Title :
Application of the zero-crossing rate, LOFAR spectrum and wavelet to the feature extraction of passive sonar signals
Author :
Xueyao, Li ; Fuping, Zhu
Author_Institution :
Harbin Eng. Univ., Harbin, China
Abstract :
In this paper, the features extraction of passive sonar signals and classification recognition of underwater target are introduced. Due to the complexity and non-stationary of underwater signals, the zero-cross ratio is first used to initially classify the noise signal; then the LOFAR spectrum reflecting non-stationary signal is extracted, and during which the wavelet transform is carried out for some classes of signals. Finally, a fuzzy ART neural network is constructed to carry out the classification. Results of the experiment show that, for six-class target 147 running environments, 5000 realistic data of ship, the mean correct ratio achieves 89%. The result obtained is satisfactory
Keywords :
ART neural nets; feature extraction; fuzzy neural nets; object recognition; pattern classification; sonar imaging; spectral analysis; wavelet transforms; LOFAR spectrum; feature extraction; fuzzy ART neural network; object recognition; passive sonar signals; pattern classification; underwater target; wavelet transform; zero-crossing rate; Data mining; Feature extraction; Fuzzy neural networks; Neural networks; Signal to noise ratio; Sonar; Subspace constraints; Target recognition; Wavelet transforms; Working environment noise;
Conference_Titel :
Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on
Conference_Location :
Hefei
Print_ISBN :
0-7803-5995-X
DOI :
10.1109/WCICA.2000.862484
