Open Access
Issue
JNWPU
Volume 39, Number 4, August 2021
Page(s) 761 - 769
DOI https://doi.org/10.1051/jnwpu/20213940761
Published online 23 September 2021
  1. Lyu Yang, Kang Tongna, Pan Quan, et al. UAV sense and avoidance: concepts, technologies, and systems[J]. Scientia Sinica Informationis, 2019, 49: 520–537 [Article] (in Chinese) [Google Scholar]
  2. Zhang Siyuan, Li Xianying, Shen Xiaoyun. ADS-B IN based conflict prediction and conflict-free trajectory planning for multi-aircraft[J]. Journal of System Simulation, 2019, 31(8): 1627–1635 [Article] (in Chinese) [Google Scholar]
  3. Arteaga R A, Cavalin M, Dandachy M, et al. Application of an ADS-B sense and avoid algorithm[C]//AIAA Flight Testing Conference, 2016 [Google Scholar]
  4. Li Zhuyuan. Study for autonomous obstacle-avoiding algorithm of civilian UAVs[D]. Guanghan: Civil Aviation Flight University of China, 2018 (in Chinese) [Google Scholar]
  5. Sahawneh L R, Duffield M O, Beard R W, et al. Detect and avoid for small unmanned aircraft systems using ADS-B[J]. Air Traffic Control Quarterly, 2015, 23(2/3): 203–240 [Google Scholar]
  6. Fasano G, Accado D, Moccia A, et al. Sense and avoid for unmanned aircraft systems[J]. IEEE Aerospace and Electronic Systems Magazine, 2016, 31(11): 82–110 [Article] [Google Scholar]
  7. Yu X, Zhang Y M. Sense and avoid technologies with applications to unmanned aircraft systems: review and prospects[J]. Progress in Aerospace Sciences, 2015, 74: 152–166 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  8. Ni Yude, Ma Yushen, Liu Ping. Aircraft conflict detection based on ADS-B[J]. Journal of Civil Aviation University of China, 2014, 32(5): 31–35, 44 [Article] (in Chinese) [Google Scholar]
  9. Jia Yuncong. Research on the UAV conflict avoidance method and hedging equipment[D]. Shijiazhuang: Hebei University of Science and Technology, 2018 (in Chinese) [Google Scholar]
  10. Zhang Zhongguang, Shi Hongwei. A conflict alert algorithm based on ADS-B information[J]. Computer and Modernization, 2015(11): 79–83 [Article] (in Chinese) [Google Scholar]
  11. Jie Dong. Research on technologies of UAV conflict detection and resolution strategy[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2019 (in Chinese) [Google Scholar]
  12. Zhang Songcan, Pu Jiexin, Si Yanna, et al. Survey on application of ant colony algorithm in path planning of mobile robot[J]. Computer Engineering and Applications, 2020, 56(8): 10–19 [Article] (in Chinese) [Google Scholar]
  13. Li Xianqiang, Ma Rong, Zhang Shen, et al. Improved design of ant colony algorithm and its application in path planning[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(suppl 2): 213–219 [Article] (in Chinese) [Google Scholar]
  14. Liu Yang, Zhang Weiguo, Li Guangwen, et al. Path Planning of UAV in dynamic environment[J]. Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(2): 252–256 [Article] (in Chinese) [Google Scholar]
  15. Zhang C, Hu C, Feng J, et al. A self-heuristic ant-based method for path planning of unmanned aerial vehicle in complex 3D space with dense u-type obstacles[J]. IEEE Access, 2019, 99: 1–1 [Google Scholar]
  16. Li Li, Li Hong, Shan Ningbo. Path planning based on improved ant colony algorithm with multiple inspired factor[J]. Computer Engineering and Applications, 2019, 55(5): 219–225, 250 [Article] (in Chinese) [Google Scholar]
  17. Ni Zhuang, Xiao Gang, Jing Zhongliang, et al. Path planning method for aircrafts conflict resolution based on improved ant colony algorithm[J]. Transducer and Microsystem Technologies, 2016, 35(4): 130–133 [Article] (in Chinese) [Google Scholar]
  18. Zhang Shuran. Research on UAV track planning based on swarm intelligence algorithm[D]. Chengdu: University of Electronic Science and Technology of China, 2020 (in Chinese) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.