Automatic classification of active sonar data using time-frequency transforms

Francesco Lari; Avideh Zakhor

doi:10.1117/12.130836

16 December 1992 Automatic classification of active sonar data using time-frequency transforms

Francesco Lari, Avideh Zakhor

Author Affiliations +

Proceedings Volume 1766, Neural and Stochastic Methods in Image and Signal Processing; (1992) https://doi.org/10.1117/12.130836
Event: San Diego '92, 1992, San Diego, CA, United States

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.

Citation Download Citation

Francesco Lari and Avideh Zakhor "Automatic classification of active sonar data using time-frequency transforms", Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); https://doi.org/10.1117/12.130836

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