Unmanned Aircraft System Airspace Structure and Safety Measures Based on Spatial Digital Twins

To explore the airspace structure and safety performance of unmanned aerial vehicle (UAV) system based on spatial digital twins (DTs), the study introduces DTs technology, and combines convolutional neural network (CNN) algorithm with UAV autonomous network. The DTs system of UAV is constructed by using wireless communication technology, and its security performance is simulated. The results show that in the analysis of the system packet loss rate, it is found that with the increase of the acquisition points, the amount of transmitted data only increases slightly, but the packet loss rate does not change significantly. In the analysis of the network performance of the unmanned aircraft system, it is found that the node energy-based weighted clustering algorithm (EWCA) can be used to increase the life of the overall network and enhance its availability by rationally controlling the number of nodes and the number of switching between clusters. As the number of nodes increases, the minimum survival time of each clustering algorithm decreases linearly. When the number of nodes is less than 600, the growth rate of cluster head is higher; When the number of nodes is more than 600, the curve growth is relatively smooth. In the analysis of the probability of network safety interruption, it is found that using the model constructed, when the energy acquisition coefficient is close to 0.5, the energy conversion efficiency is higher, the signal-to-noise ratio is larger. Also, when the number of intermediate nodes is increased to 10, the UAV has the best network safety performance. Therefore, through the research, it is found that the UAV DTs system constructed can significantly improve the safety performance of the UAV during its airspace flight. It can provide experimental references for the widespread application of the UAV in the later period.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2021.3108995
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Wang, Weixi
  • 0000-0002-9941-7556
  • Li, Xiaoming
  • 0000-0002-7804-368X
  • Xie, Linfu
  • 0000-0001-8545-6303
  • Lv, Haibin
  • 0000-0003-1059-4765
  • Lv, Zhihan
  • 0000-0003-2525-3074
• Publication Date: 2022-3

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 2809-2818
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 23
  • Issue Number: 3
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Airspace (Aeronautics); Aviation safety; Neural networks; Unmanned aircraft systems; Wireless communication systems
• Subject Areas: Aviation; Data and Information Technology; Operations and Traffic Management; Safety and Human Factors;

Filing Info

• Accession Number: 01847633
• Record Type: Publication
• Files: TRIS
• Created Date: May 31 2022 9:16AM