (Subspace GLRT) and the Adaptive Cosine Estimator (ACE), applied to a publicly-available AVIRIS datacube augmented with a synthetic material spectrum in selected pixels. The use of synthetic spectrum-augmented data enables quantitative comparison of Subspace-GLRT and ACE using Receiver Operating Characteristic (ROC) curves. For all cases investigated, Receiver Operating Characteristic (ROC) curves generated using ACE were as good as or superior to those generated using Subspace-GLRT. The favorability of ACE over Subspace-GLRT was more pronounced as the synthetic spectrum mixing fraction decreased. For probabilities of detection in the range of 50-80%, segmentation reduced the probability of false alarm by a factor of 3–5 when using ACE. In contrast, segmentation had no apparent effect on detection statistics using Subspace-GLRT, in this example.

The Advanced Responsive Tactically-Effective Military Imaging Spectrometer (ARTEMIS) is under development for tactical military applications and is the primary payload for the TacSat-3 satellite. The optical design for the telescope, imaging spectrometer, and high resolution imager is described.

The U.S. Army is actively pursuing 3D active imaging techniques using laser sources emitting at 1.5 μm. This eyesafe short wave infrared (SWIR) waveband is advantageous due to both the improved eye safety and atmospheric propagation through obscurants. NVESD has several active programs in this area, which will be reviewed in this paper. These are: 1) single-pixel scanned imaging laser radar, 2) 2D gated SWIR imaging, and 3) 3D-flash laser radar. These systems are being evaluated for various targeting scenarios, including as potential payloads on unmanned air-vehicles, ground vehicles and other sensor suites. Applications include low-cost long-range target identification, identification of heavily obscured targets, obstacle avoidance, and high resolution imaging.

BAE SYSTEMS has developed a Low Cost Targeting System (LCTS) consisting of a FLIR for target detection, laser-illuminated, gated imaging for target identification, laser rangefinder and designator, GPS positioning, and auto-tracking capability within a small compact system size. The system is based upon BAE Systems proven micro-bolometer passive LWIR camera coupled with Intevac's new EBAPS camera. A dual wavelength diode pumped laser provides eyesafe ranging and target illumination, as well as designation; a custom detector module senses the return pulse for target ranging and to set the range gates for the gated camera. Trials show that the current detectors offer complete extinction of signals outside of the gated range, thus, providing high resolution within the gated region. The images have shown high spatial resolution arising from the use of solid state focal plane array technology. Imagery has been collected in both the laboratory and the field to verify system performance during a variety of operating conditions.

3-D flash ladar, herein defined as obtaining an entire frame of 3-D ladar data with one laser pulse, is an emerging technology with a number of advantages over conventional point scanner systems. Probably the most obvious advantage is the higher data rates possible and the potential for much higher data rates with increases in the associated 3-D focal planes array (FPA) format. High data rate means that topographical mapping, for example, can be obtained more rapidly decreasing the amount of flight time required. This paper investigates the clear but perhaps not-so-intuitive use of the high data rate: time dependent 3-D movies can be acquired at the repetition frequency of the associated laser. Data is taken using 3-D flash ladar cameras fabricated by Advanced Scientific Concepts, Inc. The paper concludes that there are a number of advantages and unique applications of the time dynamic 3-D flash ladar, including 3-D collision avoidance and object tracking.

Since the distribution of anthrax causing spores through the U.S. Postal System in the autumn of 2001, bioterrorism has become an ever present threat. During and following an attack it is also important to detect spores on surfaces, to assess the extent of an attack, to quantify risk of infection by contact, as well as to evaluate post-attack clean-up. To perform useful measurements, analyzers and/or methods must be capable of detecting as few as 10 spores/cm², in under 5-minutes, with little or no sample preparation or false-positive responses, using a portable device. In an effort to develop such a device, we have been investigating the ability of surface-enhanced Raman spectroscopy (SERS) to detect dipicolinic acid (DPA) as a chemical signature of bacilli spores. In general, SERS is capable of detecting mg/L concentrations and lower, and providing unequivocal identification of chemicals based on each one's unique spectrum. However, rapid analysis requires extracting the DPA from the spores for SERS detection. Here we describe the use of a room temperature digesting agent in combination with SERS to detect 220 spores on a surface, the entire procedure which was performed in 2.5 minutes.

