Nevada Nanotech Systems, Inc. (Nevada Nano) has developed a multi-sensor solution to Chemical, Biological, Radiological, Nuclear and Explosives (CBRNE) detection that combines the Molecular Property Spectrometer™ (MPS™)—a micro-electro-mechanical chip-based technology capable of measuring a variety of thermodynamic and electrostatic molecular properties of sampled vapors and particles—and a compact, high-resolution, solid-state gamma spectrometer module for identifying radioactive materials, including isotopes used in dirty bombs and nuclear weapons. By conducting multiple measurements, the system can provide a more complete characterization of an unknown sample, leading to a more accurate identification. Positive identifications of threats are communicated using an integrated wireless module. Currently, system development is focused on detection of commercial, military and improvised explosives, radioactive materials, and chemical threats. The system can be configured for a variety of CBRNE applications, including handheld wands and swab-type threat detectors requiring short sample times, and ultra-high sensitivity detectors in which longer sampling times are used. Here we provide an overview of the system design and operation and present results from preliminary testing.
Chemical detection using infrared hyperspectral imaging systems often is limited by the effects of variability of the scene background emissivity spectra and temperature. Additionally, the atmospheric up-welling and down-welling radiance and transmittance are difficult to estimate from the hyperspectral image data, and may vary across the image. In combination, these background variability effects are referred to as "clutter." A study has been undertaken at Pacific Northwest National Laboratory to determine the relative impact of atmospheric variability and background variability on the detection of trace chemical vapors. This study has analyzed Atmospheric Emitted Radiance Interferometer data to estimate fluctuations in atmospheric constituents. To allow separation of the effects of background and atmospheric variability, hyperspectral data was synthesized using large sets of simulated atmospheric spectra, measured background emissivity spectra, and measured high-resolution gas absorbance spectra. The atmosphere was simulated using FASCODE in which the constituent gas concentrations and temperatures were varied. These spectral sets were combined synthetically using a physics model to realize a statistical synthetic scene with a plume present in a portion of the image. Noise was added to the image with the level determined by a numerical model of the hyperspectral imaging instrument. The chemical detection performance was determined by applying a matched-filter estimator to both the on-plume and off-plume regions. The detected levels in the off-plume region were then used to determine the noise equivalent concentration path length (NECL), a measure of the chemical detection sensitivity. The NECL was estimated for numerous gases and for a variety of background and atmospheric conditions to determine the relative impact of instrument noise, background variability, and atmospheric variability.
Hyperspectral images in the long wave-infrared can be used for quantification of analytes in stack plumes. One approach uses eigenvectors of the off-plume covariance to develop models of the background that are employed in quantification. In this paper, it is shown that end members can be used in a similar way with the added advantage that the end members provide a simple approach to employ non-negativity constraints. A novel approach to end member extraction is used to extract from 14 to 53 factors from synthetic hyperspectral images. It is shown that the eigenvector and end member methods yield similar quantification performance and, as was seen previously, quantification error depends on net analyte signal.
Mismatch between the temperature of the spectra used in the estimator and the actual plume temperature was also studied. A simple model used spectra from three different temperatures to interpolate to an “observed” spectrum at the plume temperature. Using synthetic images, it is shown that temperature mismatch generally results in increases in quantification error. However, in some cases it caused an off-set of the model bias that resulted in apparent decreases in quantification error.