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

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıbilirkişi

6 Atıf (Scopus)

Özet

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.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığı40th Digital Avionics Systems Conference, DASC 2021 - Proceedings
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9781665434201
DOI'lar
Yayın durumuYayınlandı - 2021
Harici olarak yayınlandıEvet
Etkinlik40th IEEE/AIAA Digital Avionics Systems Conference, DASC 2021 - San Antonio, United States
Süre: 3 Eki 20217 Eki 2021

Yayın serisi

AdıAIAA/IEEE Digital Avionics Systems Conference - Proceedings
Hacim2021-October
ISSN (Basılı)2155-7195
ISSN (Elektronik)2155-7209

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???event.eventtypes.event.conference???40th IEEE/AIAA Digital Avionics Systems Conference, DASC 2021
Ülke/BölgeUnited States
ŞehirSan Antonio
Periyot3/10/217/10/21

Bibliyografik not

Publisher Copyright:
© 2021 IEEE.

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