METSIS: Hyperlocal Wind Nowcasting for U-space

The METeo Sensors in the Sky (METSIS) project, funded by SESAR’s Engage knowledge transfer network, investigated the use of drones as an aerial wind sensor network for U-space applications. The concept aims to provide accurate, lowcost and hyperlocal wind nowcasts for drones using data collected by drones themselves and the Meteo-Particle Model ...

Aerial Robotics: State-based Conflict Detection and Resolution (Detect and Avoid) in High Traffic Densities and Complexities

Purpose of Review A lot of research into decentralised, state-based conflict detection and resolution, or detect and avoid algorithms has been executed. This paper explains the essential properties of state-based conflict detection and reviews the work in the context of applications for not only manned but also unmanned aerial vehicles, where...

Challenges with obstacle data for manned and unmanned aviation

The objective of this paper is to raise awareness about the significance of collecting and ensuring the quality of the obstacle data required for the safety of air navigation for both manned and unmanned aviation. This information could be of importance to geodetic, CityGML, 3D model and Building Information Management (BIM) community. With the...

Drone Information Service Requirements for U-Space

The work presented in this paper is part of the SESAR Horizon 2020 exploratory research project DREAMS, which analyses operational and technical aspects of drone Aeronautical Information Management (AIM) for Europe’s Unmanned Traffic Management system, U-Space. The main objective of DREAMS is to analyse the present and future needs of...