Constrained neural networks for recognition of passive sonar signals using shape

The author describes a neural network system that recognizes seven different types of passive sonar signals from their characteristic shapes. The system has a preprocessor for signal detection and symbolic representation, a bank of three highly constrained feedforward neural networks for recognition, and a postprocessor for network interpretation and performance adjustment. The preprocessor uses image processing and morphological techniques to extract and track energy, and converts each detected signal into a chain code. The chain code is passed to an ensemble of three independent neural networks, each of which votes on the signal´s type. The system´s performance on 1400 unseen test signals was an adjustable 93% overall correct recognition rate, 5% error rate, and 2% rejection rate