A Machine Learning Based GNSS Performance Prediction for Urban Air Mobility Using Environment Recognition

Oguz Kagan Isik, Ivan Petrunin, Gokhan Inalhan, Antonios Tsourdos, Ricardo Verdeguer Moreno, Raphael Grech

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

As 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.

Original languageEnglish
Title of host publication40th Digital Avionics Systems Conference, DASC 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665434201
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event40th IEEE/AIAA Digital Avionics Systems Conference, DASC 2021 - San Antonio, United States
Duration: 3 Oct 20217 Oct 2021

Publication series

NameAIAA/IEEE Digital Avionics Systems Conference - Proceedings
Volume2021-October
ISSN (Print)2155-7195
ISSN (Electronic)2155-7209

Conference

Conference40th IEEE/AIAA Digital Avionics Systems Conference, DASC 2021
Country/TerritoryUnited States
CitySan Antonio
Period3/10/217/10/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • environment classification
  • environment recognition
  • GNSS
  • integrity
  • machine learning
  • performance prediction
  • urban air mobility

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