Second-order network for automatic target recognition in real-beam radar

James H. Hughen; Kenneth Rex Hollon

doi:10.1117/12.59013

12 May 1992 Second-order network for automatic target recognition in real-beam radar

James H. Hughen, Kenneth Rex Hollon

Proceedings Volume 1630, Synthetic Aperture Radar; (1992) https://doi.org/10.1117/12.59013
Event: OE/LASE '92, 1992, Los Angeles, CA, United States

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.

Citation Download Citation

James H. Hughen and Kenneth Rex Hollon "Second-order network for automatic target recognition in real-beam radar", Proc. SPIE 1630, Synthetic Aperture Radar, (12 May 1992); https://doi.org/10.1117/12.59013

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KEYWORDS

Linear filtering

Automatic target recognition

Radar

Digital filtering

Image filtering

Visual process modeling

Visual system

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