A.I. Sulimov

Kazan Federal University, Kazan, 420008 Russia

E-mail: amir.sulimov@kpfu.ru

Received August 09, 2021


ORIGINAL ARTICLE

Full text PDF

DOI: 10.26907/2541-​7746.2021.3-4.231-249

For citation: Sulimov A.I. Optimization of periodic synchronization of UAV's clock by differential phase method. Uchenye Zapiski Kazanskogo Universiteta. Seriya Fiziko-Matematicheskie Nauki, 2021, vol. 163, no. 3–4, pp. 231–249. doi: 10.26907/2541-7746.2021.3-4.231-249. (In Russian)

Abstract

The article considers the problem of designing a synchronized aerial wireless sensor network of unmanned aerial vehicles (UAVs) with centralized control by a master unit. A 10-ns synchronization precision must be ensured for all units within the aerial network in the time intervals of at least 100 s. To achieve it, the master unit periodically generates a synchronizing signal with two coherent sine tones of different frequencies that helps the slave UAVs to adjust their clocks. High-stability oven-controlled crystal quartz oscillators (OCXO) are used as onboard clocks for the UAVs.

The study aims to assess the optimal frequency separation of the coherent synchronizing tones that provides the best possible noise immunity of the measured data with a reliable ambiguity resolution of the carrier phase. The problem is solved using a computer simulation of periodic synchronization of the slave UAVs by differential phase measurements associated with the reference time scale of the master UAV in order to suppress possible random clock offsets.

According to the simulation results, aside from the positioning errors of the UAVs, the systematic Doppler phase shift of the synchronizing signal in the propagation channel is the main obstacle to differential phase synchronization. Depending on the efficiency of the Doppler phase shift compensation, the optimum frequency separation of the synchronizing tones ranges from 10 to 1500 MHz with the correspondent synchronization precision achieved from 0.25 to

2.65 ns at the observation time of up to 100 s. It is shown that the effective compensation for the Doppler shift requires periodic channel estimation for at least every 10 ms. For most practical applications, however, adjusting the slave clock every 5 s using two coherent synchronizing sine tones separated by 400–500 MHz results in a satisfactory quality of synchronization.

Keywords: unmanned aerial vehicles, aerial wireless sensor network, time synchronization, frequency stability, quartz oscillator, time scale

Acknowledgments. This work was funded by the Ministry of Science and Higher Education of the Russian Federation (agreement no. 075-11-2019-038 of November 26, 2019 “Development of a multifunctional hardware and software complex based on unmanned aerial vehicles for planning and support of seismic exploration”).

