Precise Onboard Aircraft Cabin Localization using UWB and ML

TL;DR

This paper evaluates UWB-based indoor positioning in aircraft cabins, addressing environmental challenges with ML corrections, achieving 97% localization accuracy, and demonstrating practical applicability for aircraft maintenance and operations.

Contribution

It presents a comprehensive measurement campaign in real aircraft cabins and introduces ML methods to improve UWB localization accuracy in challenging environments.

Findings

UWB signals are significantly affected by cabin geometry and materials.

ML techniques can effectively correct ranging errors in aircraft cabins.

Achieved 97% likelihood of accurate node localization relative to aircraft seats.

Abstract

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%.