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Open AccessArticle

GNSS Performance Modelling and Augmentation for Urban Air Mobility

School of Engineering, Royal Melbourne Institute of Technology University, Melbourne, VIC 3083, Australia
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This paper is an extension of conference paper “GNSS Performance Modelling for Positioning and Navigation in Urban Environments” published in 2018 5th IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace), Rome, Italy, 20–22 June 2018.
Sensors 2019, 19(19), 4209; https://doi.org/10.3390/s19194209
Received: 25 July 2019 / Revised: 23 September 2019 / Accepted: 23 September 2019 / Published: 27 September 2019
(This article belongs to the Special Issue Aerospace Sensors and Multisensor Systems)
One of the primary challenges facing Urban Air Mobility (UAM) and the safe integration of Unmanned Aircraft Systems (UAS) in the urban airspace is the availability of robust, reliable navigation and Sense-and-Avoid (SAA) systems. Global Navigation Satellite Systems (GNSS) are typically the primary source of positioning for most air and ground vehicles and for a growing number of UAS applications; however, their performance is frequently inadequate in such challenging environments. This paper performs a comprehensive analysis of GNSS performance for UAS operations with a focus on failure modes in urban environments. Based on the analysis, a guidance strategy is developed which accounts for the influence of urban structures on GNSS performance. A simulation case study representative of UAS operations in urban environments is conducted to assess the validity of the proposed approach. Results show improved accuracy (approximately 25%) and availability when compared against a conventional minimum-distance guidance strategy. View Full-Text
Keywords: Global Navigation Satellite System; error analysis; Urban Air Mobility; UAS Traffic Management Global Navigation Satellite System; error analysis; Urban Air Mobility; UAS Traffic Management
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Bijjahalli, S.; Sabatini, R.; Gardi, A. GNSS Performance Modelling and Augmentation for Urban Air Mobility. Sensors 2019, 19, 4209.

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