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Detection and classification of underwater acoustic transients using neural networks

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2 Author(s)
T. L. Hemminger ; Dept. of Eng. & Eng. Technol., Pennsylvania Univ., Erie, PA, USA ; Yoh-Han Pao

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, the Hausdorff metric. 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

Published in:

IEEE Transactions on Neural Networks  (Volume:5 ,  Issue: 5 )