Underwater target classification using wavelet packets and neural networks Azimi-Sadjadi, Mahmood R. ; Yao, De ; Huang, Qiang ; Dobeck, Gerald J. "This work was supported by the Office of Naval Research (ONR321TS) and the Biosonar Program under Contract N00014-99-1-0166. The data and technical support were provided by the NSWC, Coastal Systems Station, Panama City, FL." 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. Colorado State University. Libraries 2000 text ; image application/pdf ECEmra00006.pdf FACFECEN100486ARTI eng c2000 IEEE
Underwater target classification using wavelet packets and neural networks
Azimi-Sadjadi, Mahmood R. ; Yao, De ; Huang, Qiang ; Dobeck, Gerald J.
"This work was supported by the Office of Naval Research (ONR321TS) and the Biosonar Program under Contract N00014-99-1-0166. The data and technical support were provided by the NSWC, Coastal Systems Station, Panama City, FL."
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.
Colorado State University. Libraries
2000
text ; image
application/pdf
ECEmra00006.pdf
FACFECEN100486ARTI
eng
c2000 IEEE