Coordinates measuring techniques improving of unmanned aerial vehicle in conditions of abnormality (distortion)

Aeronautical and Space-Rocket Engineering

Dynamics, ballistics, movement control of flying vehicles


Аuthors

Goncharov V. M.*, Zaitsev A. V.**, Lupanchuk V. Y.***

Military Academy of Strategic Rocket Troops named after Peter the Great, SRTMA, 8, Karbysheva str., Balashikha, Moscow region, 143900, Russia

*e-mail: vladimir-goncharov.1986@mail.ru
**e-mail: ug253@mail.ru
***e-mail: raketofflu@mail.ru

Abstract

The article regards the problem of the coordinates measuring system state assessing of the short range and near-in operating radius unmanned aerial vehicle (UAV) in conditions of abnormality (distortions) of measurement results obtained from the satellite navigation system (SNS). Optoelectronic system, incorporating both TV and thermal imaging information channels, as well as laser rangefinder of the target indicator is being considered as an extra information source.

This article urgency is stipulated by the necessity of positioning the short range and near-in operating radius UAV with restricted mass and size parameters without employing additional or high-accuracy measurement instruments onboard with full (partial) absence of satellite signals in autonomous flight mode.

The purpose of the article consists in preserving the UAV current position determining accuracy in conditions of partial or complete absence of the signals from the SNS.

The object of research is the UAV navigation system.

The subject of the research is navigation information processing processes in conditions of partial or complete absence of the satellite signal.

The scientific novelty of the research is stipulated by the development and scientific substantiation of a comprehensive technique for optimal estimation of the UAV current position by visual navigation method, allowing correction amendments forming to refine the UAV spatial position in the presence of the extra information source.

Theoretical significance of the results consists in supplementing of visual navigation methods by forming coefficients, characterizing the sparseness of the terrain exceptional points and actual share of the reference image generality from the current one, allowing determine the UAV’s sufficient altitude over exceptional points of the underlying terrain. Computation of the correction image period forming, with the regard to the instrumental errors of the strapdown inertial navigation system (SINS) based on micro-electrical and micromechanical systems was performed as well.

Practical significance of the research lies in application of integrated technique in the small-sized vehicles positioning problems in the absence of signals from the SNS, as well as substantiating intelligent image processing employing high-performance, small-sized equipment on board the UAV.

The experiment demonstrated that in the absence of the SINS correction, the UAV accumulates the maximum positioning RMS error on an average of 150 m during the first minutes of flight. With regard to this and the maximum possible UAV speed of the of 120 km / h, at a distance of 5 km from the launch point the limiting RMS error of positioning, during the return flight, will be about 300 m, which can lead to the UAV loss. The UAV correction according to the formed correction areas allows to reducing the RMS error to 200 m.

Keywords:

unmanned aerial vehicle, coordinateometry, state coordinateometry, Kalman filter, correction area, correction height, distortion of satellite signals

References

  1. Berkovich S.B., Kotov N.I., Makhaev A.Yu. et al. Izvestiya instituta inzhenernoi fiziki, 2017, no. 1(43), pp. 2-13.

  2. Sharov S.N., Petukhova E.S., Dvoryashin M.S. et al. Patent RU 110070 U1, 10.11.2011.

  3. Zav’yalov R.A., Zainullin A.V., Poroshkin K.V., Yamaliev R.R. Patent RU 137016 U1, 27.01.2014.

  4. Vilkova N.N., Shakhrai V.I., Arzumanyan E.P. Patent RU 155323 U1, 27.09.2015.

  5. Lunev E.M. Improving the accuracy of UAV navigation parameters during landing on the basis of photogrammetric measurements. Aerospace MAI Journal, 2011, vol. 18, no. 2, pp. 150-159.

  6. Qadir A., Semke W., Neubert J. Implementation of an Onboard Visual Tracking System with Small Unmanned Aerial Vehicle (UAV). International Journal of Innovative Technology & Creative Engineering, 2011, vol. 1, no. 10, pp. 17-25.

  7. Zhang J., Liu W., Wu Y. Novel technique for vision-based UAV navigation. IEEE Transactions on Aerospace and Electronic Systems, 2011, vol. 47, no. 4, pp. 2731–2741. DOI: 10.1109/TAES.2011.6034661

  8. Woo J., Son K., Li T., Kim G., Kweon I.S. Vision-based UAV Navigation in Mountain Area. IAPR Conference on Machine Vision Applications MVA’2007 (16-18 May 2007; Tokyo, Japan), pp. 236-239.

  9. Goncharov V.M., Lupanchuk V.Yu. Neirokomp’yutery: razrabotka, primenenie, 2020, vol. 22, no. 1, pp. 18-31. DOI: 10.18127/j19998554-202001-02

  10. Lupanchuk V.Yu., Kukankov S.N., Goncharov V.M. Patent RU 2722599 C1, 02.06.2020.

  11. Lobanov A.M. Fotogrammetriya (Photogrammetry), Moscow, Nedra, 1984, 552 p.

  12. Kalitkin N.N. Chislennye metody (Numerical methods), Moscow, Nauka, 1978, 512 p.

  13. Christofides N. Graph theory. An algorithmic approach (Computer science and applied mathematics). Academic Press, 1975, 400 p.

  14. Goncharov V.M. Svidetel’stvo o registratsii programmy dlya EVM RU 2019665397, 22.11.2019.

  15. Stepanov O.A. Osnovy teorii otsenivaniya s prilozheniyami k zadacham obrabotki navigatsionnoi informatsii. Ch. 1. Vvedenie v teoriyu otsenivaniya (Fundamentals of the theory of estimation with applications to problems of processing navigation information. Part 1. Introduction to the theory of estimation). 3rd ed. Saint Petersburg, TsNII “Elektropribor”, 2017, 509 p.

  16. Stepanov O.A. Primenenie teorii nelineinoi fil’tratsii v zadachakh obrabotki navigatsionnoi informatsii (Application of the theory of nonlinear filtering in problems of navigation information processing). 3rd ed. Saint Petersburg, TsNII “Elektropribor”, 2003, 370 p.

  17. Kalman R.E. A New Approach to Linear Filtering and Prediction Problems. Journal of Fluids Engineering, 1960, vol. 82, no. 1, pp. 35-45. DOI: 10.1115/1.3662552

  18. Babich O.A. Obrabotka informatsii v navigatsionnykh kompleksakh (Information processing in navigation complexes), Moscow, Mashinostroenie, 1991, 512 p.

  19. Neusypin K.A. Sovremennye sistemy i metody navedeniya, navigatsii i upravleniya letatel’nymi apparatami (Modern systems and methods of guidance, navigation and control of aircraft), Moscow, MGOU, 2009, 500 p.

  20. Statisticheskie metody. Generatsiya sluchainykh chisel. GOST R ISO 28640-2012 (Statistical methods. Random variate generation. State Standard R ISO 28640-2012), Moscow, Standartinform, 2014, 40 p.

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