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Proceedings Paper

Discrimination of bottom underwater mine-like objects in different conditions using a wideband data set
Author(s): Marc Robinson; Mahmood R. Azimi-Sadjadi; Arta A. Jamshidi
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Paper Abstract

The problem of classification of underwater targets involves discrimination between mine-like and non-mine-like objects as well as the characterization of background clutter. In this work this problem is addressed using a newly collected wideband data set. The developed system consists of a pre- processing scheme, which includes removing multi-path effects and other artifacts from the acquired data. Features are then extracted and fed to a back-propagation neural network (BPNN). Test results will be given for the classification between various types of mine-like and non- mine-like objects and for different bottom conditions and depression/elevation angles of the sonar to test the robustness and generalization of the classification scheme.

Paper Details

Date Published: 13 August 2002
PDF: 12 pages
Proc. SPIE 4742, Detection and Remediation Technologies for Mines and Minelike Targets VII, (13 August 2002); doi: 10.1117/12.479118
Show Author Affiliations
Marc Robinson, Colorado State Univ. (United States)
Mahmood R. Azimi-Sadjadi, Colorado State Univ. (United States)
Arta A. Jamshidi, Colorado State Univ. (United States)

Published in SPIE Proceedings Vol. 4742:
Detection and Remediation Technologies for Mines and Minelike Targets VII
J. Thomas Broach; Russell S Harmon; Gerald J. Dobeck, Editor(s)

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