Infrared spectroscopy has been demonstrated as a powerful tool for taxonomic classification of bacteria when the microbes are grown and sampled under carefully controlled conditions. Infrared spectroscopy affords limited information about relative proportions of certain chemical functional groups in whole microbial cells. The objective of this work is to elucidate the ability of infrared spectroscopy to identify and speciate Bacillus spp. regardless of sample history. Spectrometers utilize different scanning methods to collect infrared absorption spectra. We employed three; transmission through a thin film, transmission infrared microscopy, and Attenuated Total Reflection (ATR). Target organisms include Bacillus anthracis, and several near neighbors. Each strain was cultured at 24°C and 35°C on three solid media. Microorganisms were incubated for up to ten days to include vegetative cells, spore formation and mature spores. Triplicate microbe samples were prepared and analyzed according to instrument requirements using the three measurement modes. Triplicate samples of BSL-3 organisms were analyzed only by the thin film transmission method. Spectral data was analyzed using the cluster analysis function of OPUS software. We report that infrared spectrometry is capable of discerning Bacillus spores from vegetative cells and the phylogenic clustering of Bacillus species according to pathogenicity levels via infrared spectral analysis.

The stand-off detection classification by laser induced fluorescence is the objective of the Biosense project. The sensor will perform the monitoring of a defined area around its location using an elastic lidar detector for particles cloud. The detection of cloud will trigger fluorescence probing of the cloud. To perform this task the area fluorescence background will be monitored in order to evaluate if a return signal changed. Using a simple signal model built with experimental data, we designed a detection and monitoring procedure for the fluorescence at a single location. Signal simulations have been performed to verify the operation of the system. The results of the simulation indicate the system is able to detect anomaly with small contrast between a signal and the background. The results will have to be extended to area surveillance and a more complete signal model for various environments in natural conditions is required.

We investigate the use of multiple scattering via Multiple-Field-Of-View (MFOV) lidar signals to characterize bioaerosol particles size and concentration from ground based lidar over distances shorter than a few kilometers. The MFOV lidar signal is calculated for background aerosols at a wavelength 355 nm for a visibility of 30 km. The optical depths studied are small and the calculations are restricted to second order scattering. Also since background aerosols are constituted of relatively small particles which diffuse the light at large angles, the fields of view (FOV) range from 1 to 100 mrad full angle. We show that the MFOV lidar measurements contain exploitable information on particle size and extinction.

In this paper, the passive standoff long wave infrared technology developed for atmospheric remote sensing was used to detect and identify chemical pollutants in the atmosphere. The measurement approach is based on the differential passive standoff detection method that has been developed by DRDC Valcartier during the past few years. The measurements were performed on real chemical warfare agents and toxic chemical vapors. The results clearly demonstrate the capability of the differential radiometry approach for the detection, identification and quantification of toxic chemical vapor clouds in an open-air environment.

Pixel Purity Index (PPI) has been widely used for endmember extraction. Recently, an approach using blocks of skewers was proposed by Theiler et al., called blocks of skewers (BOS) method, to improve computation of the PPI. It utilizes a block of skewers to reduce number of calculations of dot products operated by the PPI on each skewers with all data sample vectors. Unfortunately, the BOS method also suffers from the same drawbacks that the PPI does in terms of several parameters which are needed to be determined a priori. Besides, it also has an additional parameter, block size, B needed to be determined where no guideline is provided of how to select this parameter. In this paper, the BOS method is also investigated. Most importantly, a new pyramid-based block design for the BOS method is also introduced as opposed to the cube-based block designed used by Theiler et al.'s BOS. One major advantage of our proposed pyramid-based BOS over Theiler et al.'s cubedesign BOS is the hardware design for Field Programmable Gate Arrays (FPGAs) implementation.

