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Proceedings Paper

Comparison of two neural net classifiers to a quadratic classifier for millimeter-wave radar
Author(s): Joe R. Brown; Mark Roger Bower; Hal E. Beck; Susan J. Archer
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Paper Abstract

This paper describes the comparison of three classifiers for use in an automatic target recognition (ATR) system for millimeter wave (MMW) radar data. The three classifiers were the quadratic (Bayesian-like), the multilayer perceptron using a backpropagation training algorithm (termed backpropagation for short), and the counterpropagation network. Two data sets, statistical with four classes and real radar data with three classes, were used for training and testing all three classifiers. Three experiments were performed including: comparing the performances between the three classifiers on both the statistical feature set and the real radar data; optimal configuration for the backpropagation network; and the number of training iterations required for optimal performance using the backpropagation network before overtraining occurred.

Paper Details

Date Published: 1 August 1990
PDF: 8 pages
Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21172
Show Author Affiliations
Joe R. Brown, Martin Marietta Corp. (United States)
Mark Roger Bower, Martin Marietta Corp. (United States)
Hal E. Beck, Martin Marietta Corp. (United States)
Susan J. Archer, Martin Marietta Corp. (United States)


Published in SPIE Proceedings Vol. 1294:
Applications of Artificial Neural Networks
Steven K. Rogers, Editor(s)

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