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Proceedings Paper

Second-order network for automatic target recognition in real-beam radar
Author(s): James H. Hughen; Kenneth Rex Hollon
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Paper Abstract

We investigate a type of artificial neural network which has been called a high order network for application to the millimeter wave (MMW) radar stationary target classification problem. The high order network like the multilayer perceptron provides a minimum mean square error (MMSE) estimate of the optimal discriminant, however, the high order network has the advantage of ease of training. This network can be trained via iterative gradient descent and also by a closed form one-pass solution. Using real beam Ka-band radar field data, we compare the classification performance of the high order network with that of a gaussian classifier for several conditions. We found that the high order network performance is improved over the gaussian classifier and further, we obtained very attractive results with the one-pass solution.

Paper Details

Date Published: 12 May 1992
PDF: 9 pages
Proc. SPIE 1630, Synthetic Aperture Radar, (12 May 1992); doi: 10.1117/12.59013
Show Author Affiliations
James H. Hughen, Martin Marietta Electronic Systems (United States)
Kenneth Rex Hollon, Martin Marietta Electronic Systems (United States)


Published in SPIE Proceedings Vol. 1630:
Synthetic Aperture Radar
Richard D. McCoy; Martin E. Tanenhaus, Editor(s)

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