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Proceedings Paper

Self-training algorithms for ultrawideband radar target detection
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Paper Abstract

An ultra-wideband (UWB) synthetic aperture radar (SAR) simulation technique that employs physical and statistical models is developed and presented. This joint physics/statistics based technique generates images that have many of the "blob-like" and "spiky" clutter characteristics of UWB radar data in forested regions while avoiding the intensive computations required for the implementation of low-frequency numerical electromagnetic simulation techniques. Approaches towards developing "self-training" algorithms for UWB radar target detection are investigated using the results of this simulation process. These adaptive approaches employ some form of modified singular value decomposition (SVD) algorithm where small blocks of data in the neighborhood of a sliding test window are processed in real-time in an effort to estimate localized clutter characteristics. These real-time local clutter models are then used to cancel clutter in the sliding test window. Comparative results from three SVD-based approaches to adaptive and "self-trained" target detection algorithms are reported. These approaches are denoted as "Energy-Normalized SVD", "Condition-Statistic SVD", and "Terrain-Filtered SVD". The results indicate that the "Terrain-Filtered SVD" approach, where a pre-filter is applied in an effort to eliminate severe clutter discretes that adversely effect performance, appears promising for the purposes of developing "self-training" algorithms for applications that may require localized "on-the-fly" training due to a lack of accurate off-line training data.

Paper Details

Date Published: 12 September 2003
PDF: 11 pages
Proc. SPIE 5095, Algorithms for Synthetic Aperture Radar Imagery X, (12 September 2003); doi: 10.1117/12.484882
Show Author Affiliations
Atindra K. Mitra, Air Force Research Lab. (United States)
Thomas Lee Lewis, Air Force Research Lab. (United States)
Anindya S. Paul, Wright State Univ. (United States)
Arnab Kumar Shaw, Wright State Univ. (United States)


Published in SPIE Proceedings Vol. 5095:
Algorithms for Synthetic Aperture Radar Imagery X
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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