MP-SET-315-26.pdf — page 1

STO-MP-SET-315-26 
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A Novel Swarm Defense End-to-End System for Autonomous Drone 
Detection, Tracking, and Neutralization 
Antoine Fagette, Junior Samuel Lopez Yepez, Stephanie Allogba, Jeremy Viel 
and Filipe Carvalhais Sanches 
Thales Digital Solutions Inc., 6650 Street Saint-Urbain, Suite 350, Montreal 
CANADA 
Guillaume Dorion-Racine 
ARA Robotics, 2260 Avenue Aird, Suite 204, Montreal 
CANADA 
Iraj Mantegh and Varun Mehta 
National Research Council of Canada, Montreal 
CANADA 
Miodrag Bolic 
University of Ottawa, Ottawa 
CANADA 
antoine.fagette@thalesgroup.com  
ABSTRACT  
As the usage of drones across various applications continues to grow, the security challenges associated 
with their unsanctioned operations in restricted zones become increasingly critical. To address these 
challenges, we present a novel approach for automatic and autonomous threat management that detects 
drone intrusions and executes real-time countermeasures. The proposed system, called BlueSwarm 
integrates sensor data, implements data communication among resources, and leverages artificial 
intelligence to operate a counter-attacking swarm of drones. This work was validated during simulated and 
real-world experiments, confirming its detection, tracking and neutralization capabilities.  
The proposed counter-UAV system integrates a ground segment and an air segment. The ground segment is 
composed of a ground control station, manned by an operator and acting as a Command & Control Center, 
alongside a ground radar and a PTZ camera for long-range detection, classification and localization of 
incoming threats. The air segment is composed of a fleet of five drones, each embedding an Electro-Optic 
sensor and a deployable net as neutralization payload. Those drones deploy autonomously as a swarm to 
intercept, track, assess and neutralize the intruding drones while keeping the human operator in the loop. 
Each drone is operated by its own AI agent working collaboratively with the others and with the human 
operator. The system is designed for scalability and adaptability. The number of ground sensors, their type 
and distribution in space can be adjusted depending on the needs and configuration of the space to protect. 
The number of defending drones and their payloads can be equally adapted. The system relies on machine 
learning techniques to detect, track and localize intruders, and to adapt to constantly evolving threats.  
We have studied, designed and implemented, in collaboration with subject matter experts, different use cases 
to test our system in simulation and during real-world demonstrations. Those use cases involve the intrusion 
of up to five enemy drones exhibiting patterns of careless, clueless and criminal operators, evolving as 
independent individuals or in swarm in simulations, and two enemy drones for real-world demonstrations.  
Each mission is organized in four distinct phases, ensuring a streamlined and efficient process from 
detection to neutralization: (i) ground sensor-based detection, classification and localization of potential 
threats, (ii) deployment of the defensive swarm of drones, (iii) assignment of each drone to individual