Uncrewed Aircraft System Traffic Management | Technology focus There is also an issue around data privacy and awareness. With multiple operators, the UTM has to allow an operator to see its UAVs, but to only see other UAVs when there is a conflict as this is potential business intelligence about a competitor’s flight. This might use geoawareness areas – a polygon with altitude, dimensions – for a no-fly zone. This is already accounted for in the standard, but the UTM has to implement it in a way that is not abused. The central server runs an instance of Airborne Collision Avoidance System X (ACAS X) for each UAV, which provides appropriate collision avoidance for that particular aircraft. This is an onboard flight safety system that is designed to replace the current Traffic Alert and Collision Avoidance System II (TCAS) used in crewed aircraft. Surveillance systems ACAS X is fully compatible with the current airspace procedures and technologies of the FAA’s Next-Generation Air Transportation System, which aims to reduce gridlock in the sky and at airports with new methods of surveillance and navigation. ACAS X detects nearby aircraft by receiving sensor measurements from onboard surveillance systems, and it estimates their relative position and speed by using tracking algorithms. The system then weighs the costs of all the actions that the pilot could take and decides on the single best action. If a collision avoidance alert is necessary, ACAS X will send this information directly to the pilot via the flight-deck display. A version of this system, called ACAS Xu, is tailored for use on larger, uncrewed aircraft that are equipped with collision avoidance protection, and this will provide unrestricted access to the National Airspace System for the very first time. One of the largest UAV operations has an onboard perception system with ADS-B transponders that identify aircraft in the nearby airspace, as well as an acoustic avoidance system that uses small, lightweight microphones to detect and avoid other aircraft flying up to two miles away in all directions, including during the night and in challenging weather. This uses an m:N operation (explained in our earlier In Conversation feature, p.20), where a small number of humans (m) manage many autonomous vehicles (N) to transport medical supplies and consumer goods in Arkansas, USA, Japan, and Ghana and Rwanda in Africa. A working group is considering a variety of use cases for n:M operations, and addresses barriers such as technical, regulatory, safety assurance and community acceptance. A central goal of this working group is to bring together a broad collective of interested stakeholders from government, industry and academia to identify and reduce barriers to m:N operations; an operational configuration that envisions a ratio of multiple operators (m) controlling multiple vehicles (N) between them. The barriers addressed by the working group range from technical, regulatory and safety assurance to community acceptance. Identified barriers are considered across a variety of multivehicle control contexts (e.g. urban/ advanced air mobility, delivery, infrastructure inspection, disaster response and recovery, and high-altitude pseudo-satellite operations), and they form the basis for future research to confront operational, technical and regulatory gaps. Sense and avoid Radar on the UAV can also be used for sense and avoid. Recent tests in Phoenix, Arizona, USA, showed the radar can not only detect airborne traffic but can also decide autonomously on a course of action. The radar can take over navigation and pilot an aircraft to safety using its onboard processor. Avoiding unforeseen objects is a key requirement for autonomous drones and other aircraft that fly beyond visual line of sight (BVLOS) of an operator. However, this detect-and-avoid capability is extremely difficult in the air. Radars must have long ranges because of the high speeds 45 Uncrewed Systems Technology | August/September 2024 The AeroBT airborne radar can be mounted on a UAV (Image courtesy of Honeywell) ACAS X detects nearby aircraft by receiving sensor measurements… and estimates their relative position and speed by using tracking algorithms
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