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Summary: 784 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 11, NO. 3, MAY 2000
Underwater Target Classification Using Wavelet
Packets and Neural Networks
Mahmood R. Azimi-Sadjadi, Senior Member, IEEE, De Yao, Qiang Huang, and Gerald J. Dobeck
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 selec-
tion 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 demon-
strated 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
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