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Proceedings Paper

Model-based automatic target recognition from high-range-resolution radar returns
Author(s): John S. Baras; Sheldon I. Wolk
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Paper Abstract

We develop economic target descriptions based on high range resolution target returns, utilizing wavelet multiresolution representations and tree structured vector quantization, in its clustering mode. The algorithm automatically constructs the multi-scale aspect graph of the target. This results in a progressive coding of the target model information and in an extremely efficient, hierarchical indexing of the stored target models. As a final outcome we obtain extremely fast recovery, search, and matching during the on-line ATR operation. We also investigate, the so-called new target insertion problem in a fielded ATR system, and the required fast reprogrammability of the ATR system. We compare the performance and cost (both computational and hardware) of ATR algorithms based on the parallel use of single target aspect graphs vs ATR algorithms using the combined aspect graph for the group of targets under consideration. We show that efficient real-time ATR algorithms can be constructed using the aspect graph of each target in a parallel computation. The resulting architecture includes wavelet preprocessing with neural networks postprocessing. We use synthetic radar returns from ships as the experimental data to demonstrate the performance of the resulting ATR algorithm.

Paper Details

Date Published: 29 July 1994
PDF: 10 pages
Proc. SPIE 2234, Automatic Object Recognition IV, (29 July 1994); doi: 10.1117/12.181051
Show Author Affiliations
John S. Baras, Univ. of Maryland/College Park (United States)
Sheldon I. Wolk, Naval Research Lab. (United States)


Published in SPIE Proceedings Vol. 2234:
Automatic Object Recognition IV
Firooz A. Sadjadi, Editor(s)

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