Underwater acoustic transients can develop from a wide variety of sources. Accordingly, detection and classification of such transients by automated means can be exceedingly difficult. This paper describes a new approach to this problem based on adaptive pattern recognition employing neural networks and an alternative metric named for the German mathematician, Hausdorff. The system uses self-organization to both generalize and provide rapid throughput while utilizing supervised learning for decision making, being based on a concept that temporally partitions acoustic transient signals, and as a result, studies their trajectories through power spectral density space. This method has exhibited encouraging results for a large set of simulated underwater transients contained in both quiet and noisy ocean environments, and requires from five to ten MFLOPS for the implementation described below.


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    Title :

    Detection and classification of underwater acoustic transients using neural networks


    Additional title:

    Entdeckung und Klassifikation von akustischen, zeitlich veränderlichen Unterwasserwellen mit neuronalen Netzen


    Contributors:
    Hemminger, T.L. (author) / Pao, Y.H. (author)

    Published in:

    Publication date :

    1994


    Size :

    7 Seiten, 15 Bilder, 1 Tabelle, 16 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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