Devising a numerical method for estimating the positioning accuracy of aircraft by an information- communication network of optoelectronic stations

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.330922

Keywords:

optoelectronic station, infocommunication network, aircraft, convex polyhedron, scattering ellipsoid

Abstract

The object of this study is the accuracy of aircraft positioning for open and covert video surveillance by an infocommunication network of optical-electronic stations along the trajectories of their movement. The task addressed is numerical assessment of the accuracy of aircraft positioning in airspace. It is proposed to use a convex polyhedron as a universal assessment of the accuracy of aircraft positioning, in which, with a given probability, the aircraft is located. It is shown that the lower estimate of this probability depends on the a priori information on the statistical properties of the errors in the estimates of the coordinates of the aircraft location, and the scattering ellipsoid, which is currently the main form of assessing the accuracy of aircraft positioning in airspace, is a special case and is always located inside a convex polyhedron.

The results reported here include the following:

– simulation models of open and covert video surveillance by an infocommunication network of optoelectronic stations along the trajectories of aircraft movement;

– a numerical method for estimating the uncertainty region in the form of a convex polyhedron, in which, with a given probability, the aircraft is located;

– dependence of change in the shapes and boundaries of the convex polyhedron on the errors of video surveillance and the mutual spatial location of the aircraft and the network of optoelectronic stations;

– software implementation of methods for constructing and visualizing the shapes and boundaries of uncertainty regions in the form of convex polyhedrons and scattering ellipsoids.

It is shown that the aircraft is inside the convex polyhedron with the probability P ≥ 0.8889 for any distribution, P ≥ 0.9506 for a symmetric one and P ≥ 0.9973 for a normal distribution

Author Biographies

Andriy Tevyashev, Kharkiv National University of Radio Electronics

Doctor of Technical Sciences, Professor

Department of Applied Mathematics

Oleksii Haluza, National Technical University «Kharkiv Polytechnic Institute»; Kharkiv National University of Radio Electronics

