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 dil | İngilizce |
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Ana bilgisayar yayını başlığı | 40th Digital Avionics Systems Conference, DASC 2021 - Proceedings |
Yayınlayan | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Elektronik) | 9781665434201 |
DOI'lar | |
Yayın durumu | Yayınlandı - 2021 |
Harici olarak yayınlandı | Evet |
Etkinlik | 40th IEEE/AIAA Digital Avionics Systems Conference, DASC 2021 - San Antonio, United States Süre: 3 Eki 2021 → 7 Eki 2021 |
Yayın serisi
Adı | AIAA/IEEE Digital Avionics Systems Conference - Proceedings |
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Hacim | 2021-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 |
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Ülke/Bölge | United States |
Şehir | San Antonio |
Periyot | 3/10/21 → 7/10/21 |
Bibliyografik not
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