A compact high peak power eye-safer optical parametric oscillator was constructed by pumping it with a master oscillator power amplifier consisting of a large-mode-area ytterbium doped fiber amplifier and a diode-pumped, passively Q-switched Nd:YAG microchip laser. The master oscillator power amplifier has the maximum output pulse energy of 570 μJ with a 3 nanosecond pulse width and a 3 kHz pulse repetition rate. The compact singly resonating optical parametric oscillator utilized a 50 mm periodically poled Lithium Niobate crystal and generated high peak power 1.5 μm eye-safe laser pulses with more than 140 μJ pulse energy, 3 nanosecond pulse width and 3 kHz repetition rate.

Threats associated with bioaerosol weapons have been around for several decades. However, with the recent political developments that changed the image and dynamics of the international order and security, the visibility and importance of these bioaerosol threats have considerably increased. Over the last few years, Defence Research and Development Canada has investigated the spectrometric LIDAR-based standoff bioaerosol detection technique to address this menace. This technique has the advantages of rapidly monitoring the atmosphere over wide areas without physical intrusions and reporting an approaching threat before it reaches sensitive sites. However, it has the disadvantages of providing a quality of information that degrades as a function of range and bioaerosol concentration. In order to determine the importance of these disadvantages, Canada initiated in 1999 the SINBAHD (Standoff Integrated Bioaerosol Active Hyperspectral Detection) project investigating the standoff detection and characterization of threatening biological clouds by Laser-Induced Fluorescence (LIF) and intensified range-gated spectrometric detection techniques. This article reports an overview of the different lessons learned with this program. Finally, the BioSense project, a Technology Demonstration Program aiming at the next generation of wide area standoff bioaerosol sensing, mapping, tracking and classifying systems, is introduced.

A Computed Tomographic Imagining Spectrometer (CTIS) is an imaging spectrometer system that acquires all the information required to reconstruct the data cube in a single integration time. This is compared to conventional systems such as whiskbroom systems, pushbroom systems, and filter wheel systems that requiring scanning in one or more coordinate direction.

CTIS systems have been designed and tested in several different singular spectral bands as well as a dual band system. In addition to hyperspectral imaging spectrometers, CTIS systems have been used as an imaging spectropolarimeter and as a ranging imaging spectrometer. An imaging spectropolarimeter not only reconstructs the spectral content at every point in the scene of interest, but also provides the Stokes parameters at every point. So instead of just one data cube, we get four data cubes, one for each element of the Stokes vector. The ranging CTIS incorporates a LADAR system with the CTIS to provide the range information to targets in scene as well as the reconstructed data cube.

The physical principles behind the CTIS system are presented as well as some of representative data from single band systems, the dual band proof of concept, the spectropolarimeter, and the ranging imaging spectrometer.

A visible, hyperspectral imager using chromotomography CT) has been built, with the goal of extending the technology to spatially extended sources with quickly varying (> 10 Hz) features, such as bomb detonations and muzzle flashes. Even with a low dispersion, ∼0.7 mrad/nm, direct vision prism with undeviated wavelength near λ = 548 nm, spectral resolution of better than Δ λ< 10 nm across the λ = 400 − 600 nm band is demonstrated with spatial resolution of better than 0.5 mm. The primary objective of this paper is to show empirically that the spatial and spectral resolution of data obtained by a simple CT instrument is unchanged in projection space and reconstructed object space.

