Discrimination Of Cylinders With Different Wall Thicknesses Using Neural Networks And Simulated Dolphin Sonar Signals

. This paper describes a method integrating neural networks into a system for recognizing underwater objects. The system is based on a combination of simulated dolphin sonar signals, simulated auditory lters and articial neural networks. The system is tested on a cylinder wall thickness dierence exp...

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Bibliographic Details
Main Authors: Lars Nonboe Andersen, Whitlow Au, Jan Larsen, Lars Kai Hansen
Other Authors: The Pennsylvania State University CiteSeerX Archives
Format: Text
Language:English
Published: 1999
Subjects:
Killer Whale
Online Access:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.20.9994
http://eivind.imm.dtu.dk/publications/1999/nonboe.nnsp99.ps.gz
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Summary:. This paper describes a method integrating neural networks into a system for recognizing underwater objects. The system is based on a combination of simulated dolphin sonar signals, simulated auditory lters and articial neural networks. The system is tested on a cylinder wall thickness dierence experiment and demonstrates high accuracy for small wall thickness dierences. Results from the experiment are compared with results obtained by a false killer whale (pseudorca crassidens).

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