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    Undersea target classification using canonical correlation analysis

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    http://hdl.handle.net/10217/994
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    ECEmra00064.pdf (381.0Kb)
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    Abstract
    Canonical correlation analysis is employed as a multiaspect feature extraction method for underwater target classification. The method exploits linear dependence or coherence between two consecutive sonar returns, at different aspect angles. This is accomplished by extracting the dominant canonical correlations between the two sonar returns and using them as features for classifying mine-like objects from nonmine-like objects. The experimental results on a wideband acoustic backscattered data set, which contains sonar returns from several mine-like and nonmine-like objects in two different ...
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    Author(s)
    Scharf, Louis L.; Azimi-Sadjadi, Mahmood R.; Pezeshki, Ali

    Date Issued
    2007
    Format
    born digital; articles
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    • Faculty Publications - Department of Electrical and Computer Engineering

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