Region-of-interest cueing by hyperspectral imaging systems for tactical reconnaissance has emphasized wide area coverage, low false alarm rates, and the search for manmade objects. Because they often appear embedded in complex environments and can exhibit large intrinsic spectral variability, these targets usually cannot be characterized by consistent signatures that might facilitate the detection process. Template matching techniques that focus on distinctive and persistent absorption features, such as those characterizing gases or liquids, prove ineffectual for most hardbody targets. High-performance autonomous detection requires instead the integration of limited and uncertain signature knowledge with a statistical approach. Effective techniques devised in this way using Gaussian models have transitioned to fielded systems. These first-generation algorithms are described here, along with heuristic modifications that have proven beneficial. Higher-performance Gaussian-based algorithms are also described, but sensitivity to parameter selection can prove problematical. Finally, a next-generation parameter-free non-Gaussian method is outlined whose performance compares favorably with the best Gaussian methods.

The Defence Research and Development Canada Agency has successfully completed a Technology Demonstration Program to assess the military utility of airborne hyperspectral Imagery. This required developing a sensor, the Airborne Infrared Imaging Spectrometer (AIRIS), and collecting in-flight imagery data. AIRIS was designed as a flexible instrument using a Fourier Transform spectrometer with a spectral resolution ranging from 1 to 16 cm⁻¹, wide spectral coverage (2 to 12 microns), and different optical configurations. This paper provides a description of AIRIS and discusses examples of the spectral images collected during one air-trial. Emphasis is put on images of sub-pixel targets. Processing AIRIS data is labor intensive and can only be performed during post-trial analysis. Hardware and software modifications to AIRIS will implement a real-time processing capability over the next three years. These modifications will enable the instrument to output radiometrically calibrated digital spectrograms. These spectrograms will then be processed in real-time to output target detection and identification for selected target types.

ATK Mission Research will describe a very high speed FT-IR spectrometer that collects 1000 interferograms per second at a 4 cm⁻¹ resolution in the 2-5 micrometer bandpass. The field of view of the instrument is about 1 degree. Collection of photons is on a single cryo-cooled InSb detector with output data streamed to a RAID device for later processing. The system uses a rotating lightweight air bearing mirror to achieve rapid variation of optical path length. Tilt compensation optics include a corner cube in a variation of the Michelson arrangement. The instrument is a one-man portable, tripod-mounted unit with off-unit data collection electronics. The system is designed for field deployment where measurements of energetic events are desired. Applications are commercial and military.

Multi-color narrow-band Salisbury Screen and Jaumann Absorbers combined with optimized thick Si₃N₄ support layers are designed for wavelength selectivity in 7 ~ 14μm wavelength band. The Jaumann Absorbers are adopted as a vertically ‘stacked’ pixel structure to save space and enhance resolution compared against ‘tiled’ structure (pixels lying in a common plane).

A data fusion-based, multisensory detection system, called “Volume Sensor”, was developed under the Advanced Damage Countermeasures (ADC) portion of the US Navy's Future Naval Capabilities program (FNC) to meet reduced manning goals. A diverse group of sensing modalities was chosen to provide an automated damage control monitoring capability that could be constructed at a relatively low cost and also easily integrated into existing ship infrastructure. Volume Sensor employs an efficient, scalable, and adaptable design framework that can serve as a template for heterogeneous sensor network integration for situational awareness. In the development of Volume Sensor, a number of challenges were addressed and met with solutions that are applicable to heterogeneous sensor networks of any type. These solutions include: 1) a uniform, but general format for encapsulating sensor data, 2) a communications protocol for the transfer of sensor data and command and control of networked sensor systems, 3) the development of event specific data fusion algorithms, and 4) the design and implementation of modular and scalable system architecture. In fullscale testing on a shipboard environment, two prototype Volume Sensor systems demonstrated the capability to provide highly accurate and timely situational awareness regarding damage control events while simultaneously imparting a negligible footprint on the ship's 100 Mbps Ethernet network and maintaining smooth and reliable operation in a real-time fashion.

