Underwater target classification in changing environments using an adaptive feature mapping Azimi-Sadjadi, Mahmood R. ; Yao, De ; Jamshidi, Arta A. ; Dobeck, Gerald J. "This work was supported by the Office of Naval Research, Bisonar Program under Contracts N00014-99-1-0166 and N00014-01-1-0307." A new adaptive underwater target classification system to cope with environmental changes in acoustic backscattered 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 K-nearest neighbor (K-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 when the signal-to-reverberation ratio (SRR) in the environment is varying. Colorado State University. Libraries 2002 text ; image application/pdf ECEmra00012.pdf FACFECEN100492ARTI eng c2002 IEEE
Underwater target classification in changing environments using an adaptive feature mapping
Azimi-Sadjadi, Mahmood R. ; Yao, De ; Jamshidi, Arta A. ; Dobeck, Gerald J.
"This work was supported by the Office of Naval Research, Bisonar Program under Contracts N00014-99-1-0166 and N00014-01-1-0307."
A new adaptive underwater target classification system to cope with environmental changes in acoustic backscattered 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 K-nearest neighbor (K-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 when the signal-to-reverberation ratio (SRR) in the environment is varying.
Colorado State University. Libraries
2002
text ; image
application/pdf
ECEmra00012.pdf
FACFECEN100492ARTI
eng
c2002 IEEE