Factors affecting drone detection and countermeasures in future battle scenes

Document Type : Original Article

Authors

1 PhD Student in Department Of Army Staff and Command University

2 Assistant of Prof. in Supreme National Defense University and Strategic Research, Tehran, Iran

3 Associate Prof. Of Supreme National Defense University and Strategic Research, Tehran, Iran

4 Assistant Prof. in AJA Command & Staff University, Tehran, Iran

Abstract

Investigating the nature of UAS threats, the results of recent wars in the West Asian region, the increasing development of military technologies, especially the characteristics of future air threats, including unmanned aerial vehicles (UAVs), the need for scientific attention And accurate to the category of air defense has made it inevitable. In the meantime, dealing with unmanned aircraft systems in times of war and peace, which endanger national and military security, public safety, and people's privacy, is of particular importance. Air defense has three dimensions: detection systems, integrated command and control network, and weapon systems that play the main role in air defense operations. The purpose of the research is to explain the factors affecting the detection and countering of drone systems in future battles. The research is of an applied-developmental type and its approach is prospective and the case-contextual method is used. In this research, the threats of unmanned aircraft are studied first, and then the most important components and indicators of detection systems are explained. To enrich the research, a number of experts in the field of air defense were interviewed. Finally, by identifying the components and indicators affecting the discovery of the air defense model, suggestions were made to deal with drones.

Keywords

References

  •  

    • Bilgin G. (2022). İnsansiz Hava Araci Kullanimina Yönelik Avrupa Konumsal Veri Altyapisi Ulaşim Ağlari Temasinin Genişletilmesi.‏
    • Castrillo V. U. Manco A. Pascarella D. & Gigante G. (2022). A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones. Drones. 6(3): 65.‏
    • Chadwick A. D. (2017). Micro-drone detection using software-defined 3G passive radar.‏
    • Fang G. Yi, J. Wan X. Liu Y., & Ke H. (2018). Experimental research of multistatic passive radar with a single antenna for drone detection. IEEE Access, 6, 33542-33551.‏
    • Federal Aviation Administration, “Unmanned aircraft Systems,” April [Online]. Available: https://www.faa.gov/uas/.
    • Federal Aviation Administration. “FAA Aerospace Forecast 201939.” April 2019. [Online]. Available: https://www.faa.gov/aerospace/
    • FM‌ 44-100. (2017). Chapter 2. Page 2-10‌.
    • Frew, J. (2018). Drone Wars. The next generation. SIPRI Arms Transfers Database. Stockholm International Peace Research Institute.
    • Gettinger D. (2020). Drone Databook Update: March 2020. Center for the Study of the Drone at Bard College.‏
    • Gettinger D. (2020). The Drone Data Book (The Center for the Study of the Drone). Bard College.‏
    • Hengy S. Laurenzis, M. Schertzer S. Hommes A. Kloeppel F. Shoykhetbrod A & Christnacher F. (2017 October). Multimodal UAV detection: Study of various intrusion scenarios. In Electro-Optical Remote Sensing XI.(Vol. 10434, pp. 203-212). SPIE.‏
    • Jian M. Lu Z. & Chen V. C. (2017 May). Experimental study on radar micro-Doppler signatures of unmanned aerial vehicles. In 2017 IEEE Radar Conference (RadarConf) (pp. 0854-0857). IEEE.‏
    • Josephs L. (2019 April). drone flights shut down london gatwick airport, stranding thousands of travelers.
    • Kim J. Park C. Ahn J. KO Y. Park J. & Gallagher J. C. (2017 March). Real-time UAV sound detection and analysis system. In 2017 IEEE Sensors Applications Symposium (SAS) (pp. 1-5). IEEE.‏
    • Knoedler B. Zemmari R. & Koch W. (2016, May). On the detection of small UAV using a GSM passive coherent location system. In 2016 17th International Radar Symposium (IRS) (pp. 1-4). IEEE.‏
    • Martelli T. Murgia F. Colone F. Bongioanni C. & Lombardo. P. (2017 October). Detection and 3D localization of ultralight aircrafts and drones with a WiFi-based passive radar. In International Conference on Radar Systems (Radar 2017) (pp. 1-6). IET.‏
    • Mohajerin N. Histon J. Dizaji R. & Waslander S. L. (2014, May). Feature extraction and radar track classification for detecting UAVs in civillian airspace. In 2014 IEEE Radar Conference (pp. 0674-0679). IEEE.‏
    • Nakamura R. & Hadama H. (2017). Characteristics of ultra-wideband radar echoes from a drone. IEICE Communications Express.‏
    • NATO Standardization Office (NSO) ‘ATP-3.3.8.1 Minimum Training Requirements for Unmanned Aircraft Systems (UAS) Operators and Pilots’ Edition B Version 1.May 2019.
    • Ritchie, M. (2021). Multi-Frequency Micro-Doppler Based Classification of Micro-Drone Payload Weight. Frontiers in Signal Processing.‏
    • Rovkin, M. E., Khlusov, V. A., Malyutin, N. D., Hristenko, A. V., Novikov, A. S., Nosov, D. M., ... & Ilchenkoy, V. E. (2018, May). Radar detection of small-size UAVs. In 2018 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT) (pp. 371-374). IEEE.‏
    • Song, H., Fink, G. A., & Jeschke, S. (Eds.). (2017). Security and privacy in cyber-physical systems: foundations, principles, and applications. John Wiley & Sons.‏
    • H. Liu. Y. and Wang. J “UAS Detection and Negation,” U.S. Patent 62 833 153, 4 12. 2019.
    • Vilímek J. & Buřita L. (2017 May). Ways for copter drone acustic detection. In 2017 International Conference on Military Technologies (ICMT) (pp. 349-353). IEEE.‏
    • Wallace R. J. Haritos T. & Robbins J. (2018). Building Evidence the Federal Aviation Administration's UAS Safety Strategy Needs Improvement. International Journal of Aviation, Aeronautics, and Aerospace. 5(1): 10.‏
    • Wang J. Liu Y. & Song H. (2021). Counter-unmanned aircraft system (s) (C-UAS): State of the art, challenges, and future trends. IEEE Aerospace and Electronic Systems Magazine, 36(3), 4-29.‏
    • Zhang H. Cao C. Xu L. & Gulliver T. A. (2018). A UAV detection algorithm based on an artificial neural network. Ieee Access, 6, 24720-24728.‏
    • Zhao Y. & Su Y. (2018). Cyclostationary phase analysis on micro-Doppler parameters for radar-based small UAVs detection. IEEE Transactions on Instrumentation and Measurement, 67(9), 2048-2057.‏
    • Zhou C. Liu Y. & Song Y. (2016 October). Detection and tracking of a UAV via Hough transform. In 2016 CIE International Conference on Radar (RADAR) (pp. 1-4). IEEE.‏
    • Zywek M. Krawczyk G. & Malanowski M. (2018 June). Experimental results of drone detection using noise radar. In 2018 19th International Radar Symposium (IRS) (pp. 1-10). IEEE.‏
Defensive Future Studies
Volume 7, Issue 25 - Serial Number 25
September 2022
Pages 137-166

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