This report describes tasks comparing the simulated performance levels of infrared (IR) sensing systems in detecting, recognizing, and identifying (DRI) targets using the Night Vision Integrated Performance Model (NV-IPM) version 1.1. Both mid-wave infrared (MWIR) and long-wave infrared (LWIR) systems, chosen to represent the current state-of-the-art, were analyzed across various environmental conditions. These states included a range of both man-made and natural obscurants, selected to simulate atmospheric conditions commonly experienced throughout the world. This report investigates the validity of the NV-IPM, down-selects top-performing systems from an original set, and provides detailed performance analysis of these best-of-breed systems in various environmental scenarios. Six sensing systems, Indium-Antimonide (InSb) MWIR, Mercury-Cadmium-Telluride (MCT) MWIR, nBn InSb MWIR, Quantum Well Infrared Photodetector (QWIP) LWIR, uncooled LWIR, and dual-band MCT MWIR/LWIR system, were evaluated against a variety of environmental variations. Specifications for the IR systems were obtained from manufacturers or relevant published literature. Simulation results indicated the nBn InSb MWIR system as the strongest-performing system in many of the tests.
Though many materials behave approximately as greybodies across the long-wave infrared (LWIR) waveband, certain important infrared (IR) scene modeling materials such as brick and galvanized steel exhibit more complex optical properties1. Accurately describing how non-greybody materials interact relies critically on the accurate incorporation of the emissive and reflective properties of the in-scene materials. Typically, measured values are obtained and used. When measured using a non-imaging spectrometer, a given material’s spectral emissivity requires more than one collection episode, as both the sample under test and a standard must be measured separately. In the interval between episodes changes in environment degrade emissivity measurement accuracy. While repeating and averaging measurements of the standard and sample helps mitigate such effects, a simultaneous measurement of both can ensure identical environmental conditions during the measurement process, thus reducing inaccuracies and delivering a temporally accurate determination of background or ‘down-welling’ radiation. We report on a method for minimizing temporal inaccuracies in sample emissivity measurements. Using a LWIR hyperspectral imager, a Telops Hyper-Cam2, an approach permitting hundreds of simultaneous, calibrated spectral radiance measurements of the sample under test as well as a diffuse gold standard is described. In addition, we describe the data reduction technique to exploit these measurements. Following development of the reported method, spectral reflectance data from 10 samples of various materials of interest were collected. These data are presented along with comments on how such data will enhance the fidelity of computer models of IR scenes.
A Forward Looking Interferometer (FLI) sensor has the potential to be used as a means of detecting aviation hazards in
flight. One of these hazards is mountain wave turbulence. The results from a data acquisition activity at the University
of Colorado's Mountain Research Station will be presented here. Hyperspectral datacubes from a Telops Hyper-Cam
are being studied to determine if evidence of a turbulent event can be identified in the data. These data are then being
compared with D&P TurboFT data, which are collected at a much higher time resolution and broader spectrum.
The use of a hyperspectral imaging system for the detection of gases has been investigated, and algorithms have been
developed for various applications. Of particular interest here is the ability to use these algorithms in the detection of
the wake disturbances trailing an aircraft. A dataset of long wave infrared (LWIR) hyperspectral datacubes taken with a
Telops Hyper-Cam at Hartsfield-Jackson International Airport in Atlanta, Georgia is investigated. The methodology
presented here assumes that the aircraft engine exhaust gases will become entrained in wake vortices that develop;
therefore, if the exhaust can be detected upon exiting the engines, it can be followed through subsequent datacubes until
the vortex disturbance is detected. Gases known to exist in aircraft exhaust are modeled, and the Adaptive
Coherence/Cosine Estimator (ACE) is used to search for these gases. Although wake vortices have not been found in
the data, an unknown disturbance following the passage of the aircraft has been discovered.
Recent research efforts at Georgia Tech have focused on the development of a multi-resolution ocean clutter model. This
research was driven by the need to support both surveillance and search requirements set by several government
customers. These requirements indicated a need to support target detection and tracking for both resolved and unresolved
scenarios for targets located either above or on an ocean surface. As a result of this changing sensor resolution
characteristic for the various acquisition scenarios, a need for accurate ocean surface models at different geometric
resolutions arose. Georgia Tech met this need through development of a multi-resolution approach to modeling both the
ocean surface and, subsequently, the ocean signature across the optical spectrum. This approach combined empirical
overhead data with high resolution ocean surface models to construct a series of varying resolution ocean clutter models.
This paper will describe the approach to utilizing and merging the various clutter models as well as the results of using
these models in the target detection and tracking analysis. Remaining issues associated with this clutter model
development will be identified and potential solutions discussed.