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This article offers a neural network method for automatic classification of Inverse Synthetic Aperture Radar objects represented in images with high level of postreceive optimization. A full explanation of the procedures of two-layer neural network architecture creating and training is described. The classification in the recognition stage is proposed, based on several main classes or sets of flying objects. The classification sets are designed according to distinctive specifications in the structural models of the aircrafts. The neural network is experimentally simulated in MATLAB environment. Numerical results of the experiments carried, prove the correct classification of the objects in ISAR optimized images. Keywords— artificial neural network, engine position, reference model.

NEURAL NETWORK CLASSIFICATION METHOD FOR AIRCRAFT IN ISAR IMAGES