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.
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