A machine learning based GNSS performance prediction for urban air mobility using environment recognition

Simple item page
dc.contributor.authorIsik, Oguz Kagan
dc.contributor.authorPetrunin, Ivan
dc.contributor.authorInalhan, Gokhan
dc.contributor.authorTsourdos, Antonios
dc.contributor.authorMoreno, Ricardo Verdeguer
dc.contributor.authorGrech, Raphael
dc.date.accessioned2022-01-20T17:33:39Z
dc.date.available2022-01-20T17:33:39Z
dc.date.issued2021-11-15
dc.description.abstractAs the primary navigation source, GNSS performance monitoring and prediction have critical importance for the success of mission-critical urban air mobility and cargo applications. In this paper, a novel machine learning based performance prediction algorithm is suggested considering environment recognition. Valid environmental parameters that support recognition and prediction stages are introduced, and K-Nearest Neighbour, Support Vector Regression and Random Forest algorithms are tested based on their prediction performance with using these environmental parameters. Performance prediction results and parameter importances are analyzed based on three types of urban environments (suburban, urban and urban-canyon) with the synthetic data generated by a high quality GNSS simulator.en_UK
dc.identifier.citationIsik OK, Petrunin I, Inalhan G, et al., (2021) A machine learning based GNSS performance prediction for urban air mobility using environment recognition. In: 2021 AIAA/IEEE 40th Digital Avionics Systems Conference (DASC), 3-7 October 2021, San Antonio, USAen_UK
dc.identifier.eisbn978-1-6654-3420-1
dc.identifier.issn2155-7209
dc.identifier.urihttps://doi.org/10.1109/DASC52595.2021.9594434
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17462
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectGNSSen_UK
dc.subjectmachine learningen_UK
dc.subjectperformance predictionen_UK
dc.subjectenvironment recognitionen_UK
dc.subjectenvironment classificationen_UK
dc.subjectintegrityen_UK
dc.subjecturban air mobilityen_UK
dc.titleA machine learning based GNSS performance prediction for urban air mobility using environment recognitionen_UK
dc.typeConference paperen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
urban_air_mobility-2021.pdf
Size:
499.08 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)