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

Volume

4

fYear

2000

fDate

2000

Firstpage

2461

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on

Conference_Location

Hefei

Print_ISBN

0-7803-5995-X

Type

conf

DOI

10.1109/WCICA.2000.862484

Filename

862484