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J. KIMS Technol > Volume 20(1); 2017 > Article
Journal of the Korea Institute of Military Science and Technology 2017;20(1):33-39.
DOI: https://doi.org/10.9766/KIMST.2017.20.1.033   
Active Sonar Classification Algorithm based on HOG Feature
Hyunhak Shin, Jaihyun Park, Bonhwa Ku, Iksu Seo, Taehwan Kim, Junseok Lim, Hanseok Ko, Wooyoung Hong
1Department of Electrical Engineering, Korea University
2Sonar Systems PMO, Agency for Defense Development
3Department of Defense Systems Engineering, Sejong University
HOG 특징 기반 능동 소나 식별 기법
신현학, 박재현, 구본화, 서익수, 김태환, 임준석, 고한석, 홍우영
1고려대학교 전기전자전파공학과
2국방과학연구소 소나체계개발단
3세종대학교 국방시스템공학과
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.
Key Words: Active Sonar Classification, Histogram of Gradient feature


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