Doctor of Physical and Mathematical Sciences, Professor

Department of Computer Mathematics and Data Analysis

Department of Software Engineering

Dmytro Kostaryev, Kharkiv National University of Radio Electronics

Doctor of Technical Sciences

Department of Applied Mathematics

Anton Paramonov, Kharkiv National University of Radio Electronics

PhD Student

Department of Information Technology Security

Nataliia Sizova, O.M. Beketov National University of Urban Economy in Kharkiv

Doctor of Physical and Mathematical Sciences, Professor

Department of Applied Mathematics and Information Technology

References

  1. Bensky, A. (2016). Wireless Positioning Technologies and Applications. Artech House, 424.
  2. Lazzari, F., Buffi, A., Nepa, P., Lazzari, S. (2017). Numerical Investigation of an UWB Localization Technique for Unmanned Aerial Vehicles in Outdoor Scenarios. IEEE Sensors Journal, 17 (9), 2896–2903. https://doi.org/10.1109/jsen.2017.2684817
  3. Semenyuk, V., Kurmashev, I., Lupidi, A., Alyoshin, D., Kurmasheva, L., Cantelli-Forti, A. (2025). Advances in UAV detection: integrating multi-sensor systems and AI for enhanced accuracy and efficiency. International Journal of Critical Infrastructure Protection, 49, 100744. https://doi.org/10.1016/j.ijcip.2025.100744
  4. Saadaoui, F. Z., Cheggaga, N., Djabri, N. E. H. (2023). Multi-sensory system for UAVs detection using Bayesian inference. Applied Intelligence, 53 (24), 29818–29844. https://doi.org/10.1007/s10489-023-05027-z
  5. Stuckey, H., Escamilla, L., Garcia Carrillo, L. R., Tang, W. (2024). Real-Time Optical Localization and Tracking of UAV Using Ellipse Detection. IEEE Embedded Systems Letters, 16 (1), 1–4. https://doi.org/10.1109/les.2023.3234871
  6. Stuckey, H., Al-Radaideh, A., Escamilla, L., Sun, L., Carrillo, L. G., Tang, W. (2021). An Optical Spatial Localization System for Tracking Unmanned Aerial Vehicles Using a Single Dynamic Vision Sensor. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 3093–3100. https://doi.org/10.1109/iros51168.2021.9636665
  7. Golyak, I. S., Anfimov, D. R., Golyak, I. S., Morozov, A. N., Tabalina, A. S., Fufurin, I. L. (2020). Methods for real-time optical location and tracking of unmanned aerial vehicles using digital neural networks. Automatic Target Recognition XXX, 50. https://doi.org/10.1117/12.2573209
  8. Nam, S. Y., Joshi, G. P. (2017). Unmanned aerial vehicle localization using distributed sensors. International Journal of Distributed Sensor Networks, 13 (9), 155014771773292. https://doi.org/10.1177/1550147717732920
  9. Hu, F., Wu, G. (2020). Distributed Error Correction of EKF Algorithm in Multi-Sensor Fusion Localization Model. IEEE Access, 8, 93211–93218. https://doi.org/10.1109/access.2020.2995170
  10. Sorbelli, F. B., Pinotti, C. M., Silvestri, S., Das, S. K. (2022). Measurement Errors in Range-Based Localization Algorithms for UAVs: Analysis and Experimentation. IEEE Transactions on Mobile Computing, 21 (4), 1291–1304. https://doi.org/10.1109/tmc.2020.3020584
  11. Vitiello, F., Causa, F., Opromolla, R., Fasano, G. (2024). Radar/visual fusion with fuse-before-track strategy for low altitude non-cooperative sense and avoid. Aerospace Science and Technology, 146, 108946. https://doi.org/10.1016/j.ast.2024.108946
  12. Bala, A., Muqaibel, A. H., Iqbal, N., Masood, M., Oliva, D., Abdullahi, M. (2025). Machine learning for drone detection from images: A review of techniques and challenges. Neurocomputing, 635, 129823. https://doi.org/10.1016/j.neucom.2025.129823
  13. Yan, X., Fu, T., Lin, H., Xuan, F., Huang, Y., Cao, Y. et al. (2023). UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey. Applied Sciences, 13 (20), 11320. https://doi.org/10.3390/app132011320
  14. Svanstrom, F., Englund, C., Alonso-Fernandez, F. (2021). Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors. 2020 25th International Conference on Pattern Recognition (ICPR), 7265–7272. https://doi.org/10.1109/icpr48806.2021.9413241
  15. Tevyashev, A., Zemlyaniy, O., Shostko, I., Kostaryev, D., Paramonov, A. (2024). Devising an analytical method for estimating aircraft positioning accuracy by an infocommunication network of optoelectronic stations. Eastern-European Journal of Enterprise Technologies, 5 (9 (131)), 36–48. https://doi.org/10.15587/1729-4061.2024.312762
  16. Zekavat, S. A. (Reza), Buehrer, R. M. (Eds.) (2011). Handbook of Position Location. Wiley. https://doi.org/10.1002/9781118104750
  17. Khudov, H., Berezhnyi, A., Oleksenko, O., Maliuha, V., Balyk, I., Herda, M., Sobora, A. et al. (2023). Increasing of the accuracy of determining the coordinates of an aerial object in the two-position network of small-sized radars. Eastern-European Journal of Enterprise Technologies, 5 (9 (125)), 6–13. https://doi.org/10.15587/1729-4061.2023.289623
  18. Zheng, Q., Chen, J., Yang, R., Shan, Z. (2017). Research on airborne infrared location technology based on orthogonal multi-station angle measurement method. Infrared Physics & Technology, 86, 202–206. https://doi.org/10.1016/j.infrared.2017.08.019
  19. Putyatin, V. G., Dodonov, A. G. (2017). Ob odnoy zadache vysokotochnyh traektornyh izmereniy opticheskimi sredstvami. Reiestratsiya, zberihannia i obrobka danykh, 19 (2), 36–54. Available at: http://nbuv.gov.ua/UJRN/rzod_2017_19_2_6
  20. Dodonov, A. G., Putyatin, V. G. (2017). Nazemnye opticheskie, optiko-elektronnye i lazerno-televizionnye sredstva traektornyh izmereniy. Matematychni mashyny i systemy, 4, 30–56. Available at: http://dspace.nbuv.gov.ua/bitstream/handle/123456789/131985/02-Dodonov.pdf?sequence=1
  21. Tevjashev, A., Zemlyaniy, O., Shostko, I., Paramonov, A. (2024). Mathematical Models and Methods of Observation and High-Precision Assessment of the Trajectories Parameters of Aircraft Movement in the Infocommunication Network of Optoelectronic Stations. 2nd International Congress of Electrical and Computer Engineering, 295–309. https://doi.org/10.1007/978-3-031-52760-9_21
  22. Shostko, I., Tevyashev, A., Zemlyaniy, O., Tsibulnikov, D. (2023). Designing and testing a prototype of optical-electronic station for detecting and tracking moving objects in the air. Eastern-European Journal of Enterprise Technologies, 6 (5 (126)), 36–42. https://doi.org/10.15587/1729-4061.2023.295101
  23. Hofmann-Wellenhof, B., Lichtenegger, H., Collins, J. (2001). Global Positioning System. Springer Vienna. https://doi.org/10.1007/978-3-7091-6199-9
  24. Hahn, G. J., Shapiro, S. S. (1994). Statistical models in engineering. Wiley, 376.
  25. Precise Simulation. GEOMLib version 1.0. GitHub. Available at: https://github.com/precise-simulation/geomlib/releases/tag/1.0
Devising a numerical method for estimating the positioning accuracy of aircraft by an information- communication network of optoelectronic stations

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Published

2025-06-25

How to Cite

Tevyashev, A., Haluza, O., Kostaryev, D., Paramonov, A., & Sizova, N. (2025). Devising a numerical method for estimating the positioning accuracy of aircraft by an information- communication network of optoelectronic stations. Eastern-European Journal of Enterprise Technologies, 3(9 (135), 101–120. https://doi.org/10.15587/1729-4061.2025.330922

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Section

Information and controlling system