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 filters and artificial neural networks. The system is tested on a cylinder wall thickness difference...

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Bibliographic Details
Published in:Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468)
Main Authors: Andersen, Lars Nonboe, Au, Whitlow, Larsen, Jan, Hansen, Lars Kai
Format: Other Non-Article Part of Journal/Newspaper
Language:English
Published: IEEE 1999
Subjects:
Killer Whale
Online Access:https://orbit.dtu.dk/en/publications/0d379e1a-4e3d-4d0d-bfcc-2b797d4b6e72
https://doi.org/10.1109/NNSP.1999.788167
https://backend.orbit.dtu.dk/ws/files/5381051/Andersen.pdf
Description
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 filters and artificial neural networks. The system is tested on a cylinder wall thickness difference experiment and demonstrates high accuracy for small wall thickness differences. Results from the experiment are compared with results obtained by a false killer whale (pseudorca crassidens).

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