The Inexpensive Chemical Agent Detection System (ICADS) consists of a network of affordable line-of-sight sensors, each designed to detect chemical threats passing between two points with high sensitivity and a low false-alarm rate. Each leg of the ICADS system is composed of two devices, a broadband IR transmitter, and a receiver containing a long-wave-IR spectrometer. The spectrometer continually measures the spectrum of the radiation emitted by the transmitter, which is separated from the receiver by up to several hundred meters, forming a line of protection. A chemical vapor or aerosol plume with sufficient long-wave-IR absorption causes a characteristic change in the spectrum of light collected by the receiver as the plume crosses the protected line, signaling a threat. Background measurements were conducted to determine background-limited performance. Additionally, a sensor composed of a long-wave-IR fixed-grating spectrometer and a hot-filament transmitter was designed and built. Measurements of the signal-to-noise ratio (SNR) and resolution agree with our analytical model and meet sensor requirements.

To address the problem of sources and sinks of atmospheric CO₂, measurements are needed on a global scale. Satellite instruments show promise, but typically measure the total column. Since sources and sinks at the surface represent a small perturbation to the total column, a precision of better than 1% is required. No species has ever been measured from space at this level. Over the last three years, we have developed a small instrument based upon a Fabry-Perot interferometer that is highly sensitive to atmospheric CO₂. We have tested this instrument in a ground based configuration and from aircraft platforms simulating operation from a satellite. The instrument is characterized by high signal to noise ratio, fast response and great specificity. We have performed simulations and instrument designs for systems to detect, H₂O, CO, ₁₃CO₂, CH₄, CH₂O, NH₃, SO₂, N₂O, NO₂, and O₃. The high resolution and throughput, and small size of this instrument make it adaptable to many other atmospheric species. We present results and discuss ways this instrument can be used for ground, aircraft or space based surveillance and the detection of pollutants, toxics and industrial effluents in a variety of scenarios including battlefields, industrial monitoring, or pollution transport.

Background scene characterization in the long wave infrared (LWIR) region of the electromagnetic spectrum is of particular interest for simulating the operational environment of passive standoff chemical detection systems. In conjunction with collections of local metrological data and temperature and water vapor profiles, spectrally resolved LWIR imagery, acquired using a scanned Fourier Transform Spectrometer, and boresighted visible imagery were collected during Fall 2005. Resulting spectra in the 700 – 1450 cm⁻¹ frequency range were analyzed to provide estimates of the foreground/background temperature differential that can then be used as a metric to assess the information content in the resulting IR imagery. Measurements were performed at two geographical sites and over 100 GBs of raw data were accumulated. Through spatio-temporal analysis of the resulting temperature differential maps, we have derived strategies for optimizing scan patterns. These optimized strategies attempt to minimize the amount of redundant data thus providing shorter inter-sample times for temporally varying portions of the scene. Considerations include the first and second order spatial and temporal statistics as well as information content as quantified by average pixel entropy. This research was financed by the U.S. Army Joint Project Manager for Nuclear, Biological, and Chemical Contamination Avoidance under contract number N00024-03-D-6606.

This paper presents comparative analysis of different wavelength ranges for the spectroscopic detection of acetone vapor. We collected and analyzed original absorption line spectra arising from electronic transitions in the ultraviolet, near-infrared vibrational overtones, mid-infrared fundamentals, THz torsional modes, and mm-wave rotational transitions. Peak absorption cross sections of prominent spectral features are determined. The relative merit of each spectral range for sensing is considered, taking into account the absorption strength, available technology, and possible interferences.

Standoff LIDAR detection of BW agents depends on accurate knowledge of the infrared and ultraviolet optical elastic scatter (ES) and ultraviolet fluorescence (UVF) signatures of bio-agents and interferents. MIT Lincoln Laboratory has developed the Standoff Aerosol Active Signature Testbed (SAAST) for measuring polarization-dependent ES cross sections from aerosol samples at all angles including 180° (direct backscatter) [1]. Measurements of interest include the dependence of the ES and UVF signatures on several spore production parameters including growth medium, sporulation protocol, washing protocol, fluidizing additives, and degree of aggregation. Using SAAST, we have made measurements of the polarization-dependent ES signature of Bacillus globigii (atropheaus, Bg) spores grown under different growth methods. We have also investigated one common interferent (Arizona Test Dust). Future samples will include pollen and diesel exhaust. This paper presents the details of the apparatus along with the results of recent measurements.

