Law enforcement officers and public safety personnel are a critical component of the Global Nuclear Detection Architecture, and would benefit from additional opportunities to train for this mission in realistic threat scenarios. Physical Sciences Inc. (PSI) is developing a Virtual Source Training Toolkit (VSTT) system capable of reproducing the response of handheld radiation detectors to a virtual source in a complex occlusion and shielding environment. The toolkit will allow additional low-cost training opportunities for these officers inside operationally relevant public areas in order to reduce the time required to detect and localize a realistic radiological threat. The main components of the VSTT are a user position estimation system and a radiation propagation algorithm. Both algorithms operate at 10 Hz update rate on a handheld Android smart device that simulates the user interface of a radiation detector. The user position and orientation are determined through a Bayesian fusion process between the smart phone IMU measurements and range estimates to Bluetooth beacons. The radiation propagation algorithm simulates both attenuation and scattering of radiation between the programmed virtual source position and the user’s estimated position. The VSTT has been demonstrated to provide an average localization error < 1.2 m while traversing a complex interior space including walls and magnetic perturbations. The simulated radiation spectra achieve Spectral Angle Mapping values < 0.93 between simulated and measured source configurations through multiple shielding materials and thicknesses. In a series of experiments, an operator is able to rapidly localize a virtual source using a prototype VSTT.
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 development of two longwave infrared quantum cascade laser (QCL) based surface contaminant detection platforms supporting government programs will be discussed. The detection platforms utilize 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. Operation at standoff (10s of m) and proximal (1 m) ranges will be reviewed with consideration given to the spectral signatures contained in the specular and diffusely reflected components of the signal. The platforms comprise two variants: Variant 1 employs a spectrally tunable QCL source with a broadband imaging detector, and Variant 2 employs an ensemble of broadband QCLs with a spectrally selective detector. Each variant employs a version of the Adaptive Cosine Estimator for detection and discrimination in high clutter environments. Detection limits of 5 μg/cm2 have been achieved through speckle reduction methods enabling detector noise limited performance. Design considerations for QCL-based standoff and proximal surface contaminant detectors are discussed with specific emphasis on speckle-mitigated and detector noise limited performance sufficient for accurate detection and discrimination regardless of the surface coverage morphology or underlying surface reflectivity. Prototype sensors and developmental test results will be reviewed for a range of application scenarios. Future development and transition plans for the QCL-based surface detector platforms are discussed.
Physical Sciences Inc. (PSI) is developing a longwave infrared (LWIR) compressive sensing hyperspectral imager (CS HSI) based on a single pixel architecture for standoff vapor phase plume detection. The sensor employs novel use of a high throughput stationary interferometer and a digital micromirror device (DMD) converted for LWIR operation in place of the traditional cooled LWIR focal plane array. The CS HSI represents a substantial cost reduction over the state of the art in LWIR HSI instruments. Radiometric improvements for using the DMD in the LWIR spectral range have been identified and implemented. In addition, CS measurement and sparsity bases specifically tailored to the CS HSI instrument and chemical plume imaging have been developed and validated using LWIR hyperspectral image streams of chemical plumes. These bases enable comparable statistics to detection based on uncompressed data. In this paper, we present a system model predicting the overall performance of the CS HSI system. Results from a breadboard build and test validating the system model are reported. In addition, the measurement and sparsity basis work demonstrating the plume detection on compressed hyperspectral images is presented.
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.
Technology development efforts seek to increase the capability of detection systems in low Signal-to-Noise regimes encountered in both portal and urban detection applications. We have recently demonstrated significant performance enhancement in existing Advanced Spectroscopic Portals (ASP), Standoff Radiation Detection Systems (SORDS) and handheld isotope identifiers through the use of new advanced detection and identification algorithms. The Poisson Clutter Split (PCS) algorithm is a novel approach for radiological background estimation that improves the detection and discrimination capability of medium resolution detectors. The algorithm processes energy spectra and performs clutter suppression, yielding de-noised gamma-ray spectra that enable significant enhancements in detection and identification of low activity threats with spectral target recognition algorithms. The performance is achievable at the short integration times (0.5 – 1 second) necessary for operation in a high throughput and dynamic environment. PCS has been integrated with ASP, SORDS and RIID units and evaluated in field trials. We present a quantitative analysis of algorithm performance against data collected by a range of systems in several cluttered environments (urban and containerized) with embedded check sources. We show that the algorithm achieves a high probability of detection/identification with low false alarm rates under low SNR regimes. For example, utilizing only 4 out of 12 NaI detectors currently available within an ASP unit, PCS processing demonstrated Pd,ID > 90% at a CFAR (Constant False Alarm Rate) of 1 in 1000 occupancies against weak activity (7 - 8μCi) and shielded sources traveling through the portal at 30 mph. This vehicle speed is a factor of 6 higher than was previously possible and results in significant increase in system throughput and overall performance.
