Reducing the risk of conflict between airspace users becomes more important as more drones enter the airspace. The Clear Air Situation for uaS (CLASS) project examined the potential of ground-based technologies to detect and monitor cooperative and non-cooperative drone traffic in real-time. The consortium fused surveillance data obtained using a drone identifier and tracker, and holographic radar, to feed a real-time UTM display.
CLASS tested tracking and display of cooperative and non-cooperative drones in six operational scenarios, ranging from an out-of-control leisure drone, conflicts with emergency operations, and incursions by rogue drones. Various scenarios were carried out by project partners to benchmark the surveillance and data fusion technology and achieve the lowest rates of false alarms. The functionalities provide the basis for a real-time centralised UTM system, which can be used by all stakeholders, from drone operators to air navigation service providers, authorities and airports. The functionalities were also designed to support advanced services, such as geo-fencing (where the drone pilot is warned automatically if he trespasses into an unauthorised zone), geo-caging (where the drone pilot is warned that he is leaving a pre-defined zone), conflict detection and resolution.
As a result of the demonstrations, CLASS was able to define and detail the functional and technical requirements for tracking, monitoring and tactical deconfliction.
For example, tracking requirements vary from statically managed to dynamically managed airspace where
real-time decisions are necessary because of conflict, or new dynamic geo-fenced volumes. CLASS also found variations in the performance of tracking technology and recommended the drawing up of standards for different U-space services. For example, there is a difference between tactical deconfliction services and on-board detect and avoid systems, which means these must operate effectively to manage the wide range of drone types and sizes.
Further research is recommended to scale up the operational scenarios to simulate surveillance in denser environments, initially involving tens of drones.
