MEFA: Mapping and Enabling Future Airspace

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.

Summary: By being able to differentiate birds from drones and different species of birds from one another this research will open new vistas on bird behaviour and the impact of urban developments on bird flight patterns, bird strikes, and mortality. It will create a monitoring system that is critical to the future commercial exploitation of the new unmanned airspace.

Societal Impact:
There is an acute need to understand how the complex land use configurations found in cities and the patterns in building form, design and function confine and structure urban airspaces, thereby constraining use by wildlife and unmanned aerial vehicles (UAVs). Crucially, planners and city engineers need better targeted information concerning the interaction between lighting and built infrastructure so the impact on wildlife can be minimized. Moreover, improved characterisation of the lighting infrastructure associated with different landcover and land use will help with future proofing through cycles of urban regeneration and change of use.

Commercial Impact:
A recent House of Commons briefing paper (CBP 7734, 31 August 2017) stated "Achieving the full and safe integration of drones (UAVs) into non-segregated airspace is the underlying policy objective of Government. To achieve this requires technology, which is not yet fully developed, for sensing and avoiding air traffic under all possible scenarios". Radar has been identified as a core component of a future Air Traffic Management (ATM) system that will have to cope with a quadrupling in the number of aircraft over the next 10-15 years. Currently, no radar sensors are able to detect UAVs and distinguish them from birds with a reliability that meets ATM safety standards. In addition, drone incursions at airports present a severe safety hazard as well as directly impacting their economic viability. Robust and reliable radar surveillance is a core component of any 24 hour, all-weather, counter-drone capability. Further, this dramatic change in airspace occupancy will also impact bird populations in unresolved ways. Therefore, this project will create an enhanced sensing capability to open up new commercial opportunities in line with EPSRC's prosperity outcomes while also providing answers to core emerging environmental questions.

Academic Impact and National Importance:
This research will benefit national and international researchers by establishing ways in which information is encoded into radar echoes enabling UAVs and birds to be differentiated. The core knowledge will be generic and applicable to other use cases such as for autonomous driving, vital-sign health monitoring of people as well as military and security applications etc. This project will also provide foundational information enabling design of future radar sensors for ATM/UTM in congested, low altitude skies. The UK will benefit through development of high-performing all-weather, day-night, sensors that can be sold across the world.

New Experts:
The research training component of this project will include internships in industry and leading laboratories so that the expertise is developed with an appreciation of exploitation requirements. The University of Birmingham and the University of Leicester will establish a team that will be expert in radar sensing, advanced signal processing for target recognition, bird measurements and behaviours. These skills will be exploited in the radar sensing industry, academia and government agencies such as the CAA, NATS, Government departments and the BTO.