The need for standoff detection technology to provide early Chem-Bio (CB) threat warning is well documented. Much
of the information obtained by a single passive sensor is limited to bearing and angular extent of the threat cloud. In
order to obtain absolute geo-location, range to threat, 3-D extent and detailed composition of the chemical threat, fusion
of information from multiple passive sensors is needed. A capability that provides on-the-move chemical cloud
characterization is key to the development of real-time Battlespace Awareness.
We have developed, implemented and tested algorithms and hardware to perform the fusion of information obtained
from two mobile LWIR passive hyperspectral sensors. The implementation of the capability is driven by current
Nuclear, Biological and Chemical Reconnaissance Vehicle operational tactics and represents a mission focused
alternative of the already demonstrated 5-sensor static Range Test Validation System (RTVS).1 The new capability
consists of hardware for sensor pointing and attitude information which is made available for streaming and aggregation
as part of the data fusion process for threat characterization. Cloud information is generated using 2-sensor data ingested
into a suite of triangulation and tomographic reconstruction algorithms. The approaches are amenable to using a limited
number of viewing projections and unfavorable sensor geometries resulting from mobile operation. In this paper we
describe the system architecture and present an analysis of results obtained during the initial testing of the system at
Dugway Proving Ground during BioWeek 2013.
The Range Test Validation System (RTVS) includes a constellation of five AIRIS-WAD standoff multispectral sensors
oriented around a 1000×1000 meter truth box at a range of 2700 meters. Column density data derived from these sensors
is transmitted in real-time to a command post using a wireless network. The data is used with computed tomographic
methods to produce 3-D cloud concentration profiles for chemical clouds traversing the box. These concentration
profiles are used to provide referee capability for the evaluation of both point and standoff sensors under test. The system
has been used to monitor chemical agent simulants released explosively as well as continuously through specialized
stacks. The system has been demonstrated to accurately map chemical clouds with concentrations as low as 0.5 mg/m3 at
spatial and temporal resolutions of 6 meters and 3 seconds.1 Data products include geo-referenced cloud mass centroids
and boundaries as well as total cloud mass.
The AIRIS Wide Area Detector is an imaging multispectral sensor that has been successfully tested in both ground and
airborne configurations for the detection of chemical and biological agent simulants. The sensor is based on the use of a
Fabry-Perot based tunable filter with a 256x256 pixel HgCdTe focal plane array providing a 32x32 degree field of regard
with 10 meter spatial resolution at a range of 5 km. The sensor includes a real-time processor that produces an infrared
image of the scene under interrogation overlaid with color-coded pixels indicating the identity and location of simulants
detected by the sensor. We review test data from this sensor taken at Dstl Porton Down, NSWC Dahlgren, as well as
from multiple test entries at Dugway Proving Ground. The data indicate the ability to detect release quantities from 0.15
to 360 kg at ranges of ~ 4.7 km including simultaneous multi-simulant releases.
Physical Sciences Inc. (PSI) has recently demonstrated near real-time visualization of chemical vapor plumes via LWIR imaging Fabry-Perot Spectrometry. Simultaneous viewing of the plume from orthogonal lines-of-sight enables estimation of the 3-D plume concentration profile via tomographic analysis of the 2-D 'chemical images' produced by each spectrometer. This paper describes results of field experiments where a controlled release of sulfur hexafluoride (SF6) was viewed by two Adaptive Infrared Imaging Spectroradiometers (AIRIS) located ~1 km from the plume release point. The PSI tomographic algorithm is capable of generating 3-D density distributions of the chemical cloud that are consistent with atmospheric model predictions even in the extreme limitation of using only two sensors viewing the chemical plume. Each AIRIS unit provides a 64 pixel x 64 pixel image with an angular resolution of ~5.5 mrad/pixel. Each AIRIS was configured to provide continuous coverage of the 10.0-10.8 micrometer spectral region at 6-8 cm-1 spectral resolution and exhibits a noise equivalent spectral radiance of ~2 micrometer W/(cm2 sr micrometer).
Physical Sciences Inc. (PSI) has developed an imaging sensor for remote detection of natural gas (methane) leaks. The sensor is comprised of an IR focal plane array-based camera which views the far field through a rapidly tunable Fabry-Perot interferometer. The interferometer functions as a wavelength-variable bandpass filter which selects the wavelength illuminating the focal plane array. The sensor generates 128 pixel x 128 pixel 'methane images' with a spatial resolution of 1 m (>100 x 100 pixel field-of-view). The methane column density at each pixel in the image is calculated in real time using an algorithm which estimates and compensates for line-of-sight atmospheric transmission. The compensation algorithm incorporates range-to-target as well as local air temperature and humidity. System tests conducted at 200 m standoff from sensor to leak location indicate probability of detection >90% for methane column densities >1000 ppmv-m and >2K thermal contrast between the air and the background. The probability of false alarm is <0.2% under these detection conditions.
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