Abstract
This paper describes the application of a visual pattern recognition neural network in a hybrid model based automatic target recognition (ATR) system. This neural network forms the feature extraction front end of the ATR and is derived from the Neocognitron network first proposed by K. Fukushima. For complex target recognition, modifications to the basic Neocognitron network paradigm were required to enhance robustness against image distortions due to undersampling (aliasing) and poor feature selection during training. The focus of the paper is on the enhancements, their rationale, and on the use of the network as a self- organizing feature extraction element of an ATR. Results of experiments with the overall ATR system against target imagery are shown and discussed.
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