Radar signature generation for feature-aided tracking research

Teri L. Piatt; John U. Sherwood; Stanton H. Musick

doi:10.1117/12.606188

19 May 2005 Radar signature generation for feature-aided tracking research

Teri L. Piatt, John U. Sherwood, Stanton H. Musick

Proceedings Volume 5808, Algorithms for Synthetic Aperture Radar Imagery XII; (2005) https://doi.org/10.1117/12.606188
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

Accurately associating sensor kinematic reports to known tracks, new tracks, or clutter is one of the greatest obstacles to effective track estimation. Feature-aiding is one technology that is emerging to address this problem, and it is expected that adding target features will aid report association by enhancing track accuracy and lengthening track life. The Sensor's Directorate of the Air Force Research Laboratory is sponsoring a challenge problem called Feature-Aided Tracking of Stop-move Objects (FATSO). The long-range goal of this research is to provide a full suite of public data and software to encourage researchers from government, industry, and academia to participate in radar-based feature-aided tracking research. The FATSO program is currently releasing a vehicle database coupled to a radar signature generator. The completed FATSO system will incorporate this database/generator into a Monte Carlo simulation environment for evaluating multiplatform/multitarget tracking scenarios. The currently released data and software contains the following: eight target models, including a tank, ammo hauler, and self-propelled artillery vehicles; and a radar signature generator capable of producing SAR and HRR signatures of all eight modeled targets in almost any configuration or articulation. In addition, the signature generator creates Z-buffer data, label map data, and radar cross-section prediction and allows the user to add noise to an image while varying sensor-target geometry (roll, pitch, yaw, squint). Future capabilities of this signature generator, such as scene models and EO signatures as well as details of the complete FATSO testbed, are outlined.

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Teri L. Piatt, John U. Sherwood, and Stanton H. Musick "Radar signature generation for feature-aided tracking research", Proc. SPIE 5808, Algorithms for Synthetic Aperture Radar Imagery XII, (19 May 2005); https://doi.org/10.1117/12.606188

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KEYWORDS

Synthetic aperture radar

Radar

Sensors

Data modeling

Seaborgium

Databases

Monte Carlo methods

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