To fully harness the capabilities of Advanced Air Mobility, aircraft need to autonomously execute missions.
A critical barrier to achieving this lies in efficiently managing non-cooperative aircraft. A crucial aspect of non-cooperative traffic management involves automatically detecting, tracking, and estimating the distance of airborne objects. This is essential for identifying potential mid-air collisions and supplying the requisite data to avoidance systems.
In addressing this challenge, we have developed the NEFELI system—a Machine Learning solution designed for the real-time identification and collision estimation of non-cooperative aircraft.
