DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Park, Seong-Ook | - |
dc.contributor.advisor | 박성욱 | - |
dc.contributor.author | Kang, Hyunseong | - |
dc.date.accessioned | 2022-04-21T19:33:53Z | - |
dc.date.available | 2022-04-21T19:33:53Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=956634&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/295640 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 전기및전자공학부, 2021.2,[v, 68 p. :] | - |
dc.description.abstract | With the recent rapid development of the drone industry, positive aspects and side effects are occurring. In particular, with the commercialization of personal drones, technological advances are being made for the regulation of drones, and as one of them, drone detection technology through radar is being studied. The distinction between the two is important because drones have similar radar reflection areas to birds. The need to further expand and identify information such as the type and angle of drones is also emerging. Compared to birds, the biggest feature of the drone is that it transmits and receives radio waves for control, and the other is that the propeller works finely and quickly. In this paper, it was possible to check the existence of a drone by detecting a leak signal from a GPS system (GPS) generated from a drone, and the type of drone was identified using a convolutional neural network by extracting a fine Doppler signal from the received signal. The leakage wave of GPS generates a lot of noise due to its low power, and the identification rate of drones is improved by removing the noise using the Bivariate Empirical Mode Decomposition (BEMD) algorithm. In addition, the elevation angle of a single drone was identified. The drone's propeller was an experiment based on research showing that different micro-Dopplers occur depending on the angle. The drone's elevation angle was measured at 10-degree intervals and applied to Visual Geometry Group-11 (VGG11), one of the types of convolutional neural networks, to record an average identification rate of 84.7%. Additionally, focusing on the fact that the characteristics of the fine Doppler generated from the propeller of a drone change according to the polarization, which is physically independent information, four pieces of information were simultaneously received through a receiver having four different polarizations. The received four polarization information was combined with each other and applied to the same model, VGG11, recording a high identification rate of 97.9%. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Drone Detection▼aClassification▼aConvolutional Neural Network▼aMicro-Doppler▼aGPS leakage receiver▼aFour channel receiver▼aFrequency Modulated Continuous Wave Radar▼aMulti-polarization | - |
dc.subject | 드론 탐지▼a식별▼a합성곱 신경망▼a미세 도플러▼aGPS 누설파 수신기▼a4채널 수신기▼a주파수 변조 연속파 레이더▼a다중 편파 | - |
dc.title | Drone classification and improvement of elevation angle classification rate of drone using multi-polarization characteristics using convolutional neural network | - |
dc.title.alternative | 합성곱 신경망을 이용한 드론 식별 및 다중 편파 특성을 이용한 드론의 고도각 식별률 향상 연구 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | 강현성 | - |
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