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Proceedings Paper

A simulation system for feature-aided tracking research
Author(s): Stanton H. Musick; John U. Sherwood; Teri L. Piatt; Neal A. Carlson
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Paper Abstract

Many years of tracking research have shown that the greatest obstacle to effective track estimation is accurately associating sensor kinematic reports to known tracks, new tracks, or clutter. Errors in report association occur more frequently under increasingly stressful conditions, like closely-spaced targets and low measurement rates, which can lead to unstable and even divergent tracking performance. It is widely expected that adding target features will aid report association and result in enhanced track accuracy and lengthened track life. Although sensors can provide features to enhance association, progress in implementing feature aiding has been slowed by the lack of data and tools that could assist exploration and algorithm development. To encourage research in this important discipline, the Sensors Directorate of the Air Force Research Laboratory (AFRL/SN) is sponsoring a challenge problem called Feature-Aided Tracking of Stop-move Objects (FATSO). FATSO's long-range goal is to provide a full suite of public data and software to promote explorations into viable methods of feature aiding. This paper introduces the FATSO project, focusing on an upcoming release that will contain data from a diverse target set and predictor software for generating radar signatures.

Paper Details

Date Published: 2 September 2004
PDF: 12 pages
Proc. SPIE 5427, Algorithms for Synthetic Aperture Radar Imagery XI, (2 September 2004); doi: 10.1117/12.548005
Show Author Affiliations
Stanton H. Musick, Air Force Research Lab. (United States)
John U. Sherwood, Jacobs Sverdrup (United States)
Teri L. Piatt, Jacobs Sverdrup (United States)
Neal A. Carlson, Integrity Systems, Inc. (United States)

Published in SPIE Proceedings Vol. 5427:
Algorithms for Synthetic Aperture Radar Imagery XI
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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