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http://dx.doi.org/10.9766/KIMST.2017.20.1.033

Active Sonar Classification Algorithm based on HOG Feature  

Shin, Hyunhak (Department of Electrical Engineering, Korea University)
Park, Jaihyun (Department of Electrical Engineering, Korea University)
Ku, Bonhwa (Department of Electrical Engineering, Korea University)
Seo, Iksu (Sonar Systems PMO, Agency for Defense Development)
Kim, Taehwan (Sonar Systems PMO, Agency for Defense Development)
Lim, Junseok (Department of Defense Systems Engineering, Sejong University)
Ko, Hanseok (Department of Electrical Engineering, Korea University)
Hong, Wooyoung (Department of Defense Systems Engineering, Sejong University)
Publication Information
Journal of the Korea Institute of Military Science and Technology / v.20, no.1, 2017 , pp. 33-39 More about this Journal
Abstract
In this paper, an effective feature which is capable of classifying targets among the detections obtained from 2D range-bearing maps generated in active sonar environments is proposed. Most conventional approaches for target classification with the 2D maps have considered magnitude of peak and statistical features of the area surrounding the peak. To improve the classification performance, HOG(Histogram of Gradient) feature, which is popular for their robustness in the image textures analysis is applied. In order to classify the target signal, SVM(Support Vector Machine) method with reduced HOG feature by the PCA(Principal Component Analysis) algorithm is incorporated. The various simulations are conducted with the real clutter signal data and the synthesized target signal data. According to the simulated results, the proposed method considering HOG feature is claimed to be effective when classifying the active sonar target compared to the conventional methods.
Keywords
Active Sonar Classification; Histogram of Gradient feature;
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Times Cited By KSCI : 2  (Citation Analysis)
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