Hyperspectral imaging sensors operating in the visual, near IR, and thermal IR bands are sufficiently advanced to
become a standard component of surveillance sensor suites. The output of these sensors contains a wealth of spectral
and spatial information that can improve target detection and recognition performance. However, the large volume and
complex features of hyperspectral data are challenges to automatic target recognition (ATR) algorithm development, and
a simulation of hyperspectral sensing is therefore essential in evaluating algorithm performance. This paper describes
the Infrared Hyperspectral Scene Simulation (IRHSS), an accurate, non-real-time large-scene simulation tool for
hyperspectral imagers operating in the thermal IR bands. The simulation contains models for target and background
spectral radiance, atmospheric propagation, and sensor processing. It uses a new hyperspectral version of the Multi-service
Electro-optical Signature (MuSES) model to compute scene temperatures and hyperspectral radiances. IRHSS
is able to handle very large terrain and feature databases by selective use of radiation view factors. It provides end-to-end
simulation starting with scene models built from COTS simulation databases with faceted terrain and targets, and
optional overlays of visual high-resolution texture imagery. IRHSS can be run as a standalone application via its
Windows-based graphical user interface (GUI) or as a plug-in to existing software using the IRHSS application
programming interface (API). Some screen images of the IRHSS GUI and example hyperspectral image cubes
generated by IRHSS are included herein.
In recent years, military operations have seen an increasing demand for high-fidelity predictive ground target signature
modeling in the hyperspectral thermal IR bands (2 to 25 μm). Simulating hyperspectral imagery of large scenes has
become a necessary component in evaluating ATR algorithms due to the prohibitive costs and the large volume of data
amassed by multi-band imaging sensors. To address this need, MuSES (Multi-Service Electro-optic Signature code), a
validated infrared signature prediction program developed for modeling ground targets, has been enhanced to compute
bi-directional reflectance distribution radiances and atmospheric propagation hyperspectrally, and to generate
hyperspectral image data cubes. In this paper, we present the extensions in MuSES and report on how the additional
features have allowed MuSES to be integrated into the Infra-Red Hyperspectral Scene Simulation (IRHSS), a scene
simulation tool that efficiently models sensor-weighted hyperspectral imagery of large IR synthetic scenes with full
thermal interaction between the target and terrain.
Law enforcement and military operations would clearly benefit from a capability to locate snipers by backtracking the sniper's bullet trajectory. Achieving sufficient backtracking accuracy for bullets is a demanding radar design, requiring good measurement accuracy, high update rate, and detection of very low cross-section objects. In addition, reasonable cost is a driving requirement for law enforcement use. These divergent design requirements are addressed in an experimental millimeter-wave focal plane array radar that uses integrated millimeter-wave receiver technology. The radar is being built for DARPA by Technology Service Corporation, with assistance from M.I.T. Lincoln Laboratory and QuinStar Technology. The key element in the radar is a 35-GHz focal plane array receiver. The receiving antenna lens focuses radar signals from a wide field of view onto an array of receivers, each receiver processing a separate element of the field of view. Receiver detections are then combined in a tracking processor. An FM-CW waveform is used to provide high average power, good range resolution, and stationary clutter rejection. TSC will be testing the sniper detection radar, using radar environment simulator technology developed at Lincoln Laboratory. The simulator will retransmit the received signal with the range delay, Doppler shift, and ERP for various simulated bullet trajectories.
This paper describes the development of a computer based infrared mission rehearsal system (IR-MRS). The IR-MRS can be used for IR mission planning, selecting navigation waypoints, and for mission rehearsal. For specific scenarios, locales, and environments the IR-MRS can simulate ingress/egress corridors and realistic target engagement ranges. In addition, the sensor model's modular design allows the IR-MRS to have a very wide applicability to problems involving either the design or the simulation of infrared sensors. The simulation is performed on Silicon Graphics (SGI) computers in order to take advantage of SGI's accelerated 3D graphics hardware. The system capability includes, conversion of synthetic visual databases to infrared databases, environmental effects, IR database fly through, and modeling the characteristics of specific FLIRs.
Technology Service Corporation (TSC) improved the image generator portion of an IR scene simulator that is used for dynamic, real-time, hardware-in-the-loop testing of an IR sensor system. This paper describes the IR simulator prior to its improvement, the goals that were set for the image generator upgrade, and the resultant improved IR simulator, including its new features (e.g., antialiasing, smooth shading, and texture). The steps involved in computing an IR scene are also discussed, as are the software models used to generate the IR scene and an alternative test equipment configuration that was devised for the simulator.
In the last year, several moderately priced real-time image generators offering textured rendering have appeared on the market. These image generators provide an opportunity for more realistic IR image simulation by significantly increasing the level of detail of the simulated image with no degradation in real-time performance. As part of the development of a new imaging IR simulator, the authors have incorporated the texturing feature of their image generator with their thermal model to generate thermally accurate textured IR backgrounds. The textured thermal model uses a two-dimensional Markov process to generate a background temperature fluctuation map; this model has been proposed by several studies of thermal IR terrain measurements. The textured surfaces are processed through the radiance and sensor models to generate the simulated images.
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