The development of PlumeNet, a thermal imagery based classifier for aerosolized chemical and biological warfare agents, is detailed. PlumeNet is a convolutional neural network designed for the real-time classification of threat-like plumes from background clutter. The model weights were trained from the ground up using thermal imagery of simulant plumes recorded at various test events. The performance between different convolutional neural network architectures are compared. An analysis of the final model layers through activation mapping methods is performed to demystify the methods by which PlumeNet performs classification. The classification performance of PlumeNet at government conducted open-release field testing at Dugway Proving Ground is detailed.
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.
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.
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.
The defense of the US armed forces against chemical and biological (CB) attack is transitioning from a focus on standoff detection of these threats to the concept of Early Warning (EW). In this approach an array of dual-use and low-burden dedicated use sensor capabilities are used to replace longer-range single use sensors to detect a CB attack. In this paper we discuss the use of passive broadband thermal imaging to detect chemical vapor clouds as well as a developing suite of compact UAV-borne chemical and radiological sensors for the investigation of threats detected by these indirect approaches. The sensors include a colorimetric ammonia sensor, a chemical sensor based on ion mobility spectrometry, and a radiation detector based on gamma ray scintillation. The implementation and initial field tests of each of these sensor modalities is discussed and future plans for the further development of the capability is presented.
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 standoff detection and discrimination of aerosolized biological and chemical agents has traditionally been addressed through LIDAR approaches, but sensor systems using these methods have yet to be deployed. We discuss the development and testing of an approach to detect these aerosols using the deployed base of passive infrared hyperspectral sensors used for chemical vapor detection.
The detection of aerosols requires the inclusion of down welling sky and up welling ground radiation in the description of the radiative transfer process. The wavelength and size dependent ratio of absorption to scattering provides much of the discrimination capability. The approach to the detection of aerosols utilizes much of the same phenomenology employed in vapor detection; however, the sensor system must acquire information on non-line-of-sight sources of radiation contributing to the scattering process.
We describe the general methodology developed to detect chemical or biological aerosols, including justifications for the simplifying assumptions that enable the development of a real-time sensor system. Mie scattering calculations, aerosol size distribution dependence, and the angular dependence of the scattering on the aerosol signature will be discussed.
This methodology will then be applied to two test cases: the ground level release of a biological aerosol (BG) and a nonbiological confuser (kaolin clay) as well as the debris field resulting from the intercept of a cruise missile carrying a thickened VX warhead. A field measurement, conducted at the Utah Test and Training Range will be used to illustrate the issues associated with the use of the method.
Two AIRIS sensors were tested at Dugway Proving Grounds against chemical agent vapor simulants. The primary objectives of the test were to: 1) assess performance of algorithm improvements designed to reduce false alarm rates with a special emphasis on solar effects, and 3) evaluate performance in target detection at 5 km.
The tests included 66 total releases comprising alternating 120 kg glacial acetic acid (GAA) and 60 kg triethyl phosphate (TEP) events. The AIRIS sensors had common algorithms, detection thresholds, and sensor parameters. The sensors used the target set defined for the Joint Service Lightweight Chemical Agent Detector (JSLSCAD) with TEP substituted for GA and GAA substituted for VX. They were exercised at two sites located at either 3 km or 5 km from the release point.
Data from the tests will be presented showing that: 1) excellent detection capability was obtained at both ranges with significantly shorter alarm times at 5 km, 2) inter-sensor comparison revealed very comparable performance, 3) false alarm rates < 1 incident per 10 hours running time over 143 hours of sensor operations were achieved, 4) algorithm improvements eliminated both solar and cloud false alarms. The algorithms enabling the improved false alarm rejection will be discussed.
The sensor technology has recently been extended to address the problem of detection of liquid and solid chemical agents and toxic industrial chemical on surfaces. The phenomenology and applicability of passive infrared hyperspectral imaging to this problem will be discussed and demonstrated.
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.
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.
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.
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).
The Airborne Chemical Imaging System (ACIS) is a research platform used to evaluate passive infrared (IR) standoff detectors for airborne remote sensing of chemical vapors. It consists of a sensor suite mounted in an automated gyro-stabilized optical platform. The sensor pod is currently mounted on a UH-1 helicopter but could also be adapted to other platforms. Two developmental IR imaging sensors are used in the ACIS: a high-speed Fourier transform infrared (FTIR) spectrometer: the TurboFT, and a high-resolution tunable IR Fabry-Perot spectroradiometer: the AIRIS. The TurboFT is a high-speed (100 Hz) low-resolution (2x8 pixel) system and the AIRIS is a low-speed (~0.5 Hz), high-resolution (64x64 pixel) imager. This paper describes the ACIS configuration, general system specifications, operational concerns, and some typical results from recent flight tests.
