Underwater target classification using wavelet packets and neural networks

TLDR

The paper develops a subband‑based classification scheme to identify underwater mines and mine‑like targets from acoustic backscattered signals. The system extracts features with wavelet packets and linear predictive coding, selects relevant subbands, and classifies them using a backpropagation neural network, with data from six objects at multiple aspect angles and a multiaspect fusion step to enhance performance. Simulations at 12 dB SNR show the classifier achieves excellent ROC performance and generalizes well, as evidenced by low error and high classification rates on a large test set.

Abstract

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 in conjunction with linear predictive coding (LPC), a feature selection scheme, and a backpropagation neural-network classifier. The data set used for this study consists of the backscattered signals from six different objects: two mine-like targets and four nontargets for several aspect angles. Simulation results on ten different noisy realizations 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 demonstrated excellent classification performance of the system. The generalization ability of the trained network was demonstrated by computing the error and classification rate statistics on a large data set. A multiaspect fusion scheme was also adopted in order to further improve the classification performance.