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

The Korea Institute of Military Science and Technology (한국군사과학기술학회)
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Repeated K-means Clustering Algorithm For Radar Sorting

레이더 군집화를 위한 반복 K-means 클러스터링 알고리즘

  • Dong Hyun ParK (School of Electrical Engineering, Korea Advanced Institute of Science and Technology) ;
  • Dong-ho Seo (Electronic Warfare R&D, LIG NEX1 Co., Ltd.) ;
  • Jee-hyeon Baek (Electronic Warfare R&D, LIG NEX1 Co., Ltd.) ;
  • Won-jin Lee (Electronic Warfare R&D, LIG NEX1 Co., Ltd.) ;
  • Dong Eui Chang (School of Electrical Engineering, Korea Advanced Institute of Science and Technology)
  • 박동현 (한국과학기술원 전기 및 전자공학부) ;
  • 서동호 (LIG넥스원(주) 전자전연구소) ;
  • 백지현 (LIG넥스원(주) 전자전연구소) ;
  • 이원진 (LIG넥스원(주) 전자전연구소) ;
  • 장동의 (한국과학기술원 전기 및 전자공학부)
  • Received : 2023.05.09
  • Accepted : 2023.11.22
  • Published : 2023.12.05
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Abstract

In modern electronic warfare, a number of radar emitters are in operation, causing radar receivers to receive high-density signal pulses that occur simultaneously. To analyze the radar signals more accurately and identify enemies, the sorting process of high-density radar signals is very important before analysis. Recently, machine learning algorithms, specifically K-means clustering, are the subject of research aimed at improving the accuracy of radar signal sorting. One of the challenges faced by these studies is that the clustering results can vary depending on how the initial points are selected and how many clusters number are set. This paper introduces a repeated K-means clustering algorithm that aims to accurately cluster all data by identifying and addressing false clusters in the radar sorting problem. To verify the performance of the proposed algorithm, experiments are conducted by applying it to simulated signals that are generated by a signal generator.

Keywords

Acknowledgement

이 연구는 LIG NEX1 산학협력과제(G01220631) 지원으로 연구되었음.

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