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Novel approach to sonar target identification using backpropagation neural networks

In this paper, two back propagation neural networks were trained to recognize four hollow cylinders. These cylinders are of the same size and thickness, but made of different materials. Two neural networks were used in this experiment. In case one a single hidden layer network was used, while in case two a hidden layer neural network was used. The acoustic data from these experiments was taken from references 3 and 7. The data are acoustic echoes of hollow aluminum, bronze, glass, and steel cylinders. The results seem to indicate that characteristics of the cylinders are more observable in the modulations of the frequency spectrum than in the time and spectral signals. The results show that a two hidden layer neural network can improve identification rate of a target.