Dobeck, Gerald J., authorHuang, Qiang, authorYao, De, authorAzimi-Sadjadi, Mahmood R., authorIEEE, publisher2007-01-032007-01-032000Azimi-Sadjadi, Mahmood R., et al., Underwater Target Classification Using Wavelet Packets and Neural Networks, IEEE Transactions on Neural Networks 11, no. 3 (May 2000): 784-794.http://hdl.handle.net/10217/924In 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.born digitalarticleseng©2000 IEEE.Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.neural networklinear predictive codingfeature extractionunderwater target classificationwavelet packetsText