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MEFA: Mapping and Enabling Future Airspace

MEFA：绘制和启用未来空域

基本信息

批准号：
EP/T011068/1
负责人：
Michail Antoniou
金额：
$ 112.04万
依托单位：
University of Birmingham
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FT011068%2F1
关键词：
MEFA Mapping Enabling Future Airspace

项目摘要

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.

载人和无人空域正在经历一场革命。到 2030 年，空中交通量预计将翻两番，有人驾驶飞机和无人机 (UAV) 总数将增加一倍。这种爆炸性增长将改变和拥挤已经被大量使用的空域。无人机占据空域的方式与鸟类类似，两者的飞行高度和速度都重叠。因此，正如最近盖特威克机场的无人机入侵事件所证明的那样，迫切需要能够将无人机与使用同一空域的自然生物（例如鸟类）区分开来。关于鸟类如何使用空域的详细数据有限，特别是考虑到前所未有的城市化速度，其特点是高层建筑不断增加、人造光 (AL) 增加以及基础设施模式不断变化。所有这些都在迅速重塑迁徙和非迁徙物种所使用的栖息地。建筑基础设施与 AL 之间的相互作用及其对鸟类生物学的影响，现在是解决迁徙生态学（尤其是鸟类迁徙生态学）以及明亮的城市结构（例如纪念碑、建筑物、通讯塔）造成的死亡问题的研究重点。高层建筑越来越多地使用玻璃和其他高反射表面，增加了鸟击的频率，从而增加了鸟类的死亡率。 2004 年，英国鸟类学信托基金 (BTO) 估计英国每年有 1 亿只鸟撞上窗户。该项目主要使用专门为跟踪无人机而开发的“凝视”形式的雷达传感器。与之前的雷达研究相反，单个鸟类和无人机可以在小群体中进行观察，从而可以比以前更精细地测量轨迹。然而，为了对受控无人空域进行足够可靠的监视，根本的挑战是区分小型无人机和鸟类。鸟类具有与无人机不同的特定飞行模式。该研究将开发算法来区分无人机和鸟类、小群体（通常是 2-5 只）中的个体鸟类以及较大群体中的潜在个体鸟类。将开发和测试深度学习算法区分鸟类和无人机以及不同鸟类群体的能力。该项目贯穿 EPSRC 的“与环境变化共存（生态系统挑战）”和“全球不确定性（对基础设施的威胁）”的主题，开发一个能够同时减轻鸟类和人类安全风险的尖端系统。