Signal processing for direction finding and range determining to small UAVs in the optical and infrared ranges

Authors

  • И.В. Корытцев
  • С.А. Шейко
  • В.М. Карташов
  • О.В. Зубков
  • В.Н. Олейников
  • С.И. Бабкин
  • И.С. Селезнев

DOI:

https://doi.org/10.30837/rt.2020.3.202.13

Keywords:

UAV, video camera, range, drone, coordinates, detection, recognition, rectification, stereo vision, tracking.

Abstract

Detection and assessment of UAV coordinates is critical to protect against their unauthorized use in protected areas. The paper considers the problem of choosing the algorithm and parameters of stereo pair video processing in the visible, near-infrared and far-infrared ranges for reliable determination of small UAVs coordinates, further tracking of them and evaluation of UAVs motion parameters. Theoretical analysis of the optical method possibilities for two-channel stereo-video observation is carried out. The paper presents the results of field experiments aimed to determine the coordinates of a small UAV DJI Phantom 4 using a stereo-video observation system based on IP cameras. The external and internal parameters of the stereo-video observation system were calibrated taking into account the nonlinear distortions of the lenses. The cameras were calibrated in OpenCV using a function based on Zhang and Bouguet methods. The theoretical and practical errors in measuring the range to test objects at their different positions were determined. An algorithm for image processing of a stereo-video observation system for detection, recognition and measurement of UAV coordinates is described. The results of measurements of UAV coordinates for two test flights are presented. The measurement of the true coordinates of the UAV was carried out according to the data of the onboard GPS receiver. The results of measuring the azimuth and elevation of the UAV by the stereo-video observation system were matched with the data of the GPS receiver. This fact can be explained by high resolution of the cameras and the precise calibration of their internal parameters. The root-mean-square relative error in measuring the range was about 10%. Ways for improving the accuracy of UAV stereo-video observation systems are shown.

References

Farlik J., Kratky M, Casar J, Stary V. Multispectral Detection of Commercial Unmanned Aerial Vehicles // Sensors. 2019. Vol. 19 (7). P.1517–1545.

Shapiro L., Stockman G. Computer Vision. Prentice Hall, 2001. 617 p.

Computer Vision. CCF Chineese conf. CCCV 2015. Proseedings, Part II / Editors Zha H., Chen X., Wang L., Miao Q. Xi’an, China. September, 2015. 471 р.

Mrovlje J., Vrancic D. Distance measuring based on stereoscopic pictures // Proc. 9th International PhD Workshop on Systems and Control. Izola, Slovenia. 2008. 6 p.

Gökçe F, Üçoluk G, Şahin E, Kalkan S. Vision-Based Detection and Distance Estimation of Micro Unmanned Aerial Vehicles // Sensors. 2015. Vol. 15 (9). P. 23805–23846.

Ma Z., Hu T., Shen L. Stereo Vision Guiding for the Autonomous Landing of Fixed-Wing UAVs: A Saliency-Inspired Approach // International Journal of Advanced Robotic Systems. March, 2016. 13 р.

Mustafah Y. M., Azman A. W., Akbar F. Indoor UAV positioning using stereo vision sensor // Procedia Engineering. 2012. Vol. 41. P. 575–579.

Kong W., Zhang D., Wang X., Xian Z., J. Zhang. Autonomous landing of an UAV with a ground-based actuated infrared stereo vision system // 2013 IEEE/RSJ International Conference on Intelligent Robots and Sys-tems. Tokyo, 2013. P. 2963–2970.

Chaudhuri S., Rajagopalan A.N. Depth from Defocus: A Real Aperture Imaging Approach. Washington: Springer, 1999. 172 p.

Патент RU2568335C1. Способ измерения дальности до объектов по их изображениям преимущественно в космосе / Смирнов А.И. Заявл. 22.05.2014; опубл. 20.11.2015, бюл. № 32. 10 c.

Andraši P., Radišić T., Muštra M., Ivošević J. Night-time Detection of UAVs using Thermal Infrared Camera // Transportation Research Procedia. Vol. 28. 2017. P. 183–190.

Kartashov V., Oleynikov V., Koryttsev I., Zubkov O., Babkin S., Sheiko S. Processing and Recognition of Small Unmanned Vehicles Sound Signals // International Scientific-Practical Conference on Problems of Infocommunications – Science and Technology, PICS&T 2018 – Proceedings. P. 392–396.

Oleynikov V.N., Zubkov O.V., Kartashov V.M., Korytsev I.V., Babkin S.I., Sheiko S.A. Investigation of detection and recognition efficiency of small unmanned aerial vehicles on their acoustic radiation // Tel-ecommunications and Radio Engineering. 2019. Vol. 78 (9). P. 759–770.

Oleynikov V., Zubkov O., Kartashov V., Koryttsev I., Sheiko S., Babkin S. Experimental estimation of direction finding to unmanned air vehicles algorithms efficiency by their acoustic emission // 2019 International Scientific-Practical Conference on Problems of Infocommunications – Science and Technology, PIC S&T 2019 – Proceedings. P. 175–178.

Kartashov V.M., Oleynikov V.N, Sheyko S.A., Babkin S.I., Koryttsev I.V., Zubkov O.V., Anokhin M.A. Information characteristics of sound radiation of small unmanned aerial vehicles // Telecommunications and Radio Engineering. 2018. Vol. 77 (10). P. 915–924.

Szeliski R. Computer Vision: Algorithms and Applications. Washington: Springer, 2011. 812 p.

Zaarane A., Slimani I., Al Okaishi W., Atouf I., Hamdoun A. Distance measurement system for autonomous vehicles using stereo camera // Array. 2020. Vol. 5. P. 100016–100023.

Kusworo A. Distance measurement with a stereo camera // International Journal of Innovative Research in Advanced Engineering. 2017. Vol. 4 (11). P. 24–27.

Hou A.L., Cui X., Geng,Y., Yuan J., Hou J. Measurement of Safe Driving Distance based on Stereo Vision // Sixth International Conference on Image and Graphics (ICIG). Hefei, Anhui, China. 2011. P. 902–907.

Tsai R.Y. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses // IEEE Int. Journal on Robotics and Automation. 1987. Vol. 3. P. 323–344.

Cipolla R., Drummond T., Robertson D. Camera calibration from vanishing points in images of architectural scenes // BMVC. September, 1999. P. 382–391.

Zhang Z. Flexible New Technique for Camera Calibration // IEEE Transaction on Pattern Analysis and Machine Intelligence. 2000. Vol. 22 (11). P. 1330–1334.

Bouguet J.Y. MATLAB calibration tool // http://www.vision.caltech.edu/bouguetj/calib_doc.

Kartashov V., Oleynikov V., Zubkov O., Sheiko S. Optical detection of unmanned air vehicles on a video stream in a real-time // The Fourth International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo’2019). Odessa, Ukraine. 9–13 September 2019. 4 p.

How to Cite

Корытцев, И., Шейко, С., Карташов, В., Зубков, О., Олейников, В., Бабкин, С., & Селезнев, И. (2020). Signal processing for direction finding and range determining to small UAVs in the optical and infrared ranges. Radiotekhnika, 3(202), 125–135. https://doi.org/10.30837/rt.2020.3.202.13

Issue

Section

Articles

Most read articles by the same author(s)

1 2 > >>