A robust discriminant parameter set for the underwater object classification

Author

Radoi, Emanuel ; Quinquis, André

Author_Institution

EIA Dept., ENSIETA, Brest, France

Volume

2

fYear

1997

fDate

6-9 Oct 1997

Firstpage

789

Abstract

The time-frequency analysis of the magnetic signals, generated by ferromagnetic objects, is used in order to find a robust discriminant parameter set for their classification. An original combination of energetic, spectral and time-scale based parameters is proposed. An excellent recognition rate and a good and regular behavior in the presence of noise are obtained. It is also proven that only a complex analysis of the acquired signals using their representations in different domains is able to lead to good results

Keywords

feature extraction; neural nets; object detection; object recognition; pattern classification; signal sampling; singular value decomposition; spectral analysis; time-frequency analysis; vector quantisation; wavelet transforms; MUSIC algorithm; Nyquist sampling; SVD; complex analysis; discriminant analysis eigenvalues; energetic parameters; feature selection; ferromagnetic objects; learning VQ classifier; magnetic signals; neural net; parameter extraction; passive sensor; recognition rate; robust discriminant parameter set; spectral parameters; time-frequency analysis; time-scale based parameters; underwater object classification; wavelet packet analysis; Acoustic sensors; Magnetic analysis; Magnetic field measurement; Magnetic sensors; Noise robustness; Signal analysis; Signal generators; Signal processing; Signal to noise ratio; Time frequency analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

OCEANS '97. MTS/IEEE Conference Proceedings

Conference_Location

Halifax, NS

Print_ISBN

0-7803-4108-2

Type

conf

DOI

10.1109/OCEANS.1997.624093

Filename

624093