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Summary: IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 13, NO. 5, SEPTEMBER 2002 1099
Underwater Target Classification in Changing
Environments Using an Adaptive Feature Mapping
Mahmood R. Azimi-Sadjadi, Senior Member, IEEE, De Yao, Arta A. Jamshidi, and Gerry J. Dobeck
Abstract--A new adaptive underwater target classification
system to cope with environmental changes in acoustic backscat-
tered data from targets and nontargets is introduced in this paper.
The core of the system is the adaptive feature mapping that
minimizes the classification error rate of the classifier. The goal is
to map the feature vector in such a way that the mapped version
remains invariant to the environmental changes. A -nearest
neighbor ( -NN) system is used as a memory to provide the
closest matches of an unknown pattern in the feature space.
The classification decision is done by a backpropagation neural
network (BPNN). Two different cost functions for adaptation are
defined. These two cost functions are then combined together
to improve the classification performance. The test results on a
40-kHz linear FM acoustic backscattered data set collected from
six different objects are presented. These results demonstrate the
effectiveness of the adaptive system versus nonadaptive system
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