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

The Korea Institute of Military Science and Technology (한국군사과학기술학회)
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Analysis on the Distribution of RF Threats Using Unsupervised Learning Techniques

비지도 학습 기법을 사용한 RF 위협의 분포 분석

  • Kim, Chulpyo (School of Computer Science and Information Engineering, The Catholic University of Korea) ;
  • Noh, Sanguk (School of Computer Science and Information Engineering, The Catholic University of Korea) ;
  • Park, So Ryoung (School of Information Communication and Electronic Engineering, The Catholic University of Korea)
  • 김철표 (가톨릭대학교 컴퓨터정보공학부) ;
  • 노상욱 (가톨릭대학교 컴퓨터정보공학부) ;
  • 박소령 (가톨릭대학교 정보통신전자공학부)
  • Received : 2015.09.16
  • Accepted : 2016.05.20
  • Published : 2016.06.05
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Abstract

In this paper, we propose a method to analyze the clusters of RF threats emitting electrical signals based on collected signal variables in integrated electronic warfare environments. We first analyze the signal variables collected by an electronic warfare receiver, and construct a model based on variables showing the properties of threats. To visualize the distribution of RF threats and reversely identify them, we use k-means clustering algorithm and self-organizing map (SOM) algorithm, which are belonging to unsupervised learning techniques. Through the resulting model compiled by k-means clustering and SOM algorithms, the RF threats can be classified into one of the distribution of RF threats. In an experiment, we measure the accuracy of classification results using the algorithms, and verify the resulting model that could be used to visually recognize the distribution of RF threats.

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References

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