References

  1. Sulimov A.I., Sherstyukov O.N., Latypov R.R., Nurgaliev D.K. Simulation of periodic synchronization of UAV's clock. Proc. Conf.: 2021 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO-2021), 2021, art. 9488361, pp. 1–8. doi: 10.1109/SYNCHROINFO51390.2021.9488361.
  2. Villas L.A., Boukerche A., Guidoni D.L., Maia G., Loureiro A.A.F. A joint 3D localization and synchronization solution for Wireless Sensor Networks using UAV. Proc. 38th Annu. IEEE Conf. on Local Computer Networks, 2013, pp. 719–722. doi: 10.1109/LCN.2013.6761319.
  3. Yanmaz E., Kuschnig R., Bettstetter C. Achieving air-ground communications in 802.11 networks with three-dimensional aerial mobility. 2013 Proc. IEEE INFOCOM, 2013, pp. 120–124. doi: 10.1109/INFCOM.2013.6566747.
  4. Kang J.-H., Park K.-J., Kim H. Analysis of localization for drone-fleet. Proc. 2015 Int. Conf. on Information and Communication Technology Convergence (ICTC), 2015, pp. 533–538. doi: 10.1109/ICTC.2015.7354604.
  5. Liu T., Hu Y., Hua Y., Jiang H. Study on autonomous and distributed time synchronization method for formation UAVs. Proc. 2015 Joint Conf. of the IEEE International Frequency Control Symposium & the European Frequency and Time Forum, 2015, pp. 293– 296. doi: 10.1109/FCS.2015.7138844.
  6. Seijo O´ ., Val I., L´opez-Fern´andez J.A. Portable full channel sounder for industrial wireless applications with mobility by using sub-nanosecond wireless time synchronization. IEEE Access, 2020, vol. 8, pp. 175576–175588. doi: 10.1109/ACCESS.2020.3025896.
  7. Tiemann J., Wietfeld C. Scalable and precise multi-UAV indoor navigation using TDOA-based UWB localization. Proc. 2017 Int. Conf. on Indoor Positioning and Indoor Navigation (IPIN), 2017, pp. 1–7. doi: 10.1109/IPIN.2017.8115937.
  8. Calero D., Fernandez E. Characterization of chip-scale atomic clock for GNSS navigation solutions. Proc. 2015 International Association of Institutes of Navigation World Congr. (IAIN), 2015, pp. 1–8. doi: 10.1109/IAIN.2015.7352264.
  9. Baojian C., Ying C., Dehai Z., Haiying Z. Study on high stability frequency equipment based on double disciplined loops. Proc. 11th IEEE Int. Conf. on Electronic Measurement & Instruments (ICEMI'2013), 2013, pp. 331–335. doi: 10.1109/ICEMI.2013.6743061.
  10. Bagala T., Fibich A., Kubinec P., Stofanik V. Improvement of short-term frequency stability of the Chip Scale Atomic Clock. Proc. 2016 IEEE Int. Frequency Control Symp. (IFCS), 2016, pp. 1–4. doi: 10.1109/FCS.2016.7546746.
  11. Fundamentals of Quartz Oscillators. Application Note 200-2. Hewlett Packard Co., 1997. 28 p.
  12. Allan D.W. Time and frequency (time-domain) characterization, estimation, and prediction of precision clocks and oscillators. IEEE Trans. Ultrason., Ferroelectr., Freq. Control, 1987, vol. 34, no. 6, pp. 647–654. doi: 10.1109/T-UFFC.1987.26997.
  13. Barnes J.A. Simulation of oscillator noise. Proc. 38th Annu. Symp. on Frequency Control, 1984, pp. 319–326. doi: 10.1109/FREQ.1984.200775.
  14. Sherstyukov O.N., Sulimov A.I., Latypov R.R., Nurgaliev D.K., Smolyakov A.D. Simulation of short-term instability of UAV's clock. Proc. Conf.: 2021 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO-2021), 2021, art. 9488167, pp. 1–8. doi: 10.1109/SYNCHROINFO51390.2021.9488167.
  15. Kay S.M. Fundamentals of Statistical Signal Processing. Vol. 1: Estimation theory. Englewood Cliffs, N. J., Prentice-Hall PTR, 1993. 608 p.
  16. Bamler R. Doppler frequency estimation and the Cramer–Rao bound. IEEE Trans. Geosci. Remote Sens., 1991, vol. 29, no. 3, pp. 385–390. doi: 10.1109/36.79429.
  17. Kinkulkin I.E., Rubtsov V.D., Fabrik M.A. Fazovyi metod opredeleniya koordinat [Phase Method for Coordinates Determination]. Moscow, Sov. Radio, 1979. 280 p. (In Russian)
  18. Epictetov L.A., Merzakreev R.R., Sidorov V.V. Application of meteor burst equipment for high precision comparisons of time and frequency standards. Proc. 7th Eur. Frequency and Time Forum (EFTF'93), 1993, pp. 413–416.
  19. Liu H.-Y., Tian X.-H., Gu Ch., Fan P., Ni X., Yang R., Zhang J.-N., Hu M., Guo J., Cao X., Hu X., Zhao G., Lu Y.-Q., Gong Y.-X., Xie Zh., Zhu Sh.-N. Optical-relayed entanglement distribution using drones as mobile nodes. Phys. Rev. Lett., 2021, vol. 126, no. 2, art. 020503, pp. 1–6. doi: 10.1103/PhysRevLett.126.020503.

The content is available under the license Creative Commons Attribution 4.0 License.