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

doi:10.1109/DASC52595.2021.9594434

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 proceeding › Conference contribution › peer-review

5 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 language	English
Title of host publication	40th Digital Avionics Systems Conference, DASC 2021 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665434201
DOIs	https://doi.org/10.1109/DASC52595.2021.9594434
Publication status	Published - 2021
Externally published	Yes
Event	40th IEEE/AIAA Digital Avionics Systems Conference, DASC 2021 - San Antonio, United States Duration: 3 Oct 2021 → 7 Oct 2021

Publication series

Name	AIAA/IEEE Digital Avionics Systems Conference - Proceedings
Volume	2021-October
ISSN (Print)	2155-7195
ISSN (Electronic)	2155-7209

Conference

Conference	40th IEEE/AIAA Digital Avionics Systems Conference, DASC 2021
Country/Territory	United States
City	San Antonio
Period	3/10/21 → 7/10/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/DASC52595.2021.9594434

Cite this

Isik, O. K., Petrunin, I., Inalhan, G., Tsourdos, A., Moreno, R. V., & Grech, R. (2021). A Machine Learning Based GNSS Performance Prediction for Urban Air Mobility Using Environment Recognition. In 40th Digital Avionics Systems Conference, DASC 2021 - Proceedings (AIAA/IEEE Digital Avionics Systems Conference - Proceedings; Vol. 2021-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DASC52595.2021.9594434

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title = "A Machine Learning Based GNSS Performance Prediction for Urban Air Mobility Using Environment Recognition",

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.",

keywords = "GNSS, environment classification, environment recognition, integrity, machine learning, performance prediction, urban air mobility",

author = "Isik, {Oguz Kagan} and Ivan Petrunin and Gokhan Inalhan and Antonios Tsourdos and Moreno, {Ricardo Verdeguer} and Raphael Grech",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 40th IEEE/AIAA Digital Avionics Systems Conference, DASC 2021 ; Conference date: 03-10-2021 Through 07-10-2021",

year = "2021",

doi = "10.1109/DASC52595.2021.9594434",

language = "English",

series = "AIAA/IEEE Digital Avionics Systems Conference - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "40th Digital Avionics Systems Conference, DASC 2021 - Proceedings",

address = "United States",

}

Isik, OK, Petrunin, I, Inalhan, G, Tsourdos, A, Moreno, RV & Grech, R 2021, A Machine Learning Based GNSS Performance Prediction for Urban Air Mobility Using Environment Recognition. in 40th Digital Avionics Systems Conference, DASC 2021 - Proceedings. AIAA/IEEE Digital Avionics Systems Conference - Proceedings, vol. 2021-October, Institute of Electrical and Electronics Engineers Inc., 40th IEEE/AIAA Digital Avionics Systems Conference, DASC 2021, San Antonio, United States, 3/10/21. https://doi.org/10.1109/DASC52595.2021.9594434

A Machine Learning Based GNSS Performance Prediction for Urban Air Mobility Using Environment Recognition. / Isik, Oguz Kagan; Petrunin, Ivan; Inalhan, Gokhan et al.
40th Digital Avionics Systems Conference, DASC 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. (AIAA/IEEE Digital Avionics Systems Conference - Proceedings; Vol. 2021-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Machine Learning Based GNSS Performance Prediction for Urban Air Mobility Using Environment Recognition

AU - Isik, Oguz Kagan

AU - Petrunin, Ivan

AU - Inalhan, Gokhan

AU - Tsourdos, Antonios

AU - Moreno, Ricardo Verdeguer

AU - Grech, Raphael

PY - 2021

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AB - 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.

KW - GNSS

KW - environment classification

KW - environment recognition

KW - integrity

KW - machine learning

KW - performance prediction

KW - urban air mobility

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DO - 10.1109/DASC52595.2021.9594434

M3 - Conference contribution

AN - SCOPUS:85122828497

T3 - AIAA/IEEE Digital Avionics Systems Conference - Proceedings

BT - 40th Digital Avionics Systems Conference, DASC 2021 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 40th IEEE/AIAA Digital Avionics Systems Conference, DASC 2021

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Isik OK, Petrunin I, Inalhan G, Tsourdos A, Moreno RV, Grech R. A Machine Learning Based GNSS Performance Prediction for Urban Air Mobility Using Environment Recognition. In 40th Digital Avionics Systems Conference, DASC 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. (AIAA/IEEE Digital Avionics Systems Conference - Proceedings). doi: 10.1109/DASC52595.2021.9594434

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

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