Classification of underwater signals using a back-propagation neural network
By: Richard Campbell Bennett, Jr
Publisher: Naval Postgraduate School; Available from National Technical Information Service
Published: 1997
Place of publication: Monterey, Calif; Springfield, Va
Language: eng
Number of pages: 97
Description: This thesis examines a number of underwater acoustic signals and the problem of classifying these signals using a back-propagation neural network. The neural network classifies the signals based upon features extracted from the original signals. The effect on classification by using an adaptive line enhancer for noise reduction is explored. Two feature extraction methods have been implemented; modeling by an autoregressive technique using the reduced-rank covariance method, and the discrete wavelet transformation. Both orthonormal and non-orthonormal transforms are considered in this study.
Notes: Thesis advisors, Monique P. Fargues, Roberto Cristi.

Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, June 1997.

Includes bibliographical references (p. 95).

Also available online.

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