  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%.
