Determining the unmanned aerial vehicle horizontal flight path through the line of electrostatic sensors

Mathematica modeling, numerical technique and program complexes


Аuthors

Scriabin Y. M.*, Potechin D. S.**

MIREA — Russian Technological University (Lomonosov Institute of Fine Chemical Technologies), 78, Vernadsky prospect, Moscow, 119454, Russia

*e-mail: meh-record@yandex.ru
**e-mail: msyst@msyst.ru

Abstract

The article considers the problem of unmanned flying vehicles (UAV) detection and localization. This problem is associated with the UAVs rapid growth in number and relatively mild laws on the UAVs' regulation and flights organization. Thus, it is utterly important to ensure safety of critical facilities and highly crowded places fr om possible threats associated with the UAV application, as well as monitor the air space any time and under any weather conditions. To solve the problem, the authors propose to employ electrostatic monitoring technology.

It is worth mentioning, that conventional detection methods have problems with the low-altitude small UAVs detection. The advantage of electrostatic monitoring technology consists in the fact that this technology is capable provide information passively on low-altitude targets by monitoring changes in the electrostatic field in several points on the Earth surface. Besides, combining electrostatic monitoring technology with other existing detecting techniques may increase the probability of targets detecting.

The authors propose employing the time-frequency analysis for the electrostatic signal extraction. This method may be based on positions of the electrostatic signal extremes. The article defines analytical solution of the electrostatic problem of a point electrostatic charge movement above an infinite horizontal conducting plane.

The results of the analytical solution of the electrostatic problem of a UAV detecting by three electrostatic sensors lying on one straight line are presented. The article determined the theoretical lim it of the UAV detection by an electrostatic sensor. Theoretical equations for the UAV coordinates calculating based on the time-frequency analysis of electrostatic signals were derived.

The article presents experimental electrostatic signals received by an electrostatic sensor while the UAV flight. Two types of sensors were examined in the experiment. These are electrostatic fluxmeter and electrostatic probe.

The authors note that the electrostatic sensor system is potentially capable of detecting UAVs with a charge of 1 µC at the altitudes up to 100 m.

Keywords:

unmanned aerial vehicle detection, electrostatic monitoring technology, electrostatic sensor, electrostatic signal

References

  1. Ananenkov A.E., Marin D.V., Nuzhdin V.M., Rastorguev V.V. Trudy MAI, 2016, no. 91, available at: http://trudymai.ru/eng/published.php?ID=75662

  2. Godunov A.I., Shishkov S.V., Yurkov N. K. Nadezhnost' i kachestvo slozhnykh system, 2014, no. 2 (6), pp. 62 - 70.

  3. Sel'vesyuk N.I., Veselov Yu.G., Gaidenkov A.V., Ostrovskii A.S. Trudy MAI, 2018, no. 103, available at: http://trudymai.ru/eng/published.php?ID=10782

  4. Zhenhua Wen, Junxing Hoa, Jason Atkin. A review of electrostatic monitoring technology: The state of the art and future research directions, Progress in Aerospace Sciences, 2017, vol. 2, Issue 1, pp. 1 - 11.

  5. Zhang W., Yan Y., Yang Y. & Wang J. Measurement of flow characteristics in a bubbling fluidized bed using electrostatic sensor arrays, IEEE Transactions on Instrumentation and Measurement, 2016, no. 65, pp. 703 - 712.

  6. Efimov E., Polushin P., Grunskaya L. Izmerenie elektrostaticheskoi sostavlyayushchei elektrostaticheskikh polei. Izmeriteli elektrostaticheskogo polya, Berlin, Verlag, 2008, 146 p.

  7. Chubb J. The measurement of atmospheric electric fields using pole mounted electrostatic fieldmeters, Journal of Electrostatics, 2014, no. 72, pp. 295 - 300.

  8. Molniezashchita samoletov, available at: http://www.zandz.ru/biblioteka/statya5/zaryad_elektrizacii_samoleta.html

  9. Mikhailov N.A. Trudy MAI, 2017, no. 96, available at: http://trudymai.ru/eng/published.php?ID=85861

  10. McFarland M.B., Zachery R.A., Taylor B.K. Motion planning for reduced observability of autonomous aerial vehicles, IEEE International Conference on Control Applications, 22-27 Aug. 1999, Hawaii, 1999, vol. MP 5, no. 3, pp. 231 - 235.

  11. J. Barreiro-Gomez, I. Mas, C. Ocampo-Martínez, R. Sánchez Peña and N. Quijano. Distributed formation control of multiple unmanned aerial vehicles over time-varying graphs using population games, 55th IEEE Conference on Decision and Control, Las Vegas, EEUU, 2016, pp. 5245 – 5250.

  12. Imyanitov I.M. Pribory i metody dlya izucheniya elektrichestva (Instruments and methods for studying electricity), Moscow, Gostekhizdat, 1957, 344 p.

  13. Skryabin Yu.M., Potekhin D.S. Fundamental'nye problemy radioelektronnogo priborostroeniya, 2017, vol. 17, no. 5, pp. 1235 - 1238.

  14. Metin I.G., Potekhin D.S., Skryabin Yu.M. Trudy 18-nauchnoi konferentsii po radiofizike, posvyashchennoi dnyu Radio, Nizhnii Novgorod, NNGU, 2014, pp. 313 - 315.

  15. Wenhui Wang, Ruxia Du, Linping He, Weiwei Zhao, Yunfei Chen, Junpeng Lu, Zhenhua Ni. High-performance graphene-based electrostatic field sensor, IEEE Electron Device Letters, 2017, no. 38, pp. 1136-1138.

  16. Smait V. Elektrostatika i elektrodinamika (Electrostatics and Electrodynamics), Moscow, IL, 1954, 605 p.

  17. Z. Wen, X. Zhao. A hybrid de-noising method based on wavelet and median filter for aero-engines gas path electrostatic monitoring, International Conference on Graphic and Image Processing, International Society for Optics and Photonics, 2011, doi: 10.1117/12.913570

  18. C.S. Chang, J. Jin, S. Kumar, Q. Su, T. Hoshino, M. Hanai et al. Denoising of partial discharge signals in wavelet packets domain, IEE Proceedings A - Science, Measurement and Technology, 2005, no. 152 (3), pp. 129 – 140.

  19. D.L. Donoho. De-noise via soft threshold, IEEE Transactions on Information Theory, 1995, no. 41 (3), pp. 613 - 627.

  20. S.G. Mallat. A theory for multiresolution signal decomposition: the wavelet representation, IEEE transactions on pattern analysis and machine intelligence, 1989, no. 11 (7), 674 – 693.


Download

mai.ru — informational site MAI

Copyright © 2000-2024 by MAI

Вход