Empirical results of using back-propagation neural networks to separate single echoes from multiple echoes

Empirical results illustrate the pitfalls of applying an artificial neural network (ANN) to classification of underwater active sonar returns. During training, a back-propagation ANN classifier learns to recognize two classes of reflected active sonar waveforms: waveforms having two major sonar echoes or peaks and those having one major echo or peak. It is shown how the classifier learns to distinguish between the two classes. Testing the ANN classifier with different waveforms of each type generated unexpected results: the number of echo peaks was nor the feature used to separate classes.