The Pronghorn Field Tests were held at the Nevada Test Site for a two-week period in June 2001. Two passive infrared sensors were tested for inclusion into the Joint Service Wide Area Detection Program. The Adaptive InfraRed Imaging Spectroradiometer (AIRIS) and Compact Atmospheric Sounding Interferometer (CATSI) systems were tested with good results. This field test was a joint effort between the US (SBCCOM) and Canada (DREV). Various chemicals were detected and quantified from a distance of 1.5 kilometers. Passive ranging of Chemical Plumes was demonstrated.
The Pronghorn Field Tests were held at the Nevada Test Site for a two-week period in June 2001. Two passive infrared sensors were tested for inclusion into the Joint Service Wide Area Detection Program. The Adaptive InfraRed Imaging Spectroradiometer (AIRIS) and Compact ATmospheric Sounding Interferometer (CATSI) systems were tested with good results. This field test was a joint effort between the U.S (SBCCOM) and Canada (DREV). Various chemicals were detected and quantified from a distance of 1.5 kilometers. Passive ranging of Chemical Plumes was demonstrated.
Physical Sciences Inc. has developed and tested two long-wavelength infrared (LWIR) hyperspectral imaging spectroradiometers based on the insertion of a rapidly tunable Fabry-Perot etalon in the field of view of a HgCdTe focal plane array (FPA). The tunable etalon-based optical system enables a wide field-of-view and the acquisition of narrowband (7 to 11 cm-1 spectral resolution), radiometrically calibrated imagery throughout the 8 to 11 micrometers spectral region. The instruments function as chemical imaging sensors by comparing the spectrum of each pixel in the scene with reference spectra of target chemical species. We present results of recent field tests in this paper.
An experimental and modeling study performed to estimate the spectral radiance of surface contaminants is presented. The goal of the study is to address issues relevant to the passive standoff detection of surface contaminants. For this experiment, SF96 and Krylon 41325 are used as contaminant simulants and the contamination of four different surfaces (aluminum, grass, soil and plywood) is analyzed. A first order model of reflectance for surface contaminants is proposed. Measurements of spectral radiance with the CATSI system is compared with the best-fit spectra derived from the model. The experimental results agree well with the model best fits for Krylon on aluminum and grass samples. For Krylon on soil and SF96 on plywood the model best fits fail to reproduce the experimental spectra. The reasons for this discrepancy is discussed.
Physical Sciences Inc. (PSI) has developed an Adaptive IR Imaging Spectroradiometer, comprised of a low-order tunable Fabry-Perot etalon coupled to an HgCdTe detector array, for passive, stand-off detection of chemical vapor plumes. The tunable etalon allows coverage of the 9.5 to 14 micrometers spectral region with a resolution of approximately 7 cm-1 and provides the capability to obtain monochromatic images of a scene at only those wavelengths needed for chemical species identification and quantification. The adaptive sampling capability of the etalon allows suppression of background clutter and minimization of data volume. The tuning time between transmission wavelengths is typically approximately 10 ms, however the mirror tuning system may be operated to obtain tuning times as short as 1.3 ms. We present results using a brassboard imaging system for stand-off detection and visualization of chemical vapor plumes against near ambient temperature backgrounds. This data shows detections limits of 22 ppmv m and 0.6 ppmv m for DMMP and SF6 respectively against a (Delta) T of 6 K. The reported detection limits are consistent with the measured system noise-equivalent spectral radiance, approximately 2 (mu) W cm-2 sr-1 micrometers -1.
This paper discusses the development of a frequency agile receiver for CO2 laser based differential absorption lidar (DIAL) systems. The receiver is based on the insertion of a low-order tunable etalon into the detector field of view. The incorporation of the etalon in the receiver reduces system noise by decreasing the instantaneous spectral bandwidth of the IR detector to a narrow wavelength range centered on the transmitted CO2 laser line, thereby improving the overall D* of the detection system. A consideration of overall lidar system performance result in a projected factor of 2 to 7 reduction in detector system noise, depending on the characteristics of the environment being probed. These improvements can play a key role in extending the ability of DIAL to monitor chemical releases form long standoff distances.
The adaptive IR imaging spectroradiometer (AIRIS) is a multispectral imaging system comprising a low-order tunable Fabry-Perot etalon coupled to an IR focal plane array. This low-order interferometer based imaging system provides wide spectral coverage combined with narrow spectral bandwidth, flexible and adaptive sampling and processing of the image to isolate specific spectral features or signatures, high radiance sensitivity, and an extended field-of-view for the survey of wide areas. The adaptive sampling capability of the AIRIS sensor provides the opportunity to rapidly image a scene at only those wavelengths needed for target identification and clutter suppression. We have developed a prototype LWIR AIRIS sensor to perform passive stand-off detection of hazardous chemical vapor plumes. The imaging sensor covers the 10.0 to 11.5 micrometers region and allows identification of numerous compounds, including chemical warfare agents and simulants, on the basis of observed IR spectra. The LWIR AIRIS has a 40 X 40 deg FOV and a NESR equals 2 (mu) W cm-2 sr-1, resulting in a detection limit of 25 ppmv*m for DMMP against a temperature drop of 6 degrees C.
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