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Proceedings Paper

Target classification using radar data: a comparative study
Author(s): Gerardo J. Melendez; Allon Guez; Stanislav Kesler
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Paper Abstract

This paper compares three different approaches when used against a problem of present and practical interest: the classification of radar return data from two classes of aircraft. The three approaches are: the typical feature extraction approach used for target classification when dealing with radar data; a multi layer perceptron neural network approach and; a branched multi layer perceptron neural network approach. The comparison was performed under equal conditions and at restricted sensor parameter conditions to demonstrate the anticipated advantage of the neural network approach in that it will be able to classify targets when the sensor parameters are not suitable for the feature extraction approach to work. The classification rate was used as the measure of effectiveness. Up to date the feature extraction approach has provided a classification rate of 84.1%. The multi layer perceptron consists of a one hidden layer network and the best classification rate it has provided is 86.8%. The branched multi layer perceptron consists of two separate multi layer perceptron neural networks trained to recognize only one class of targets and the best classification rate it has provided is 54.9%. The discrepancy in performance between the two neural network approaches is perhaps due to the more general structure with improved discrimination power of the branched network.

Paper Details

Date Published: 16 September 1992
PDF: 11 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140063
Show Author Affiliations
Gerardo J. Melendez, U.S. Army Communications and Electronics Command (United States)
Allon Guez, Drexel Univ. (United States)
Stanislav Kesler, Drexel Univ. (United States)


Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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