한국항행학회논문지 (Journal of Advanced Navigation Technology)

  • 제28권5호
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  • Pages.601-609
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  • 2024
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  • 1226-9026(pISSN)
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  • 2288-842X(eISSN)
한국항행학회 (The Korean Navigation Institute)
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드론 검출을 위한 레이다 신호처리 시스템 설계

Design of Radar Signal Processing System for Drone Detection

  • 김홍석 (한국항공대학교 항공전자정보공학부) ;
  • 반규리 (한국항공대학교 항공전자정보공학부) ;
  • 서지훈 (한국항공대학교 항공전자정보공학부) ;
  • 정윤호 (한국항공대학교 항공전자정보공학부)
  • Hong-suk Kim (School of Electronics and Information Engineering, Korea Aerospace University) ;
  • Gyu-ri Ban (School of Electronics and Information Engineering, Korea Aerospace University) ;
  • Ji-hun Seo (School of Electronics and Information Engineering, Korea Aerospace University) ;
  • Yunho Jung (School of Electronics and Information Engineering, Korea Aerospace University)
  • 투고 : 2024.09.24
  • 심사 : 2024.10.29
  • 발행 : 2024.10.31
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초록

본 논문에서는 FMCW (frequency modulated continuous wave) 레이다 센서를 활용하여 드론 검출이 가능한 시스템 설계 및 구현 결과를 제시한다. 드론 검출 시스템의 구현을 위해, 레이다 센서로부터 입력된 신호를 FFT, CFAR, clustering, tracking으로 이어지는 총 4단계의 신호처리 과정을 통해 객체를 탐지하고, 해당 객체를 드론과 다른 사물로 분류하기 위한 딥러닝 추론 과정을 수행한다. 딥러닝의 높은 연산량과 많은 메모리 요구를 감소시키기 위해 CNN (convolution neural network) 연산을 이진화하여 수행하는 BNN (binary neural network) 구조를 적용하였다. 성능 평가 및 검증 결과 89.33%의 객체 구분 정확도를 확인할 수 있었고, 총 수행 시간은 4 ms로 실시간 동작이 가능함을 확인하였다.

In this paper, we present the design and implementation results of a system that classifies drones from other objects using an FMCW (frequency-modulated continuous wave) radar sensor. The proposed system detects various objects through a four-stage signal processing procedure, consisting of FFT, CFAR, clustering, and tracking, using signals received from the radar sensor. Subsequently, a deep learning process is conducted to classify the detected objects as either drones or other objects. To mitigate the high computational demands and extensive memory requirements of deep learning, a BNN (binary neural network) structure was applied, binarizing the CNN (convolutional neural network) operations. The performance evaluation and verification results demonstrated a drone classification accuracy of 89.33%, with a total execution time of 4 ms, confirming the feasibility of real-time operation.

키워드

과제정보

본 연구는 2024년도 정부 (과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행되었으며 (No. 2017-0-00528), CAD tool은 IDEC에 의해 지원되었음.

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