Abstract
Unmanned traffic management (UTM) systems rely on collaborative position reporting to track unmanned aerial system (UAS) operations over wide unsurveilled (with counter-UAS systems) areas. Many different technologies, such as Remote-ID, ADS-B, FLARM, or MLAT might be used for this purpose, in addition to the direct exploitation of C2 telemetry, relayed though cellular networks. This paper provides an overview of the most used collaborative sensors and surveillance systems in this context, analyzing their main technical parameters and performance effects. In addition, this paper proposes an abstracted general statistical simulation model covering message encoding, network capacity and access, sensors coverage and distribution, message transmission and decoding. Making use of this abstracted model, this paper proposes a particularized set of simulation models for ADS-B, FLARM and Remote-Id; it is thus useful to test their potential integration in UTM systems. Finally, a comparative analysis, based on simulation, of these systems, is performed. It is shown that the most relevant effects are those related with quantification and the potential saturation of the communication channels leading to collisions and delays.
Highlights
Unmanned aerial vehicles (UAVs), known as drones, are becoming increasingly widespread in our societies due to their affordability, ease of use and the competitive advantage they provide for some applications [1]
Some error correction algorithms such as convolutional Forward error correction (FEC) correct errors within aAs bitstream and not in an isolated we model algorithm, the error correcting capacitythat of a the given all protocols include an manner, error detection we assume probabil protocol protocol as a maximum number of correctable errors: N
This consists of a distributed, agent-based modelling framework that allows one to replicate the input information required by Unmanned traffic management (UTM) systems for their operation. It allows for evaluating UTM systems without requiring real tests by simulating the behavior and interactions of relevant actors, such as pilots, ground control system (GCS), drones, surveillance networks and communication networks, that are individually modelled
Summary
Unmanned aerial vehicles (UAVs), known as drones, are becoming increasingly widespread in our societies due to their affordability, ease of use and the competitive advantage they provide for some applications [1]. The European commercial drone fleet is expected to grow rapidly according to the European Drones Outlook Study [6]. 400,000 commercial vehicles and 7 million recreational hulls will be operational in Europe by 2050. Unmanned traffic is expected to become prevalent in the low-level and very low-level airspaces. Their expansion creates safety concerns including in-air incidents with manned aviation or flights over unauthorized areas (e.g., people gatherings, sensitive locations)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
