OPTICAL IMPLEMENTATION OF A FEATURE-BASED NEURAL NETWORK WITH APPLICATION TO AUTOMATIC TARGET RECOGNITION

An optical neural network based on the neocognitron paradigm [IEEE Trans. Syst. Man Cybern. SMC-13, 826-834 (1983)] is introduced. A novel aspect of the architectural design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by feeding back the output of the feature correlator iteratively to the input spatial light modulator and by updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intraclass fault tolerance and interclass discrimination is achieved. A detailed system description is provided. Experimental demonstrations of a two-layer neural network for space-object discrimination is also presented.