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Proceedings Paper

Neural Net Classifier For Millimeter Wave Radar
Author(s): Joe R. Brown; Sue Archer; Mark R. Bower
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Paper Abstract

This paper describes the development of a neural net classifier for use in an automatic target recognition (ATR) system using millimeter wave (MMW) radar data. Two distinctive neural net classifiers were developed using mapping models (back-propagation and counterpropagation) and compared to a quadratic (Bayesian-like) classifier. A statistical feature set and a radar data set was used for both training and testing all three classifier systems. This statistical feature set is often used to test IMATRs prior to using actual data. Results are presented and indicate that the backpropagation net performed at near 100 percent accuracy for the statistical feature set and slightly outperformed the counterpropagation model in this application. Both networks hold promising results using real radar data.

Paper Details

Date Published: 6 December 1989
PDF: 6 pages
Proc. SPIE 1154, Real-Time Signal Processing XII, (6 December 1989); doi: 10.1117/12.962373
Show Author Affiliations
Joe R. Brown, Martin Marietta Electronic Systems (United States)
Sue Archer, Martin Marietta Electronic Systems (United States)
Mark R. Bower, Martin Marietta Electronic Systems (United States)

Published in SPIE Proceedings Vol. 1154:
Real-Time Signal Processing XII
J. P. Letellier, Editor(s)

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