Heavy loads of aerosols in the air have considerable health effects in individuals who suffer from chronic breathing difficulties. This problem is more acute in the Middle-East, where dust storms in winter and spring transverse from the neighboring deserts into dense populated areas. Discrimination between the dust types and association with their source can assist in assessment of the expected health effects. A method is introduced to characterize the properties of dense dust clouds with passive IR spectral measurements. First, we introduce a model based on the solution of the appropriate radiative transfer equations. Model predictions are presented and discussed. Actual field measurements of silicone-oil aerosol clouds with an IR spectro-radiometer are analyzed and compared with the theoretical model predictions. Silicone-oil aerosol clouds have been used instead of dust in our research, since they are composed of one compound in the form of spherical droplets and their release is easily controlled and repetitive. Both the theoretical model and the experimental results clearly show that discrimination between different dust types using IR spectral measurements is feasible. The dependence of this technique on measurement conditions, its limitations, and the future work needed for its practical application of this technique is discussed.

Remote sensing of chemical warfare agents (CWA) with stand-off hyperspectral sensors has a wide range of civilian and military applications. These sensors exploit the spectral changes in the ambient photon flux produced thermal emission or absorption after passage through a region containing the CWA cloud. In this work we focus on (a) staring single-pixel sensors that sample their field of view at regular intervals of time to produce a time series of spectra and (b) scanning single or multiple pixel sensors that sample their FOV as they scan. The main objective of signal processing algorithms is to determine if and when a CWA enters the FOV of the sensor.

We shall first develop and evaluate algorithms for staring sensors following two different approaches. First, we will assume that no threat information is available and we design an adaptive anomaly detection algorithm to detect a statistically-significant change in the observed spectrum. The algorithm processes the observed spectra sequentially-in-time, estimates adaptively the background, and checks whether the next spectrum differs significantly from the background based on the Mahalanobis distance or the distance from the background subspace. In the second approach, we will assume that we know the spectral signature of the CWA and develop sequential-in-time adaptive matched filter detectors. In both cases, we assume that the sensor starts its operation before the release of the CWA; otherwise, staring at a nearby CWA-free area is required for background estimation. Experimental evaluation and comparison of the proposed algorithms is accomplished using data from a long-wave infrared (LWIR) Fourier transform spectrometer.

The construction of very good hyperspectral sensors operating in the thermal infrared bands from 8 to 12 microns arouses much interest for the development of data exploitation tools. Temperature emissivity separation (TES) algorithms are very important components of a future toolbox, because they make it possible to extract these two fundamental targets' parameters. The emissivity relies on the nature of the target's surface materials, while the temperature gives information related to their use and relationship with the environment. The TES technique presented in this paper is based on iteration on temperature principle, where a total square error criterion is used to estimate the temperature. The complete procedure is described in the paper. Its sensitivity to noise is studied and a mathematical behavior model is provided. The model is validated through a Monte-Carlo simulation of the technique's operation.

To investigate the detection limits of biological aerosols using passive infrared measurements, we have developed a computational model that relies on physics-based simulations to generate a statistical sample. The simulation consists of three principal models: an atmospheric turbulence model, a radiative transfer model and a target detection model. The turbulence model is used to generate microscale atmospheric variability. Resulting temperature and density profiles, along with custom aerosol profiles, are used to generate inputs for MODTRAN5, which produces simulated atmospheric spectral radiance. The simulated data is then analyzed by using an optimal detection algorithm and a hypothesis test, resulting in receiver operating characteristic (ROC) curves.

