# Sensors and Communication Simulation for Unmanned Traffic Management
###### Abstract
Contrary to the aforementioned sensors, the detection of an aircraft by means of cooperative sensors requires the presence of compatible equipment on the aircraft (cooperative drones) that, being active, periodically and automatically transmits its position. Within this group we find the ADS-B system (automatic dependent surveillance-broadcast) [15], widely used in commercial aviation. Moreover, a similar system designed for small aircraft and with a shorter range called FLARM system [16] may be used. Finally there is also the possibility to receive drones positioning through the Remote ID standard [17], part of the UTM functionalities, that allows the drone's position broadcasting via WiFi, Bluetooth, and cellular communications. Although it does not require the presence of sensors, periodic sending of telemetry to the UTM system over public communication networks could also be considered as a cooperative surveillance system. From all the above, it can be concluded that the operation of UTM systems depends on having ubiquitous, reliable, and low latency connectivity between all actors. The availability and integrity of communications and drone operations are quite often tightly interrelated. For instance, as a drone moves away from a communications node, connectivity might be interrupted prompting the drone to halt its predefined flight landing or returning to a known location. We can distinguish three communication flows. First, there are air-ground communications (also known as C2 communications) that allow pilots to control and monitor drone operations from a ground control station (GCS). The second communication flow refers to the data exchange (telemetry and alerts) with UTM systems and it can be performed directly from the drone or from the GCS. These communications can be established using a wide variety of networks [18] from satellite networks (on large drones flying at high altitude), public mobile communication networks (for which special extensions are being developed [19]), ad-hoc Wi-Fi networks, long range IoT (internet of things) technologies such as ZigBee, to custom radio technology and specific communication protocols. Finally, vehicle-to-vehicle (V2V) connectivity is expected to become a key enabler of advanced detect and avoid (DAA) technologies [20] that would enable high density flight in urban areas. In order to develop UTM systems with the aim to ensure the highest safety and security requirements, a comprehensive set of tests and evaluations is needed. These tests cannot only focus on drone mechanics to predict its trajectory but need to study several aspects: pilot actions and control through GCSs, drone navigation and control systems, communications between the different actors in the operation, and sensors networks. Moreover, it is not enough to analyze each aspect independently because, as mentioned before for communications, all of them are highly interdependent and emergent behaviors may occur with slight changes in one of them. The most common way to evaluate the overall behavior of UTM systems is to perform live experimentation in scenarios that allow to generate the necessary information to excite the system. However, it is not possible to perform real tests at the scale that UTM systems are expected to operate, with hundreds or thousands of aircraft flying simultaneously. This would entail a very high economic cost, in addition to its difficulty as these tests are subject to meteorological variability, to the availability of equipment and personnel, and to the human factor. Furthermore, actual tests involving validation of safety in critical and emergency situations can lead to serious risk for equipment and people. For example, it will be necessary to evaluate the collision detection and avoidance algorithms between drones and manned aircraft, and this could require putting the aircraft at serious collision risk, which would not be acceptable.