MP-SET-315-26.pdf — page 9

A Novel Swarm Defense End-to-End System 
for Autonomous Drone Detection, Tracking, and Neutralization 
STO-MP-SET-315-26 
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First, the AI agents are all communicating with each other, exchanging information pertaining to their 
perception of the situation they are facing. The perception that an AI agent has of the current situation is 
elaborated through the processing of the data captured by its platform sensors (the embedded gimbal-
mounted EO for a drone’s AI agent or the ground radar and the ground PTZ for the GCS’s AI agent) as well 
as through the fusion of the resulting information and the information shared by the other AI agents. Through 
this process, we ensure that all AI agents have the same perception of the global situation with only a delta 
due to processing latency. 
Second, our AI agents embed Multi-Agent Reinforcement Learning (MARL) algorithms to learn how to 
execute some specific decision-making functions (e.g., the navigation or the targeting) as a team. The policy 
that each AI agent learns embeds intrinsically the collaboration model enabling the swarming behavior at the 
system level since they were trained to execute the related function together. 
Our system’s interlinking communications between the swarm drones, the sensors and the GCS, allows for a 
seamless and coordinated response to any UAV threats, thereby upholding and protecting the restricted areas. 
5.0 RESULTS IN SIMULATED AND REAL-FLIGHT OPERATIONS. 
5.1 
Simulations Results 
In the simulated environment provided by SE-Star, we executed the four different use cases. Our C-UAV 
system displayed distinct stages from the monitoring up to the capture of five intruders. 
1) Monitoring and Detection: Initially, after arming and take-off, the system was in an idle state, 
waiting for intrusions. Not until the appearance of unknown drones detected by the radar did it 
trigger actions.  
2) Intrusions: Upon entering the buffer zone, unidentified drones were recognized as threats, causing 
our system to respond. The defense drones initiated interception paths to the intruders, and if 
multiple intruders were within the buffer zone, the defense drones would split for separate targets. 
3) System Engagement: If met with conditions confirming shortest distance between defending drone 
and target, alongside the availability of neutralization payloads (i.e., a net), the system transitions to 
a “ready to neutralize mode” waiting for further approval. This process prevents any redundant 
capture attempts. The approval is meant to be triggered by a human operator for future 
developments. In our trials, the approval was granted after an arbitrary delay representing the 
operator response time as the approval interface mechanism has not been implemented. 
4) Capture and release: Once approved, the drone deploys its net to proceed with the capture, 
following which it transports the captured drone to a retrieval zone. 
On all use cases, all the drones of the intrusion swarm were successfully intercepted, tracked and captured 
before they could reach the protected area. 
5.2 
Real Flight Operations Within Hybrid Environments 
Real-world experimentations were conducted in a hybrid environment where all the BlueSwarm system’s 
and red asset’s hardware components were live except for the neutralization payloads, which were simulated. 
In order to be able to conduct complete missions, the system was mirroring the behavior of all real drones 
into SE-Star up until a neutralization phase was engaged. Once a neutralization phase was engaged, the 
behavior between the real drones engaged in that phase and their simulated counterparts diverged in order to 
allow for the neutralization to happen in the simulated world while maintaining safe conditions in the real 
world, to eliminate any collision risk during actual captures.