Modern methods for detection of unmanned aerial vehicles
Abstract
Most recent Unmanned Aerial Vehicle (UAV) detection methods are discussed in the article. Detection of UAV principles are pointed out during the overview. Brief advantages of each technique is covered and compared in between. Key technological limitations of each technique is pointed out and discussed. Several most recent and actual UAV threat accidents are presented with the indication of the used counter UAV systems. New upcoming threat of “Kamikaze” (selfdestructive) UAV and their detection limitations are presented. Case studies on the hybrid counter drone technology interactions are covered.
In this article, important civil and military types of UAV propulsion are covered. Design features and future consumer demands, are analyzed, aiming at UAV components which are mandatory to perform a flight. Using recently published articles energy sources and thrust power plants are analyzed. UAV detection principles, that include audio signal signature analysis, aerial object video tracking, thermal heat signature analysis, radar systems, radio frequency spectrum and data packet communication detection are covered, pointing out their advantages and limitations.
Conclusions are drawn taking into account future perspective of the UAV technology developments and upcoming future threats of the highest impact. Evaluation of most actual recent articles is made in order to overview weak points of the counter UAV system development techniques. Finally future UAV technology development is analyzed and main safety related threats are indicated. Slowly developing UAV components are indicated, putting more attention on possible UAV detection methods, where UAV mandatory components will not become obsolete.
Article in English.
Modernūs bepiločių orlaivių aptikimo metodai
Santrauka
Bepiločiai orlaiviai (BO) tapo XXI a. fenomenu. Jie plačiai naudojami policijos, gelbėjimo tarnybų, pasitelkiami kariuomenės poreikiams, tapo geodezijos, žemės ūkio specialistų, filmų kūrėjų ir kitų sričių entuziastų kasdieniu įrankiu. Deja, kasdien dažnėjant bepiločių orlaivių piktavališko naudojimo atvejams, sparčiai didėjant įrangos prieinamumui ir jos autonomiškumui, aptikti bepiločius orlaivius tampa sudėtingu technologiniu ir saugumo užtikrinimo iššūkiu. Vertinant bepiločių orlaivių valdymo galimybes pasitelkus dirbtinį intelektą, artimiausioje ateityje bepiločių orlaivių skrydžiai bus galimi be radijo ryšio palaikymo ar klasikinės navigacijos priemonių. Tobulėjančios autonominio išmanaus skrydžio valdymo technologijos palieka minimalias galimybes aptikti bepiločio orlaivio autonominius skrydžius, ypač kai to reikia visuomenės saugumui ar valstybės strateginių objektų apsaugai užtikrinti.
Šiame straipsnyje apžvelgiami pagrindinės bepiločių orlaivių traukos jėgainės ir energijos šaltiniai. Analizuojami populiariausi bepiločių orlaivių atpažinimo metodai, jų privalumai ir trūkumai. Atkreipiamas dėmesys į skirtingų bepiločių orlaivių aptikimo metodų taikymo galimybes ir jų apribojimus. Išvadose apibendrinamos bepiločių orlaivių aptikimo technologijų vystymosi tendencijos, artimiausi iššūkiai ir teikiamos įžvalgos moderniems bepiločių orlaivių aptikimo metodams.
Reikšminiai žodžiai: bepiločių orlaivių (BO) aptikimas, atpažinimas, UAV, C-UAS.
Keyword : UAV detection, anti drone system, counter drone, C-UAS systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
BBC News. (2018b). Venezuela President Maduro survives drone assassination attempt. https://www.bbc.com/news/world-latin-america-45073385
Blažek, J. (2015). Modelling and simulation of the possible em radiation of the power supply towards the synchronized and bldc motors. Journal of Electrical Engineering, 66(7s), 18–21.
Bunker, R. J. (2015). Terrorist and insurgent Unmanned Aerial Vehicles (UAVs): use, potentials, and military implications. U.S. Army War College, Strategic Studies Institute. http://scholarship.claremont.edu/cgu_facbooks
Chen, Y., Aggarwal, P., Choi, J., & Jay Kuo, C.-C. (2018). A deep learning approach to drone monitoring. In Proceedings − 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 (pp. 686– 691). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/APSIPA.2017.8282120
CNBC News. (2019). Saudi Arabia shuts down half its oil production after drone attack. https://www.cnbc.com/2019/09/14/ saudi-arabia-is-shutting-down-half-of-its-oil-productionafter-drone-attack-wsj-says.html
DJI Inc. (2019). DJI snake motors. https://www.dji.com/lt/snail
Eriksson, N. (2018). Conceptual study of a future drone detection system. http://publications.lib.chalmers.se/records/fulltext/255103/255103.pdf
Ezuma, M., Erden, F., Anjinappa, C. K., Ozdemir, O., & Guvenc, I. (2019). Micro-UAV detection and classification from RF fingerprints using machine learning techniques. Paper presented at the Aerospace Conference Proceedings. https://doi.org/10.1109/AERO.2019.8741970
H2 Richen Power Ltd. (2018). http://en.richenpower.com/product/121.html
Hinostroza, I., Letertre, T., & Mazieres, V. (2018). UAV detection with K band embedded FMCW radar. Paper presented at the Mediterranean Microwave Symposium. https://doi.org/10.1109/MMS.2017.8497143
Hung, J. Y., & Gonzalez, L. F. (2012, May). On parallel hybridelectric propulsion system for unmanned aerial vehicles. Progress in Aerospace Sciences, 51, 1–17. https://doi.org/10.1016/j.paerosci.2011.12.001
Lipovský, P., Heško, F., Moucha, V., & Bažek, J. (2018). Possible detection of multirotor UAVs based on disturbances in magnetic field. In 13th International Scientific Conference − New Trends in Aviation Development (NTAD) (pp. 91–95). Institute of Electrical and Electronics Engineers Inc.
Liu, H., Wei, Z., Chen, Y., Pan, J., Lin, L., & Ren, Y. (2017). Drone detection based on an audio-assisted camera array. In 2017 IEEE 3rd International Conference on Multimedia Big Data (BigMM) (pp. 402–406). https://doi.org/10.1109/BigMM.2017.57
Pechan, T., & Sescu, A. (2015). Experimental study of noise emitted by propeller’s surface imperfections. Applied Acoustics, 92, 12–17. https://doi.org/10.1016/j.apacoust.2014.11.014
Russell, L., Goubran, R., & Kwamena, F. (2019). Emerging urban challenge: RPAS/UAVs in cities. In 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 546–553). https://doi.org/10.1109/DCOSS.2019.00103
Sinibaldi, G., & Marino, L. (2013). Experimental analysis on the noise of propellers for small UAV. Applied Acoustics, 74(1),
79–88. https://doi.org/10.1016/j.apacoust.2012.06.011
Solomitckii, D., Gapeyenko, M., Semkin, V., Andreev, S., & Koucheryavy, Y. (2018). Technologies for efficient amateur drone detection in 5G millimeter-wave cellular infrastructure. IEEE Communications Magazine, 56(1), 43–50. https://doi.org/10.1109/MCOM.2017.1700450