MP-SET-315-26.pdf — page 6

A Novel Swarm Defense End-to-End System 
for Autonomous Drone Detection, Tracking, and Neutralization 
6 
STO-MP-SET-315-26 
 
 
2.2 
Organization of Mission Phases 
2.2.1 
Detection, classification, localization and threat level assessment 
The ground radar acts as an early warning sensor, detecting, classifying and localizing potential incoming 
threats at long range. Within the frame of our project, for real world demonstration, the radar sensor we used, 
was giving a detection range of up to 1,000 meters. The ground PTZ camera is the second sensor to be put in 
motion. Its purpose is to help confirm the nature and number of threats inbound thanks to computer vision. 
The GCS AI agent controls that sensor in pan, tilt and zoom, cued by the radar data.  
The GCS AI agent also assesses the level of threat of the detected objects, assigning labels such as enemy, 
friendly or neutral, by information processing. The threat assessment process considers multiple aspects like 
size, speed, flight pattern, and radar cross-section of the intruding object. This process of detection, 
classification, localization and threat level assessment is not only the first step for deploying the defending 
swarm, but a continuous one throughout a mission. 
2.2.2 
Defending Swarm Deployment and Approach 
Upon detection of an “enemy” (i.e., entity with an assigned enemy label), the system autonomously deploys 
the defending drones whose objectives are to approach, track and neutralize the threat(s). These drones move 
as a swarm towards the identified threat, with each drone being independently controlled by its own AI 
agent, constantly adapting to the mission phase it is engaged in. Each AI agent controls four interdependent 
decision-making functions. First, the Navigation function, allowing that AI agent to control the trajectory of 
its drone. Second, the Sensing function, giving control of the embedded EO sensor in pan, tilt and zoom to 
the AI agent. Third, the Neutralization function, letting the AI agent decide when to trigger its neutralization 
payload (i.e. its net). Last, the Targeting function, for the AI agent to assign itself a target drone. Those 
functions are realized thanks to decision-making algorithms that the AI agent orchestrates. 
More specifically, each AI agent can switch between different decision-making algorithms for its Navigation 
function, depending on mission phases. To boost a collaborative behavior among the swarm, the Navigation 
policy used for approaching and tracking phases was learned by a Multi-Agent Deep Reinforcement 
Learning algorithm in order for those phases to be fast, coordinated and adaptable. However, the navigation 
during neutralization must be controlled, precise and explainable, thus a rule-based algorithm is preferred in 
our system.  
For the Targeting function, the algorithm used ensures optimal allocation of targets among drones, reducing 
efficiency loss due to potential conflicts or repetition of tasks. For instance, the algorithm computes the best 
allocation by reducing the collaborative distance towards the available targets ensuring, and correcting, if 
necessary, so that no target is left within the buffer zone without being tracked by a drone of the defending 
swarm. This rule could only be broken if the number of intruders is higher than the number of drones of the 
defending swarm.  
2.2.4 
Tracking 
Once the defending drones have reached the enemy drones, they enter a tracking phase. Depending on the 
number of enemy drones, one or more defending drones could be assigned to each enemy drone. The 
tracking is relying on a combination of radar and visual data as well as shared information among the swarm. 
A defending drone shall track its assigned target as long as it is not entering into a neutralization phase or as 
long as that target has not either exited the buffer zone or been neutralized.