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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Drone Detection with Chirp-Pulse Radar Based on Target Fluctuation Models

  • Kim, Byung-Kwan (Microwave and Antenna Laboratory, School of Electrical Engineering, Korea Advanced Institute of Science and Technology) ;
  • Park, Junhyeong (Microwave and Antenna Laboratory, School of Electrical Engineering, Korea Advanced Institute of Science and Technology) ;
  • Park, Seong-Jin (Microwave and Antenna Laboratory, School of Electrical Engineering, Korea Advanced Institute of Science and Technology) ;
  • Kim, Tae-Wan (Microwave and Antenna Laboratory, School of Electrical Engineering, Korea Advanced Institute of Science and Technology) ;
  • Jung, Dae-Hwan (Microwave and Antenna Laboratory, School of Electrical Engineering, Korea Advanced Institute of Science and Technology) ;
  • Kim, Do-Hoon (Microwave and Antenna Laboratory, School of Electrical Engineering, Korea Advanced Institute of Science and Technology) ;
  • Kim, Taihyung (System Technology & Control) ;
  • Park, Seong-Ook (Microwave and Antenna Laboratory, School of Electrical Engineering, Korea Advanced Institute of Science and Technology)
  • Received : 2017.03.26
  • Accepted : 2017.11.30
  • Published : 2018.04.01
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Abstract

This paper presents a pulse radar system to detect drones based on a target fluctuation model, specifically the Swerling target model. Because drones are small atypical objects and are mainly composed of non-conducting materials, their radar cross-section value is low and fluctuating. Therefore, determining the target fluctuation model and applying a proper integration method are important. The proposed system is herein experimentally verified and the results are discussed. A prototype design of the pulse radar system is based on radar equations. It adopts three different pulse modes and a coherent pulse integration to ensure a high signal-to-noise ratio. Outdoor measurements are performed with a prototype radar system to detect Doppler frequencies from both the drone frame and blades. The results indicate that the drone frame and blades are detected within an instrumental maximum range. Additionally, the results show that the drone's frame and blades are close to the Swerling 3 and 4 target models, respectively. By the analysis of the Swerling target models, proper integration methods for detecting drones are verified and can thus contribute to increasing in detectability.

Keywords

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