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Proceedings Paper

Automatic classification of active sonar data using time-frequency transforms
Author(s): Francesco Lari; Avideh Zakhor
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Paper Abstract

We address automatic classification of active sonar signals using the Wigner Ville transform (WVT), the wavelet transform (WT), and the scalogram. Features are extracted by integrating over regions in time frequency (TF) distribution and are classified by a decision tree. Experimental results show classification and detection rates of up to 92% at -4 dB of SNR. The WT outperforms the WVT and the scalogram particularly at high noise levels; this can be partially attributed to the absence of cross terms in the WT.

Paper Details

Date Published: 16 December 1992
PDF: 8 pages
Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130836
Show Author Affiliations
Francesco Lari, Univ. of California/Berkeley (United States)
Avideh Zakhor, Univ. of California/Berkeley (United States)

Published in SPIE Proceedings Vol. 1766:
Neural and Stochastic Methods in Image and Signal Processing
Su-Shing Chen, Editor(s)

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