The concept design and preliminary applications of a new Eye Safe Polarization Diversity Lidar (ESPDL) instrument are described. The lidar operates with polarization diversity in the laser emission and polarization discrimination in the receiver at 1574 nm for tropospheric aerosol studies in the Arctic atmosphere. This instrument was originally designed to operate in the eye-safe wavelength range with a one-channel receiver and 20 dB linear polarization accuracy in the laser emission, and assembled in a compact optical bench. The instrument was upgraded for polarization diversity laser emission, i.e polarization selectivity better than 30 dB, and for linear polarization reception discrimination, i.e. polarization discrimination better than 50 dB. Geophysical lidar applications under the scope of this instrument with an overall instrumental polarimetric accuracy better than 0.1% focus on the identification of very dilute suspended aerosols in the troposphere, ice in-cloud initiation and aerosol/water interfaces, complex aerosols, sub-visible high altitude clouds and environmental issues and dynamic processes in the Arctic such as ice fog, forest fire, and a multilayered stably stratified Arctic boundary layer. In this article we describe the instrument design concept and the electronic synchronization necessary to achieve the maximum instrumental polarization accuracy. We report a preliminary case study of differential polarization analysis.

Identification of aerosol type and chemical composition may help to trace their origin and estimate their impact on land and people. Aerosols chemical composition, size distribution and particles shape, manifest themselves in their spectral scattering cross-section. In order to make a reliable identification, comprehensive spectral analysis of aerosol scattering should be carried out. Usually, spectral LIDAR measurements of aerosols are most efficiently performed using an Nd:YAG laser transmitter in the fundamental frequency and its 2^nd, 3^rd and 4^th harmonics. In this paper we describe automatic detection and identification of several aerosol types and size distributions, using a multispectral lidar system operating in the IR, NIR and UV spectral regions. The LIDAR transmitter is based on a single Nd:YAG laser. In addition to the 3^rd and 4^th harmonics in the UV, two optical parametric oscillator units produce the eye-safe 1.5 μm wavelength in the near IR and up to 40 separable spectral lines in the 8-11 μm IR. The combination of a wide spectral coverage required for backscattering analysis combined with fluorescence data, enable the generation of a large spectral data set for aerosols identification. Several natural and anthropogenic aerosol types were disseminated in controlled conditions, to test system capabilities. Reliable identification of transient and continuous phenomena demands fast and efficient control and detection algorithms. System performance, using the specially designed algorithms, is described below.

The single crystal growth of KPb₂Br₅ by vertical Bridgman technique using in-house processed zone refined PbBr₂ and KBr with rare-earth terbium doping has been studied. The grown moisture resistant crystals (1.5 cm diameter and 10 cm length) have shown high promise for low phonon energy room temperature solid-state laser applications in the longer side of mid-IR (4-15 μm) due to their high storage lifetimes, wide tunability, and excellent optical quality. The processed crystals are highly transparent (T= ≥80%) in the 0.4-25 μm spectral region. Repeated melting-freezing cycles during differential scanning calorimetry (DSC) experiments did not reveal any appreciable variation in the melting point or phase transitions, which is indicative of their excellent thermal stability. The emission spectra pumped with a 2 μm source show broadband emissions with peak wavelength of 3μm (⁷F₄→ ⁷F₆), 5μm (⁷F₅→ ⁷F₆) and 7.9μm (⁷F₄→ ⁷F₅). The KPb²Br₅:Tb laser crystals will be highly useful for standoff detection of incoming chemical and biological threats using unique infrared absorption signatures.

We have developed a hyperspectral deconvolution algorithm that sharpens the spectral dimension in addition to the more usual across-track and along-track dimensions. Using an individual threedimensional model for each pixel's point spread function, the algorithm iteratively applies maximum likelihood criteria to reveal previously hidden features in the spatial and spectral dimensions. Of necessity, our solution is adaptive to unreported across-track and along-track vibrations with amplitudes smaller than the ground sampling distance. We sense and correct these vibrations using a combination of maximum likelihood deconvolution and gradient descent registration that maximizes statistical correlations over many bands. Test panels in real hyperspectral imagery show significant improvement when locations are corrected. Tests on simulated imagery show that the precision of relative corrected positions improves by about a factor of two.