Gas Chromatography (GC) is routinely used in the laboratory to temporally separate chemical mixtures into their constituent components for improved chemical identification. This paper will provide a overview of more than twenty years of development of one-dimensional field-portable micro GC systems, highlighting key experimental results that illustrate how a reduction in false alarm rate (FAR) is achieved in real-world environments. Significantly, we will also present recent results on a micro two-dimensional GC (micro GCxGC) technology. This ultra-small system consists of microfabricated columns, NanoElectroMechanical System (NEMS) cantilever resonators for detection, and a valve-based stop-flow modulator. The separation of a 29-component polar mixture in less than 7 seconds is demonstrated along with peak widths in the second dimension ranging from 10-60 ms. For this system, a peak capacity of just over 300 was calculated for separation in about 6 s. This work has important implications for field detection, to drastically reduce FAR and significantly improve chemical selectivity and identification. This separation performance was demonstrated with the NEMS resonator and bench scale FID. But other detectors, suitably fast and sensitive can work as well. Recent research has shown that the identification power of GCxGC-FID can match that of GC-MS. This result indicates a path to improved size, weight, power, and performance in micro GCxGC systems outfitted with relatively non-specific, lightweight detectors. We will briefly discuss the performance of possible options, such as the pulsed discharge helium ionization detector (PDHID) and miniature correlation ion mobility spectrometer (mini-CIMS).
Synthetic aperture radar (SAR) images exhibit a fundamental inverse relationship between image quality and
collection range: various metrics and visual inspection clearly indicate that SAR image quality deteriorates as
collection range increases. Standoff constraints typically dictate long-range imaging geometries for operational
use of fielded SAR sensors. At the same time, system validation and data volume considerations typically dictate
short-range imaging geometries for non-operational SAR data collections. This presents a conundrum for the
developers of SAR exploitation applications: despite the fact that a sensor may be used exclusively at long
ranges in operational settings, most or all of the data available for application development and testing may
have been collected at short range. The lack of long-range imagery for development and testing can lead to a
variety of problems, potentially including not only poor robustness to range-induced image-quality degradation,
but even total failure if longer-range imagery invalidates fundamental algorithmic assumptions. We propose
a method for simulating the effects of longer-range collection using shorter-range SAR images. This method
incorporates the predominant contributing factors to range-induced image-quality degradation, including various
signal-attenuation and aperture-decoherence effects. We present examples demonstrating our approach.
Airborne ground moving-target indication (GMTI) radar can track moving vehicles at large standoff distances.
Unfortunately, trajectories from multiple vehicles can become kinematically ambiguous, resulting in confusion
between a target vehicle of interest and other vehicles. We propose the use of high range resolution (HRR) radar
profiles and multinomial pattern matching (MPM) for target fingerprinting and track stitching to overcome
Sandia's MPM algorithm is a robust template-based identification algorithm that has been applied successfully
to various target recognition problems. MPM utilizes a quantile transformation to map target intensity samples
to a small number of grayscale values, or quantiles. The algorithm relies on a statistical characterization of the
multinomial distribution of the sample-by-sample intensity values for target profiles. The quantile transformation
and statistical characterization procedures are extremely well suited to a robust representation of targets for HRR
profiles: they are invariant to sensor calibration, robust to target signature variations, and lend themselves to
efficient matching algorithms.
In typical HRR tracking applications, target fingerprints must be initiated on the fly from a limited number of
HRR profiles. Data may accumulate indefinitely as vehicles are tracked, and their templates must be continually
updated without becoming unbounded in size or complexity. To address this need, an incrementally updated
version of MPM has been developed. This implementation of MPM incorporates individual HRR profiles as they
become available, and fuses data from multiple aspect angles for a given target to aid in track stitching. This
paper provides a description of the incrementally updated version of MPM.
This paper compares three algorithms for potential use in a real-time, on-board implementation of spotlight-mode SAR image formation. These include: the polar formatting algorithm (PFA), the range migration algorithm (RMA), and the overlapped subapertures algorithm (OSA). We conclude that for any reasonable spotlight-mode imaging scenario, PFA is easily the algorithm of choice because its computational efficiency is significantly higher than that of either RMA or OSA. This comparison specifically includes cases in which wavefront curvature is sufficient to cause image defocus in conventional PFA, because a post-processing refocus step can be performed with PFA to yield excellent image quality for only a minimal increase in computation time. We demonstrate that real-time image formation for many imaging scenarios is achievable using PFA implemented on a single Pentium M processor. OSA is quite slow compared to PFA, especially for the case of moderate to high resolution (9 inches and better). RMA is not competitive with PFA for situations that do not require wavefront curvature correction.
For those cases in which PFA requires post-processing to correct for wavefront curvature, RMA comes closer in efficiency to PFA, but is still outperformed by the modified PFA.
Automatic or assisted target recognition (ATR) is an important application of synthetic aperture radar (SAR). Most ATR researchers have focused on the core problem of declaration-that is, detection and identification of targets of interest within a SAR image. For ATR declarations to be of maximum value to an image analyst, however, it is essential that each declaration be accompanied by a reliability estimate or confidence metric. Unfortunately, the need for a clear and informative confidence metric for ATR has generally been overlooked or ignored. We propose a framework and methodology for evaluating the confidence in an ATR system's declarations and competing target hypotheses. Our proposed confidence metric is intuitive, informative, and applicable to a broad class of ATRs. We demonstrate that seemingly similar ATRs may differ fundamentally in the ability-or inability-to identify targets with high confidence.
Proc. SPIE. 4053, Algorithms for Synthetic Aperture Radar Imagery VII
KEYWORDS: 3D acquisition, Detection and tracking algorithms, Data modeling, Scattering, Synthetic aperture radar, 3D modeling, Automatic target recognition, Systems modeling, Model-based design, Solid modeling
A key issue in the development and deployment of model-based automatic target recognition (ATR) systems is the generation of target models to populate the ATR database. Model generation is typically a formidable task, often requiring detailed descriptions of targets in the form of blueprints or CAD models. Recently, efforts to generate models from a single 1-D radar range profile or a single 2-D synthetic aperture radar (SAR) image have met with some success. However, the models generated from these data sets are of limited use to most ATR systems because they are not three-dimensional. We propose a method for generating a 3-D target model directly from multiple SAR images of a target obtained at arbitrary viewing angles. This 3-D model is a parameterized description of the target in terms of its component reflector primitives. We pose the model generation problem as a parametric estimation problem based on information extracted from the SAR images. We accomplish this parametric estimation in the context of data association using the expectation-maximization (EM) method. Although we develop our method in the context of a specific data extraction technique and target parameterization scheme, our underlying framework is general enough to accommodate different choices. We present results demonstrating the utility of our method.
An important problem driving much research in the SAR and model-based ATR communities is the generation and modification of target models for ATR system databases. We propose a method for generating or updating 3-D reflector primitive target models. We utilize an existing 2-D extraction algorithm to extract feature locations and classifications (such as scattering primitive type) from each image in a set of SAR data. We formulate the 3-D model generation in terms of a data association problem. We present an iterative algorithm, based on the expectation-maximization (EM) method, to solve the data association problem and yield a maximum likelihood estimate of target feature locations and types from the set of 2-D extracted features. Finally, we present examples and results for sets of simulated SAR imagery.