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Proceedings Paper

ATR complexity and template set size
Author(s): Eric A. Freudenthal; Eugene Lavely; William E. Pierson Jr.; Mariam Ali Argyle; Joshua Fishman
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Paper Abstract

We investigate the complexity of template-based ATR algorithms using SAR imagery as an example. Performance measures (such as Pid) of such algorithms typically improve with increasing number of stored reference templates. This presumes, of course, that the training templates contain adequate statistical sampling of the range of observed or test templates. The tradeoff of improved performance is that computational complexity and the expense of algorithm development training template generation (synthetic and/or experimental) increases as well. Therefore, for practical implementations it is useful to characterize ATR problem complexity and to identify strategies to mitigate it. We adopt for this problem a complexity metric defined simply as the size of the minimal subset of stored templates drawn from an available training population that yields a specified Pid. Straightforward enumeration and testing of all possible template sets leads to a combinatorial explosion. Here we consider template selection strategies that are far more practical and apply these to a SAR- and template-based target identification problem. Our database of training templates consists of targets viewed at 3-degree increments in pose (azimuth). The template selection methods we investigate include uniform sampling, sequential forward search (also known as greedy selection), and adaptive floating search. The numerical results demonstrate that the complexity metric increases with intrinsic problem difficulty, and that template sets selected using the greedy method significantly outperform uniformly sampled template sets of the same size. The adaptive method, which is far more computationally expensive, selects template sets that outperform those selected by the greedy technique, but the small reduction in template set size was not significant for the specific examples considered here.

Paper Details

Date Published: 27 August 2001
PDF: 12 pages
Proc. SPIE 4382, Algorithms for Synthetic Aperture Radar Imagery VIII, (27 August 2001); doi: 10.1117/12.438221
Show Author Affiliations
Eric A. Freudenthal, New York Univ. (United States)
Eugene Lavely, ALPHATECH, Inc. (United States)
William E. Pierson Jr., Air Force Research Lab. (United States)
Mariam Ali Argyle, New York Univ. (United States)
Joshua Fishman, New York Univ. (United States)

Published in SPIE Proceedings Vol. 4382:
Algorithms for Synthetic Aperture Radar Imagery VIII
Edmund G. Zelnio, Editor(s)

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