利用超宽带与机器学习实现机载客舱的精确定位
Precise Onboard Aircraft Cabin Localization using UWB and ML
AI总结：
本文针对飞机客舱中传统室内定位精度不足的问题，通过在真实客舱中开展测量活动评估基于超宽带的定位系统，并提出多种机器学习方法校正测距误差，最终实现了相对于飞机座位97%似然度的节点定位。
摘要：
精确的室内定位系统（IPS）对于在飞机生产、运营和维护过程中更高效地完成一系列任务至关重要。例如，IPS可以克服在飞机客舱中配置（无线）传感器节点这一繁琐任务。尽管已经提出并测试了基于成熟消费品技术（如蓝牙或WiFi）的各种解决方案，但已公布的精度结果使这些技术难以在实际应用场景中发挥作用。这源于定位所面临的挑战性环境，尤其是在飞机客舱中，这主要是由于几何结构、众多障碍物以及高反射材料所致。为了解决这些问题，我们提出在本文中通过在真实飞机客舱中开展的测量活动来评估基于超宽带（UWB）的IPS。我们首先通过研究所测得的信号传播效应，说明IPS在飞机客舱中所面临的困难。然后，我们研究了该IPS的测距和定位精度。最后，我们还引入了多种基于机器学习（ML）的方法来校正测距测量结果，并证明我们能够以实测97%的似然度相对于飞机座位对一个节点进行定位。
英文摘要：
Precise indoor positioning systems (IPSs) are key to perform a set of tasks more efficiently during aircraft production, operation and maintenance. For instance, IPSs can overcome the tedious task of configuring (wireless) sensor nodes in an aircraft cabin. Although various solutions based on technologies of established consumer goods, e.g., Bluetooth or WiFi, have been proposed and tested, the published accuracy results fail to make these technologies relevant for practical use cases. This stems from the challenging environments for positioning, especially in aircraft cabins, which is mainly due to the geometries, many obstacles, and highly reflective materials. To address these issues, we propose to evaluate in this work an Ultra-Wideband (UWB)-based IPS via a measurement campaign performed in a real aircraft cabin. We first illustrate the difficulties that an IPS faces in an aircraft cabin, by studying the signal propagation effects which were measured. We then investigate the ranging and localization accuracies of our IPS. Finally, we also introduce various methods based on machine learning (ML) for correcting the ranging measurements and demonstrate that we are able to localize a node with respect to an aircraft seat with a measured likelihood of 97%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
