DocumentCode :
1167893
Title :
Detection and classification of underwater acoustic transients using neural networks
Author :
Hemminger, Thomas L. ; Pao, Yoh-Han
Author_Institution :
Dept. of Eng. & Eng. Technol., Pennsylvania Univ., Erie, PA, USA
Volume :
5
Issue :
5
fYear :
1994
fDate :
9/1/1994 12:00:00 AM
Firstpage :
712
Lastpage :
718
Abstract :
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
Keywords :
acoustic signal processing; neural nets; pattern recognition; signal detection; sonar; underwater sound; Hausdorff metric; acoustic transient signals; adaptive pattern recognition; classification; decision making; detection; neural networks; ocean environments; passive sonar; self-organization; supervised learning; underwater acoustic transients; Acoustic noise; Acoustic signal detection; Decision making; Neural networks; Pattern recognition; Supervised learning; Throughput; Underwater acoustics; Underwater tracking; Working environment noise;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/72.317723
Filename :
317723
Link To Document :
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