MEFA: Mapping and Enabling Future Airspace

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Manned and unmanned airspace is undergoing a revolution. By 2030 air traffic is estimated to quadruple with a doubling of the total number of manned aircraft and unmanned air vehicles (UAVs). This explosive growth will change and congest already heavily used airspace. UAVs occupy airspace in a similar way to birds with both flying at overlapping altitudes and velocities. Therefore, as evidenced by the recent drone incursions at Gatwick airport, there is a pressing need to be able differentiate UAVs from natural organisms (e.g. birds) that use the same airspace. There are limited detailed data on how birds use airspace, especially in light of unprecedented rates of urbanisation, characterised by increasing high-rise building, increased artificial light (AL), and changing patterns of infrastructure. All are rapidly re-shaping habitats used by migratory and non-migratory species.The interaction between built infrastructure and AL, and its influence on bird biology, is now the focus of research addressing migration ecology, especially of birds, and mortality caused by brightly lit urban structures (e.g. monuments, buildings, communication towers). Increased use of glass and other highly reflective surfaces on high-rise buildings has increased the frequency of bird strikes and thus bird mortality. In 2004, the British Trust for Ornithology (BTO) estimated 100 million birds struck windows each year in the UK.This project primarily uses a 'staring' form of radar sensor developed specifically to track drones. Contrary to previous radar research, individual birds and drones are observable within small groups that allows finer measurement of trajectories than has been achieved previously. However, for sufficiently reliable surveillance of controlled unmanned-airspace, the fundamental challenge is to discriminate small drones from birds. Bird species have specific flight patterns that are distinguishable from those of UAVs. The research will develop algorithms to distinguish between drones and birds, individual birds in small groups (typically 2-5) and potentially individual birds in larger flocks. Deep learning algorithms will be developed and tested for their ability to distinguish between birds and drones, and between different bird groups. The project cuts across the EPSRC's themes of "Living with Environmental Change (ecosystem challenge)" and "Global Uncertainties (threats to infrastructures)", to develop a cutting-edge system with the ability to simultaneously mitigate security risks to birds and humans alike.