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http://dx.doi.org/10.6109/jkiice.2018.22.1.125

Sonar Target Classification using Generalized Discriminant Analysis  

Kim, Dong-wook (School of Electronics Engineering, Kyungpook National University)
Kim, Tae-hwan (Agency for Defense Development)
Seok, Jong-won (Department of Information and Communication, Changwon National University)
Bae, Keun-sung (School of Electronics Engineering, Kyungpook National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.22, no.1, 2018 , pp. 125-130 More about this Journal
Abstract
Linear discriminant analysis is a statistical analysis method that is generally used for dimensionality reduction of the feature vectors or for class classification. However, in the case of a data set that cannot be linearly separated, it is possible to make a linear separation by mapping a feature vector into a higher dimensional space using a nonlinear function. This method is called generalized discriminant analysis or kernel discriminant analysis. In this paper, we carried out target classification experiments with active sonar target signals available on the Internet using both liner discriminant and generalized discriminant analysis methods. Experimental results are analyzed and compared with discussions. For 104 test data, LDA method has shown correct recognition rate of 73.08%, however, GDA method achieved 95.19% that is also better than the conventional MLP or kernel-based SVM.
Keywords
Sonar target classification; linear discriminant analysis; kernel discriminant analysis;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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