Design and implementation of intelligent UAV intrusion detection, tracking and interception system

被引:0
|
作者
Fan K. [1 ,2 ]
Lei S. [1 ,2 ]
Bie T. [1 ,2 ]
机构
[1] School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou
[2] Key Laboratory of Magnetic Levitation Technology in Jiangxi Province, Ganzhou
关键词
anti-UAV; artificial intelligence; automatic interception; image recognition; motion detection;
D O I
10.3788/IRLA20210750
中图分类号
学科分类号
摘要
In recent years, the field of civilian unmanned aerial vehicles has developed rapidly, leading to the frequent occurrence of unmanned aerial vehicle "black flying" incidents, which has brought considerable challenges to national security and social stability, and there is an urgent need to develop anti-UAV technology. In this regard, this paper proposes a follow-type directional jamming method and designs a vision-based UAV intrusion detection and automatic tracking and interception system. The HOG+nonlinear SVM scheme is used to identify the UAV, the ViBe moving target detection algorithm is added to improve the recognition speed, and UAV target tracking is realized through the KCF algorithm. Design and manufacture the hardware equipment of the UAV interception system, mainly including the tracking servo system, base and tray. Experiments show that the recognition accuracy of the system reaches 90.54%, the recognition speed is 20.56 fps, the interception platform can achieve the aim of the target UAV within 0.5 s, and the tracking effect is good. The system is tested on the built physical platform, and the results show that the system can realize the movement detection, recognition, tracking and interference of invading UAVs. The recognition accuracy is high, the real-time performance is good, and the system can automatically intercept the invading UAVs. © 2022 Chinese Society of Astronautics. All rights reserved.
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  • [1] Zhan Guoxiang, Kang Lifang, Helin Duanmu, The testing and analysis on flight prevention effect of rice pests and diseases of Jifei P20 UAV, Agricultural Equipment & Technology, 46, 1, pp. 18-19, (2020)
  • [2] Xu Jing, Zhang Zhiqiang, Ma Fengyi, Device automatic unloading device design for drone-based intelligent logistics systems, Electronic Measurement Technology, 44, 7, pp. 128-132, (2021)
  • [3] Zhang Xiang, Analysis of China's UAV countermeasures technology and industry development [J], China New Telecommunication, 22, 17, pp. 83-84, (2020)
  • [4] Zhu Mengzhen, Chen Xia, Liu Xu, Et al., Situation and key technology of tactical laser anti-UAV, Infrard and Laser Engineering, 50, 7, (2021)
  • [5] Shi X, Yang C, Xie W., Anti-drone system with multiple surveillance technologies: Architecture, implementation and challenges, IEEE Communications Magazine, 56, 4, pp. 68-74, (2018)
  • [6] Farlik J, Kratky M, Casar J., Radar cross section and detection of small unmanned aerial vehicles, International Conference on Mechatronics-mechatronika, pp. 1-7, (2017)
  • [7] Hoffmann F, Ritchie M, Fioranelli F, Et al., Micro-Doppler based detection and tracking of UAVs with multistatic radar [C], 2016 IEEE Radar Conference, pp. 1-6, (2016)
  • [8] Yang Donghai, Research and implementation of four rotor drone detection technology based on sound recognition, (2017)
  • [9] Mezei J, Molnar A., Drone sound detection by correlation [C], 2016 IEEE 11th International Symposium on Applied Computational Intelligence and Informatics (SACI), (2016)
  • [10] Azari M M, Sallouha H, Chiumento A., Key technologies and system trade-offs for detection and localization of amateur drones, IEEE Communications Magazine, 56, 1, pp. 51-57, (2018)