Can drones fill our skies safely?

Urban Air Mobility revolution is expected to crowd the airspace, especially over cities, with a large variety of unmanned operations, from goods delivery to air taxis, but also infrastructure inspection, etc.. As reported in our previous blog, by 2035, our skies will be at least ten times busier than today.

However, such a revolution will only be possible if these operations are safe enough, not creating unacceptable risks for uninvolved people on the ground or for people on board manned or unmanned aircraft. But the huge number of simultaneous operations expected, will boost the collision risk, unless adequate technologies prevent drones from getting too close to other drones, manned aircraft and obstacles.

AMU-LED, among several Project, is dealing with this problem. The initial Operational Safety Analysis, available in our website, identifies the main requirements that will have to meet the CNS infrastructure and the limitations of the existing solutions.

  • Communications are essential in UAM operations: on the one hand, most operations will be BVLOS, therefore a robust and low-latency link is required to be able to command and control the UAS remotely, to cope with potential contingencies; on the other hand, network e-Identification (i.e. drone position reporting) will be the main tracking source of the U-space services, in order to detect potential conflicts and monitor traffic, so communications technologies will need to be fast enough and assure a minimum continuity of service level. As most drone operations will fly in VLL (Very Low Level), traditional aviation communication networks cannot meet these requirements, so alternative solutions are required, being mobile networks the most adequate candidate, considering their existing and expected deployment; however, although 4G networks meet most of the requirements, they can present limitations in terms of continuity of service and even latency in some cases, which might do necessary to be connected to two independent service providers.
  • Navigation accuracy is essential to follow the intended route and to keep a safe distance against obstacles (e.g. buildings) and other vehicles. The majority of the drones, not to say all, use GNSS receivers which provide a good accuracy for most operations, but on top accuracy, integrity monitoring algorithms are required to detect gross errors in GNSS data; and unfortunately, common drone receivers often do not include EGNOS or another integrity technique. Additionally, Urban Canyons will introduce masking areas and create multipath conditions that threaten the accuracy of the GNSS constellations.
  • Surveillance data will feed the Conformance Monitoring and GeoAwareness services with real time drone positions, to detect and prevent conflicts; however, as explained, the main surveillance source will be e-Identification, so if a drone receiver is not working properly, the drone will not know its real position and neither will do the Monitoring service, being a common cause of failure. That is why, in case of high drone density, independent surveillance solutions (i.e. solutions that calculate the position of the drones based on their own calculations and not on the position reports) will be required to assure adequate separation even in case of contingency. However, ATC surveillance sensors are not applicable for drone VLL operations.

To cope with these problems, in the AMU-LED demonstrations, a number of CNS technologies will be tested to characterize the minimum equipment needed and the level of service achievable in real-life operations. As said, communications fades, low precision positioning and other CNS anomalies will determine the separation minima required for a safe operation. That is why AMU-LED will use, as a basis, consolidated CNS solution to support common operations, but will also test state-of the art technologies to mitigate the aforementioned limitations:

  • 5G networks will provide reliable low latency communications, but additionally, will be tested to calculate drone positions based on the signal strengths received by different cells of the network, providing and independent tracking source.
  • An holographic radar will also be tested to provide non collaborative independent drone position.
  • Satellite communications will supplement the C2 link of eVTOL air taxis in some demos, to provide a robust communication solution supplementing mobile networks based C2.
  • GNSS navigation receivers for High Performance Vehicles will be multiconstellation (GPS/Galileo) and include EGNOS signals to guarantee integrity monitoring.

Therefore, AMU-LED will provide a good sample of results of the application of new CNS technologies, in order to increase drone operations’ safety.

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