Performance enhancement of low-cost INS/GNSS navigation system operating in urban environments

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dc.contributor.authorOzdemir, Yunus Emre
dc.contributor.authorIsik, Oguz Kagan
dc.contributor.authorGeragersian, Patrick
dc.contributor.authorPetrunin, Ivan
dc.contributor.authorGrech, Raphael
dc.contributor.authorWong, Ronald
dc.date.accessioned2023-03-02T16:38:05Z
dc.date.available2023-03-02T16:38:05Z
dc.date.issued2023-01-19
dc.description.abstractAs a result of the increasing usage of UAVs (Unmanned Air Vehicles) in urban environments for UAM (Urban Air Mobility) applications, the preciseness and reliability of PNT (Positioning, Navigation and Timing) systems have critical importance for mission safety and success. With its high accuracy and global coverage, GNSS (Global Navigation Satellite System) is the primary PNT source for UAM applications. However, GNSS is highly vulnerable to Non-Line-of-Sight (NLoS) blockages and multipath (MP) reflections, which are quite common, especially in urban areas. This study proposes a machine learning-based NLoS/MP detection and exclusion algorithm using GNSS observables to enhance position estimations at the receiver level. By using the ensemble machine learning algorithm with the proposed method, overall 93.2% NLoS/MP detection accuracy was obtained, and 29.8% accuracy enhancement was achieved by excluding these detected signals.en_UK
dc.identifier.citationOzdemir YE, Isik OK, Geragersian P, et al., (2023) Performance enhancement of low-cost INS/GNSS navigation system operating in urban environments. In: AIAA SciTech Forum 2023, 23-27 January 2023, National Harbor, Maryland, USAen_UK
dc.identifier.eisbn978-1-62410-699-6
dc.identifier.urihttps://doi.org/10.2514/6.2023-2241
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19257
dc.language.isoenen_UK
dc.publisherAIAAen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.titlePerformance enhancement of low-cost INS/GNSS navigation system operating in urban environmentsen_UK
dc.typeConference paperen_UK

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