Underwater target classification using wavelet packets and neural networks
- 1 May 2000
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 11 (3) , 784-794
- https://doi.org/10.1109/72.846748
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.Keywords
This publication has 16 references indexed in Scilit:
- Underwater target classification using wavelet packets and neural networksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Active sonar target imaging and classification systemPublished by SPIE-Intl Soc Optical Eng ,1997
- Feature extraction from acoustic backscattered signals using wavelet dictionariesPublished by SPIE-Intl Soc Optical Eng ,1997
- Parallel consensual neural networksIEEE Transactions on Neural Networks, 1997
- A modified block FTF adaptive algorithm with applications to underwater target detectionIEEE Transactions on Signal Processing, 1996
- Beamforming on seismic interface waves with an array of geophones on the shallow sea floorIEEE Journal of Oceanic Engineering, 1995
- Adaptive wavelet classification of acoustic backscatterPublished by SPIE-Intl Soc Optical Eng ,1994
- A computational model of echo processing and acoustic imaging in frequency- modulated echolocating bats: The spectrogram correlation and transformation receiverThe Journal of the Acoustical Society of America, 1993
- Learned classification of sonar targets using a massively parallel networkIEEE Transactions on Acoustics, Speech, and Signal Processing, 1988
- Determination of the resonance spectrum of elastic bodies via the use of short pulses and Fourier transform theoryThe Journal of the Acoustical Society of America, 1986