Underwater target classification using wavelet packets and neural networks

In this paper, a new subband-based classification scheme is developed for classifying underwater mines and mine-like targets from the acoustic backscattered signals. The system consists of a feature extractor using wavelet packets, a feature selection scheme, and a backpropagation neural network classifier. The data set used for this study consists of the backscattered signals for seven frequency bands and six different objects: two mine-like targets and four non-targets. The targets are insonified at 72 aspect angles from 0 to 355 degrees with 5 degree increment. Simulation results on ten different realizations of this data set and for signal-to-noise ratio (SNR) of 12 dB are presented. The receiver operating characteristic (ROC) curve of the classifier generated based on these results demonstrates excellent classification performance of the system. In addition, the generalization ability of the trained network is demonstrated by computing the error and classification rate statistics on a large data set consists of 50 different realizations.