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A Method of Fast and Simultaneous Calibration of Many Mobile FMCW Radars Operating in a Network Anti-Drone System

Abstract

A market for small drones is developing very fast. They are used for leisure activities and exploited in commercial applications. However, there is a growing concern for accidental or even criminal misuses of these platforms. Dangerous incidents with drones are appearing more often, and have caused many institutions to start thinking about anti-drone solutions. There are many cases when building stationary systems seems to be aimless since the high cost does not correspond with, for example, threat frequency. In such cases, mobile drone countermeasure systems seem to perfectly meet demands. In modern mobile solutions, frequency modulated continuous wave (FMCW) radars are frequently used as detectors. Proper cooperation of many radars demands their measurements to be brought to a common coordinate system—azimuths must be measured in the same direction (preferably the north). It requires calibration, understood as determining constant corrections to measured angles. The article presents the author's method of fast, simultaneous calibration of many mobile FMCW radars operating in a network. It was validated using 95,000 numerical tests. The results show that the proposed method significantly improves the north orientation of the radars throughout the whole range of the initial errors. Therefore, it can be successfully used in practical applications.

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Authors (3)

Details

Category:
Articles
Type:
artykuły w czasopismach
Published in:
Remote Sensing no. 11, pages 1 - 19,
ISSN: 2072-4292
Language:
English
Publication year:
2019
Bibliographic description:
Nowak A., Naus K., Maksimiuk D.: A Method of Fast and Simultaneous Calibration of Many Mobile FMCW Radars Operating in a Network Anti-Drone System// Remote Sensing -Vol. 11,iss. 22 (2019), s.1-19
DOI:
Digital Object Identifier (open in new tab) 10.3390/rs11222617
Bibliography: test
  1. Goldman Sachs. Available online: https://www.goldmansachs.com/our-thinking/technology-driving- innovation/drones/ (accessed on 11 August 2019). open in new tab
  2. Interesting Engineering. Available online: https://interestingengineering.com/top-5-drone-intercepting- methods-you-should-know-about (accessed on 11 August 2019). open in new tab
  3. Michel, A.H. Counter-Drone Syst. The report by Bard College's Center for the Study of the Drone: Dutchess Country, NY,USA, 2018. open in new tab
  4. Hertz Systems. Available online: http://thehawksystem.com/pl/ (accessed on 1 September 2019). open in new tab
  5. SpotterRF. Available online: https://spotterrf.com/products/mobile-solutions/ (accessed on 1 September 2019).
  6. Farlik, J.; Kratky, M.; Casar, J.; Stary, V. Multispectral Detection of Commercial Unmanned Aerial Vehicles. Sensors 2019, 19, 1517, doi:10.3390/s19071517. open in new tab
  7. Laurenzis, M.; Hengy, S.; Hommes, A.; Kloeppel, F.; Shoykhetbrod, A.; Geibig, T.; Johannes, W.; Naz, P.; Christnacher, F. Multi-sensor field trials for detection and tracking of multiple small unmanned aerial vehicles flying at low altitude. In Proceedings of the Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, Anaheim, CA, USA, 10-12 April 2017. open in new tab
  8. Nassi, B.; Shabtai, A.; Masuoka, R.; Elovici, Y. SoK -Security and Privacy in the Age of Drones: Threats, Challenges, Solution Mechanisms, and Scientific Gaps. arXiv 2019, arXiv:1903.05155. open in new tab
  9. Doroftei, D.; De Cubber, G. Qualitative and quantitative validation of drone detection systems. In Proceedings of the International Symposium on Measurement and Control in Robotics, Mons, Belgium, 26-28 September 2018, doi:10.5281/zenodo.1462586. open in new tab
  10. Zhahir, A.; Razali, A.; Mohd Ajir, M.R. Current development of UAV sense and avoid system. IOP Conf. Ser.: Mater. Sci. Eng. 2016, 152, 012035. open in new tab
  11. Eriksson, N. Conceptual Study of a Future Drone Detection System. Master's Thesis, Product Development, Chalmers University of Technology, Gothenburg, Sweden, 2018.
  12. Hommes, A.; Shoykhetbrod, A.; Noetel, D.; Stanko, S.; Laurenzis, M.; Hengy, S.; Christnacher, F. Detection of acoustic, electro-optical and radar signatures of small unmanned aerial vehicles. In Proceedings of the Target and Background Signatures II, Edinburgh, UK, 26-27 September 2016; Volume 9997, p. 999701. open in new tab
  13. Stateczny, A.; Lubczonek, J. FMCW Radar Implementation in River Information Services in Poland. In Proceedings of 16th International Radar Symposium (IRS), Dresden, Germany, 24-26 June 2015; pp. 852- 857. open in new tab
  14. Farlik, J. Radar cross section and detection of small unmanned aerial vehicles. In Proceedings of the 17th International Conference on Mechatronics -Mechatronika (ME), Prague, Czech Republic, 7-9 December 2016; pp.1-7.
  15. Schroder, A. Numerical and Experimental Radar Cross Section Analysis of the Quadrocopter DJI Phantom 2. In Proceedings of the 2015 IEEE Radar Conference, Johannesburg, South Africa, 27-30 October 2015; pp. 463-468; open in new tab
  16. Ritchie, M. Micro-drone RCS Analysis, In Proceedings of the 2015 IEEE Radar Conference, Johannesburg, South Africa, 27-30 October 2015. open in new tab
  17. Li, C.J.; Ling H. An Investigation on the Radar Signatures of Small Consumer Drones. IEEE Antennas Wirel. Propag. Lett. 2017, 16, 649-652. open in new tab
  18. Guay, R.; Drolet, G.; Bray, J.R. Measurement and modelling of the dynamic radar cross-section of an unmanned aerial vehicle. IET Radar, Sonar Navig. 2017, 11, 1155-1160. open in new tab
  19. Herschfelt, A.; Birtcher, R.C.; Gutierrez, R.M.; Rong, Y.; Yu, H.; Balanis, C.A.; Bliss, D.W. Consumer-Grade Drone Radar Cross-Section and Micro-Doppler Phenomenology. In Proceedings of the 2017 IEEE Radar Conference, Seattle, WA, USA, 8-12 May 2017; pp. 0981-0985. open in new tab
  20. Kim, B.K.; Kang, H.-S.; Park, S.-O.Experimental Analysis of Small Drone Polarimetry Based on Micro- Doppler Signature. IEEE Geosci. Remote. Sens. Lett. 2017, 14, 1670-1674. open in new tab
  21. de Wit, J.J.M.; Harmanny, R.I.A.; Molchanov, P.Radar micro-Doppler feature extraction using the Singular Value Decomposition. In Proceedings of the 2014 International Radar Conference, Lille, France, 13-17 October 2014; pp. 1-6. open in new tab
  22. Molchanov, P.; Egiazarian, K.; Astola, J.; Harmanny, R.I.A.; de Wit, J.J.M. Classification of small UAVs and birds by micro-Doppler signatures. In Proceedings of the 2013 European Radar Conference (EuRAD), Nuremberg, Germany, 9-11 October 2013; pp.172-175. open in new tab
  23. Harmanny, R.I.A.; de Wit, J.J.M.; Cabic, G.P. Radar micro-Doppler feature extraction using the spectrogram and the cepstrogram. In Proceedings of the 11th European Radar Conference (EuRAD), Rome, Italy, 8-10 October 2014; pp.165-168. open in new tab
  24. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). open in new tab
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Gdańsk University of Technology

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