MP-SET-315-26.pdf — page 11

A Novel Swarm Defense End-to-End System 
for Autonomous Drone Detection, Tracking, and Neutralization 
STO-MP-SET-315-26 
11 
 
 
The system’s architecture provides significant scope for scalability and adaptability responding to specific 
logistics and threat profiles of protected areas. Ground sensor types, quantity, and spatial distribution can be 
tailored according to user requirements, as can the number and payloads of the counter-UAVs.  
One potential advancement could be the incorporation of an advanced visual and automatic threat level 
assessment capability. This would provide operators with a comprehensive understanding of imminent 
threats, reducing the risk for subjective errors whilst accelerating response time.  
Strengthened by its resilience against evolving threats, adaptability to requirements, and the integration of 
leading-edge AI technologies, our system addresses a critical gap in unmanned aerial security. By refining 
and incorporating the system into existing infrastructure, we aim to safeguard restricted areas whilst 
mitigating the associated risks of unmanageable drone technology.  
In line with NATO’s vision toward reinforced security measures against drone threats, this research and 
system development, undertaken in collaboration with subject matter experts, marks a significant advance in 
counter-UAV technologies for real-flight operations. The envisaged future improvements and applications 
promise new impetus for technology transfer and further research within the NATO S&T community. 
8.0 ACKNOWLEDGEMENTS 
This work was supported by the Canadian Safety and Security Program, which is led by Defence Research and 
Development Canada’s Centre for Security Science (DRDC CSS), in partnership with Public Safety Canada. 
The Canadian Safety and Security Program is a federally funded program to strengthen Canada’s ability to 
anticipate, prevent/mitigate, prepare for, respond to, and recover from natural disasters, serious accidents, crime 
and terrorism through the convergence of science and technology with policy, operations and intelligence. 
The team also wants to acknowledge the contribution of the ThereSIS laboratory, a Thales entity providing 
the SE-Star simulator, as well as LAS Thales Business Unit involved in the C-UAV domain for sharing their 
domain expertise and knowledge pertaining to net capture, making our simulated neutralization realistic. 
Last, the team would like to thank the Royal Canadian Mounted Police (RCMP) for its help and review on 
the definition of the use cases and the red team behavior models. 
9.0 REFERENCES 
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Aerial 
Vehicle 
Systems,” 
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IEEE 
Access, 
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8, 
pp. 
168671-168710, 
2020, 
doi: 
10.1109/ACCESS.2020.3023473. 
[2] Michel, A. H. (2018). Counter-drone systems. Center for the Study of the Drone at Bard College.  
[3] M. Germen, “Alternative cityscape visualisation: Drone shooting as a new dimension in urban 
photography”, Proc. Electron. Vis. Arts, pp. 150-157, Jul. 2016. 
[4] E. Kaufmann, M. Gehrig, P. Foehn, R. Ranftl, A. Dosovitskiy, V. Koltun, et al., “Beauty and the beast: 
Optimal methods meet learning for drone racing”, Proc. Int. Conf. Robot. Automat. (ICRA), pp. 690-
696, May 2019. 
[5] E. Kaufmann, A. Loquercio, R. Ranftl, A. Dosovitskiy, V. Koltun and D. Scaramuzza, “Deep drone 
racing: Learning agile flight in dynamic environments”, Proc. Conf. Robot. Learn. (CoRL), pp. 133-
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