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Underwater Target Recognition Using Time-Frequency Analysis and Elliptical Fuzzy Clustering Classifications

[+] Author Affiliations
Hui Ou, John S. Allen, III, Vassilis L. Syrmos

University of Hawaii at Manoa, Honolulu, HI

Paper No. OMAE2009-80211, pp. 725-733; 9 pages
doi:10.1115/OMAE2009-80211
From:
  • ASME 2009 28th International Conference on Ocean, Offshore and Arctic Engineering
  • Volume 4: Ocean Engineering; Ocean Renewable Energy; Ocean Space Utilization, Parts A and B
  • Honolulu, Hawaii, USA, May 31–June 5, 2009
  • Conference Sponsors: Ocean, Offshore and Arctic Engineering Division
  • ISBN: 978-0-7918-4344-4 | eISBN: 978-0-7918-3844-0
  • Copyright © 2009 by ASME

abstract

A novel underwater target recognition approach has been developed based on the use of Wigner-type Time-Frequency (TF) analysis and the elliptical Gustafson-Kessel (GK) clustering algorithm. This method is implemented for the acoustic backscattered signals of the targets, and more precisely from the examination of echo formation mechanisms in the TF plane. For each of the training signals, we generate a clustering distribution which represents the signal’s TF characteristics by a small number of clusters. A feature template is created by combining the clustering distributions for the signals from the same training target. In the classification process, we calculate the clustering distribution of the test signal and compare it with the feature templates. The target is discriminated in terms of the best match of the clustering pattern. The advantages of GK clustering are that it allows elliptical-shaped clusters, and it automatically adjusts their shapes according to the distribution of the TF feature patterns. The recognition scheme has been applied to discriminate four spherical shell targets filled with different fluids. The data sets are the simulated acoustic responses from these targets, including the interferences caused by the seafloor interaction. [J. A. Fawcett, W. L. J. Fox, and A. Maguer, J. Acoust. Soc. Am. 104, 3296–3304 (1998)]. To evaluate the system robustness, white Gaussian noise is added to the acoustic responses. More than 95% of correct classification is obtained for high Signal-to-Noise Ratio (SNR), and it is maintained around 70% for very low SNRs.

Copyright © 2009 by ASME

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