Air Traffic Management data systems today are inefficient and not scalable to enable future unmanned systems. Current data is fragmented, siloed, and not easily accessible. There is data conflict, misuse, and eroding levels of trust in provenance and accuracy. With increased autonomy in aviation, Artificially Intelligent (AI) enabled unmanned traffic management (UTM) will be more reliant on secure data from diverse stakeholders. There is an urgent need to develop a secure network that has trustworthy data chains and works with the requirements generated by UTM. Here, we review existing research in 3 key interconnected areas: (1) blockchain development for secure data transfer between competing aviation stakeholders, (2) self-learning networking architectures that distribute consensus to achieve secure air traffic control, (3) explainable AI to build trust with human stakeholders and backpropagate requirements for blockchain and network optimisation. When connected together, this new digital ecosystem blueprint is tailored for safety critical UTM sectors. We motivate the readers with a case study, where a federated learning UTM uses real air traffic and weather data is secured and explained to human operators. This emerging area still requires significant research and development by the community to ensure it can enable future autonomous air mobility.

翻译：空中交通管理数据系统目前效率低下，且无法扩展以支持未来的无人系统。当前数据分散、孤立，不易获取。存在数据冲突、滥用以及数据来源和准确性信任度持续下降的问题。随着航空自主性的提升，基于人工智能的无人交通管理（UTM）将更依赖来自不同利益相关方的安全数据。亟需开发一种具有可信数据链并能满足UTM需求的安全网络。本文回顾了三个关键互连领域的研究现状：（1）用于竞争性航空利益相关方之间安全数据传输的区块链技术；（2）通过分布式共识实现安全空中交通控制的自主学习网络架构；（3）可解释人工智能以建立与人类利益相关方的信任，并反向传播区块链与网络优化的需求。将三者结合，这一新型数字生态系统蓝图专为安全关键型UTM领域量身定制。我们通过一个案例研究激发读者兴趣：该案例中使用联邦学习UTM结合真实空中交通与天气数据，实现数据安全并向人类操作员提供解释。这一新兴领域仍需学术界投入大量研发，以确保其能够赋能未来的自主空中交通。