Abstract
Automatic target recognition processors typically employ several stages of processing, each with a different operational purpose. New shift-invariant filters using morphological and Gabor wavelet transform operations are described for use in the initial stages of such a system. Their realization on simple correlation neural networks are noted, together with the use of neural net optimization techniques to design such filters. A new feature space trajectory classifier neural network is described that identifies the class and pose of each object, rejects clutter false alarms, and overcomes various issues associated with other classier neural networks.
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