FANET management process simulation at the deployment and operation stage

Authors

DOI:

https://doi.org/10.15587/2706-5448.2023.290033

Keywords:

ground-to-air communication network, FANET, objective functions, deployment phase, operational management, prediction, dynamic topology

Abstract

The object of the study is the process of managing the air network of air communication platforms of the FANET class (Flying Ad-Hoc Network), which is a component of the ground-air communication network, and which is performed on rotary unmanned aerial vehicles (UAVs) of the mini class, at the stage of deployment and operational management. The scientific research is aimed at the managing process formalization of aerial communication platforms of the air communication network in the implementation of two classes of management tasks – the class of traffic management tasks and the class of communication tasks. The analysis of this subject area showed that the management tasks at the stage of deployment and operational management of the air subnet are a multi-parameter optimization task and require the formation of control solutions at the OSI physical, channel and network levels, open systems interaction model. Tasks related to the adaptive management of radio coverage in zones (geographic areas of the area), including the clustering of terrestrial subscribers (communication nodes), were not considered, and relate to processes at the transport and application levels. At the same time, the article shows the mathematical apparatus of the approach to the compensation of the deviations of the trajectory of an unmanned aerial vehicle (UAV) in the conditions of a directional obstacle, which will allow the formation of control solutions for adaptive control, directional patterns at the output of the transmission path. Such compensation is carried out using methods of algorithmic exchange of probes (messages) between the mobile base station and communication platforms with a certain periodicity – solutions at the channel and network levels, as well as the use of Multi User MIMO technologies. This technology allows for information exchange with several client devices at the same time, and not sequentially, sending probes to several spacecraft on one channel, using several transmitting and receiving antennas, and the calculation of channel coefficients allows to estimate the azimuthal angle of deviation and the angle of elevation.

Author Biographies

Robert Bieliakov, Military Institute of Telecommunications and Information Technologies named after Heroes of Kruty

PhD, Associate Professor

Scientific-Organizational Department

Oleksii Fesenko, Military Institute of Telecommunications and Information Technologies named after Heroes of Kruty

Lecturer

Department of Technical and Metrological Support

References

  1. Dakov, S., Dakova, L. (2019). Improving the reliability of software-defined network. Information Systems and Technologies Security, 1 (1), 66–78. doi: https://doi.org/10.17721/ists.2019.1.66-78
  2. Thamizhmaran, K. (2020). Performance of Mobile Ad-Hoc Network. Journal of VLSI Design and Signal Processing, 6 (2), 22–25. doi: https://doi.org/10.46610/jovdsp.2020.v06i02.005
  3. Bieliakov, R., Fesenko, O. (2023). Mobility model of a special purpose terrestrial communication network. Computer-integrated technologies: education, science, production, 51, 130–138. doi: https://doi.org/10.36910/6775-2524-0560-2023-51-17
  4. Romaniuk, V. A., Bieliakov, R. О. (2023). Objective control functions of FANET communication nodes of land-air network. Computer-integrated technologies: education, science, production, 50, 125–130. doi: https://doi.org/10.36910/6775-2524-0560-2023-50-19
  5. Menna, B., Villar, S., Acosta, G. (2019). Navigation System for MACÁBOT an Autonomous Surface Vehicles Using GPS Aided Strapdown Inertial Navigation System. IEEE Latin America Transactions, 17 (6), 1009–1019. doi: https://doi.org/10.1109/tla.2019.8896824
  6. Munguía, R. (2014). A GPS-aided inertial navigation system in direct configuration. Journal of Applied Research and Technology, 12 (4), 803–814. doi: https://doi.org/10.1016/s1665-6423(14)70096-3
  7. Bieliakov, R. O., Radzivilov, H. D., Fesenko, O. D., Vasylchenko, V. V., Tsaturian, O. G., Shyshatskyi, A. V., Romanenko, V. P. (2019). Method of the intelligent system construction of automatic control of unmanned aircraft apparatus. Radio Electronics, Computer Science, Control, 1. doi: https://doi.org/10.15588/1607-3274-2019-1-20
  8. Delamer, J.-A., Watanabe, Y., Chanel, C. P. C. (2021). Safe path planning for UAV urban operation under GNSS signal occlusion risk. Robotics and Autonomous Systems, 142, 103800. doi: https://doi.org/10.1016/j.robot.2021.103800
  9. Li, Q., Li, K., Liang, W. (2023). A zero-velocity update method based on neural network and Kalman filter for vehicle-mounted inertial navigation system. Measurement Science and Technology, 34 (4), 045110. doi: https://doi.org/10.1088/1361-6501/acabde
  10. Bi, S., Ma, L., Shen, T., Xu, Y., Li, F. (2021). Neural network assisted Kalman filter for INS/UWB integrated seamless quadrotor localization. PeerJ Computer Science, 7, e630. doi: https://doi.org/10.7717/peerj-cs.630
  11. Park, K.-S., Kim, S.-H., Guerra Padilla, G. E., Kim, K.-J., Yu, K.-H. (2018). Operational Performance Evaluation of Remote Controllers for Manual Control of UAV. Journal of Institute of Control, Robotics and Systems, 24 (4), 315–320. doi: https://doi.org/10.5302/j.icros.2018.18.8001
  12. Nguyen, M. D., Le, L. B., Girard, A. (2022). Integrated UAV Trajectory Control and Resource Allocation for UAV-Based Wireless Networks With Co-Channel Interference Management. IEEE Internet of Things Journal, 9 (14), 12754–12769. doi: https://doi.org/10.1109/jiot.2021.3138374
  13. Khan, M. A., Qureshi, I. M., Khan, I. U., Nasim, A., Javed, U., Khan, W. (2018). On the Performance of Flying Ad-hoc Networks (FANETs) with Directional Antennas. 2018 5th International Multi-Topic ICT Conference (IMTIC). doi: https://doi.org/10.1109/imtic.2018.8467274
  14. Zhang, L., Tao, C., Yang, G. (2011). Wireless Positioning: Fundamentals, Systems and State of the Art Signal Processing Techniques. Cellular Networks – Positioning, Performance Analysis, Reliability. doi: https://doi.org/10.5772/14841
  15. Fakharian, A., Gustafsson, T., Mehrfam, M. (2011). Adaptive Kalman filtering based navigation: An IMU/GPS integration approach. 2011 International Conference on Networking, Sensing and Control. doi: https://doi.org/10.1109/icnsc.2011.5874871
  16. Li, J., Zhang, X. (2014). Two-dimensional angle estimation for monostatic MIMO arbitrary array with velocity receive sensors and unknown locations. Digital Signal Processing, 24, 34–41. doi: https://doi.org/10.1016/j.dsp.2013.08.005
Fanet management process simulation at the deployment and operation stage

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Published

2023-10-30

How to Cite

Bieliakov, R., & Fesenko, O. (2023). FANET management process simulation at the deployment and operation stage. Technology Audit and Production Reserves, 5(2(73), 40–47. https://doi.org/10.15587/2706-5448.2023.290033

Issue

Vol. 5 No. 2(73) (2023): Information and control systems

Section

Systems and Control Processes

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Copyright (c) 2023 Robert Bieliakov, Oleksii Fesenko

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