AI as a Tool in Drone Detection – The NAVISP program’s MEDuSA project is tackling the rising issue of intrusive drones by looking skyward. The problem of drones accidentally or intentionally intruding into sporting events, ports, and critical infrastructure is on the rise. A prominent example occurred in December 2018 when Gatwick Airport in the UK had to be closed for three days, leading to the cancellation of numerous flights due to repeated drone sightings near airport runways.

MEDuSA introduces an innovative radar-based approach that can detect drones in all weather conditions and estimate their trajectories. This approach utilizes GNSS signals as the radar signal source of opportunity for sensors to detect drones within the area of interest. It particularly leverages Galileo signals known for their exceptional stability and incorporates the added-value Open Service Navigation Message Authentication service to enhance robustness and protect against spoofing attacks. MEDuSA’s sophisticated algorithms employ ‘forward scattering detection,’ which detects slight signal phase anomalies caused by the passage of drones.

To further enhance the system’s capabilities, Machine Learning (ML) techniques are employed in combination with predictive ‘Kalman filters.’ This ML-driven data analysis allows the derivation of the drone’s onward trajectory, enabling timely alarms and appropriate countermeasures to be deployed, effectively addressing the drone intrusion problem.