Field Validation of an Integrated Counter-UAS System: Radar, EO Tracking, RF Jamming, and Drone Interception Performance
Keywords:
Autonomous systems, Counter-UAS, Drone interception, Layered defense, Radar detection, RF jamming, UAS interdictionAbstract
The proliferation of small Unmanned Aerial Systems (UAS) has raised serious security concerns in modern military operations due to their low cost, wide availability, and adaptability. This paper presents a comprehensive field evaluation of an integrated counter-UAS (C-UAS) system conducted in collaboration with a military test unit in Thailand. The deployed system combined a 3D surveillance radar, an Electro-Optical (EO) tracking camera, a multi-band Radio-Frequency (RF) jammer, and an autonomous net-based interceptor drone into a layered defense platform. Field trials were performed against representative small drones (Class 1 and Class 2 UAS targets, including both quadcopter and fixed-wing types) across various approach scenarios. Results: The system reliably detected UAS targets at distances up to 4 km, enabled visual identification at approximately 800 m, disrupted control links via RF jamming out to ~1.2 km, and achieved an interception success rate exceeding 85% in live engagements. These findings provide insight into each component’s performance in a layered defence and offer operational recommendations to inform future C-UAS deployments in military and high-value security environments.
References
Robin Radar Systems, “Lessons learned from the Gatwick drone incident,” Robin Radar Systems Blog, Jun. 2, 2020. Available: https://www.robinradar.com/blog/lessons-from-the-gatwick-airport-drone-incident
G. De Cubber, S. S. Remes, and T. van der Voort, “Standardized evaluation of counter-drone systems: Methods, technologies, and performance metrics,” Drones, vol. 9, no. 5, Art. no. 354, May 2025. Available: https://www.mdpi.com/2504-446X/9/5/354
V. U. Castrillo, A. Manco, D. Pascarella, and G. Gigante, “A review of counter-UAS technologies for cooperative defensive teams of drones,” Drones, vol. 6, no. 3, Art. no. 65, 2022. doi: https://doi.org/10.3390/drones6030065
M. Leonardi, G. Ligresti, and E. Piracci, “Drone classification by the use of a multifunctional radar and micro-Doppler analysis,” Drones, vol. 6, no. 5, Art. no. 124, 2022. Available: https://www.mdpi.com/2504-446X/6/5/124
Y. Zidane, J. S. Silva, and G. Tavares, “Jamming and spoofing techniques for drone neutralization: An experimental study,” Drones, vol. 8, no. 12, Art. no. 743, Dec. 2024. Available: https://www.mdpi.com/2504-446X/8/12/743
M. Vrba, D. Heřt, and M. Saska, “Onboard marker-less detection and localization of non-cooperating drones for their safe interception by an autonomous aerial system,” IEEE Robot. Autom. Lett., vol. 4, no. 4, pp. 3402–3409, Oct. 2019. Available: https://ieeexplore.ieee.org/document/8756100
Saab AB, “Giraffe 1X multi-mission radar – product sheet,” Apr. 2025. Available: https://www.saab.com/products/giraffe-1x
J. A. Besada et al., “Review and simulation of counter-UAS sensors for unmanned traffic management,” Sensors, vol. 22, no. 1, Art. no. 189, 2022. Available: https://www.mdpi.com/1424-8220/22/1/189
X. Zhao, W. Zhang, and F. Wang, “Intelligent sensor fusion for low-altitude target recognition in urban environments,” Sensors, vol. 23, no. 3, Art. no. 1117, Feb. 2023. doi: 10.3390/s23031117
E. Basan, O. Makarevich, M. Lapina, and M. Mecella, “Analysis of the impact of a GPS spoofing attack on a UAV,” in Proc. Int. Workshop Advances in Information Security Management and Applications (AISMA 2021), Stavropol–Krasnoyarsk, Russia, Oct. 1, 2021. Available: https://ceur-ws.org/Vol-3094/invited_paper.pdf
M. Vrba and M. Saska, “Marker-less micro aerial vehicle detection and localization using convolutional neural networks,” IEEE Robot. Autom. Lett., vol. 5, no. 2, pp. 2459–2466, Apr. 2020. Available: https://www.researchgate.net/publication/339168653
M. Pliska, M. Vrba, T. Báča, and M. Saska, “Towards safe mid-air drone interception: Strategies for tracking and capture,” IEEE Robot. Autom. Lett., vol. 9, no. 10, pp. 8810–8817, Oct. 2024. Available: https://arxiv.org/abs/2405.13542
T. Zhang, R. Lu, X. Yang, X. Xie, J. Fan, and B. Tang, “UAV hunter: A net-capturing UAV system with improved detection and tracking methods for anti-UAV defense,” Drones, vol. 8, no. 10, Art. no. 573, 2024. Available: https://www.mdpi.com/2504-446X/8/10/573
J. Rothe, M. Strohmeier, and S. Montenegro, “Autonomous multi-UAV net defense system for aerial drone interception,” Proc. 10th Int. Conf. Control, Robot. Eng. (ICCRE), May 2025, pp. 1–6. Available: https://www.researchgate.net/publication/394181425
NATO Science and Technology Organization, Countering Unmanned Air Systems: C-UAS Concepts and Technologies, Tech. Rep. STO-TR-SET-260, 2023. Available: https://www.sto.nato.int
V. Semenyuk, I. Kurmashev, A. Lupidi, D. Alyoshin, L. Kurmasheva, and A. Cantelli-Forti, “Advances in UAV detection: Integrating multi-sensor systems and AI for enhanced accuracy and efficiency,” Int. J. Crit. Infrastruct. Protect., vol. 49, Art. no. 100744, 2025. Available: https://www.sciencedirect.com/science/article/pii/S1874548225000058
Q. Wu, J. Chen, Y. Lu, and Y. Zhang, “A complete automatic target recognition system of low-altitude, small RCS and slow-speed targets based on multi-dimensional feature fusion,” Sensors, vol. 19, no. 22, Art. no. 5048, Nov. 2019. Available: https://pubmed.ncbi.nlm.nih.gov/31752410/
S. Jovanoska, M. Brötje, and W. Koch, “Multisensor data fusion for UAV detection and tracking,” Proc. 19th Int. Radar Symp. (IRS), Bonn, Germany, Jun. 2018, pp. 1–10. Available: https://www.researchgate.net/publication/327324171
J. Dudczyk, R. Czyba, and K. Skrzypczyk, “Multi-sensory data fusion in terms of UAV detection in 3D space,” Sensors, vol. 22, no. 12, Art. no. 4323, Jun. 2022. Available: https://www.mdpi.com/1424-8220/22/12/4323
Y. Zidane, J. S. Silva, and G. Tavares, “Jamming and spoofing techniques for drone neutralization: An experimental study,” Drones, vol. 8, no. 12, Art. no. 743, Dec. 2024. Available: https://www.mdpi.com/2504-446X/8/12/743
M. Aledhari, R. Razzak, R. M. Parizi, and G. Srivastava, “Sensor fusion for drone detection,” Proc. IEEE 93rd Veh. Technol. Conf. (VTC2021-Spring), Helsinki, Finland, Apr. 25–28, 2021, pp. 1–7, doi: https://doi.org/10.1109/VTC2021-Spring51267.2021.9448699