Discrimination of bottom underwater mine-like objects in different conditions using a wideband data set

Marc Robinson; Mahmood R. Azimi-Sadjadi; Arta A. Jamshidi

doi:10.1117/12.479118

13 August 2002 Discrimination of bottom underwater mine-like objects in different conditions using a wideband data set

Marc Robinson, Mahmood R. Azimi-Sadjadi, Arta A. Jamshidi

Proceedings Volume 4742, Detection and Remediation Technologies for Mines and Minelike Targets VII; (2002) https://doi.org/10.1117/12.479118
Event: AeroSense 2002, 2002, Orlando, FL, United States

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.

Citation Download Citation

Marc Robinson, Mahmood R. Azimi-Sadjadi, and Arta A. Jamshidi "Discrimination of bottom underwater mine-like objects in different conditions using a wideband data set", Proc. SPIE 4742, Detection and Remediation Technologies for Mines and Minelike Targets VII, (13 August 2002); https://doi.org/10.1117/12.479118

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