Journal of the Korea Institute of Military Science and Technology (한국군사과학기술학회지)

The Korea Institute of Military Science and Technology (한국군사과학기술학회)
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Active Sonar Classification Algorithm based on HOG Feature

HOG 특징 기반 능동 소나 식별 기법

  • 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)
  • 신현학 (고려대학교 전기전자전파공학과) ;
  • 박재현 (고려대학교 전기전자전파공학과) ;
  • 구본화 (고려대학교 전기전자전파공학과) ;
  • 서익수 (국방과학연구소 소나체계개발단) ;
  • 김태환 (국방과학연구소 소나체계개발단) ;
  • 임준석 (세종대학교 국방시스템공학과) ;
  • 고한석 (고려대학교 전기전자전파공학과) ;
  • 홍우영 (세종대학교 국방시스템공학과)
  • Received : 2016.06.27
  • Accepted : 2016.12.23
  • Published : 2017.02.05
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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

References

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