Machine learning-based environment-aware GNSS integrity monitoring for urban air mobility

Simple item page
dc.contributor.authorIsik, Oguz Kagan
dc.contributor.authorPetrunin, Ivan
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2024-12-12T16:16:55Z
dc.date.available2024-12-12T16:16:55Z
dc.date.freetoread2024-12-12
dc.date.issued2024-11-19
dc.date.pubOnline2024-11-19
dc.description.abstractThe increasing deployment of unmanned aerial vehicles (UAVs) in urban air mobility (UAM) necessitates robust Global Navigation Satellite System (GNSS) integrity monitoring that can adapt to the complexities of urban environments. The traditional integrity monitoring approaches struggle with the unique challenges posed by urban settings, such as frequent signal blockages, multipath reflections, and Non-Line-of-Sight (NLoS) receptions. This study introduces a novel machine learning-based GNSS integrity monitoring framework that incorporates environment recognition to create environment-specific error models. Using a comprehensive Hardware-in-the-Loop (HIL) simulation setup, extensive data were generated for suburban, urban, and urban canyon environments to train and validate the models. The proposed Natural Gradient Boosting Protection Level (NGB-PL) method, leveraging the uncertainty prediction capabilities of the NGB algorithm, demonstrated superior performance in estimating protection levels compared to the classical methods. The results indicated that environment-specific models significantly enhanced both accuracy and system availability, particularly in challenging urban scenarios. The integration of environment recognition into the integrity monitoring framework allows the dynamic adaptation to varying environmental conditions, thus substantially improving the reliability and safety of UAV operations in urban air mobility applications. This research offers a novel protection level (PL) estimation method and a framework tailored to GNSS integrity monitoring for UAM, which enhances the availability with narrower PL bound gaps without yielding higher integrity risks.
dc.description.journalNameDrones
dc.description.sponsorshipThis study was funded by the Ministry of National Education of Turkey.
dc.identifier.citationIsik OK, Petrunin I, Tsourdos A. (2024) Machine learning-based environment-aware GNSS integrity monitoring for urban air mobility. Drones, Volume 8, Issue 11, November 2024, Article number 690
dc.identifier.eissn2504-446X
dc.identifier.elementsID559296
dc.identifier.issn2504-446X
dc.identifier.issueNo11
dc.identifier.urihttps://doi.org/10.3390/drones8110690
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23255
dc.identifier.volumeNo8
dc.languageEnglish
dc.language.isoen
dc.publisherMDPI
dc.publisher.urihttps://www.mdpi.com/2504-446X/8/11/690
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject4605 Data Management and Data Science
dc.subject46 Information and Computing Sciences
dc.subjectMachine Learning and Artificial Intelligence
dc.subject11 Sustainable Cities and Communities
dc.subject40 Engineering
dc.subject46 Information and computing sciences
dc.titleMachine learning-based environment-aware GNSS integrity monitoring for urban air mobility
dc.typeArticle
dcterms.dateAccepted2024-11-18

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Machine_learning-based_environment-aware-2024.pdf
Size:
2.39 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Plain Text
Description:
Download

Collections

Staff publications (SATM)