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 in conjunction with linear predictive coding (LPC), a feature selecti...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2000-05, Vol.11 (3), p.784-794
Main Authors: Azimi-Sadjadi, M.R., De Yao, Qiang Huang, Dobeck, G.J.
Format: Article
Language:English
Subjects:
Backpropagation
Backscattering
Character generation
Classification
Classifiers
Computational modeling
Computer simulation
Data mining
Feature extraction
Fusion power generation
Linear predictive coding
Neural networks
Signal to noise ratio
Statistics
Underwater
Underwater acoustics
Wavelet
Wavelet packets
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Summary: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.
ISSN:1045-9227
2162-237X
1941-0093
2162-2388
DOI:10.1109/72.846748