Details
| Original language | English |
|---|---|
| Pages (from-to) | 13721-13733 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Intelligent Transportation Systems |
| Volume | 26 |
| Issue number | 9 |
| Early online date | 26 May 2025 |
| Publication status | Published - 16 Sept 2025 |
Abstract
Keywords
- GNSS feature map, Robust statistics, RTK, urban GNSS
ASJC Scopus subject areas
- Engineering(all)
- Automotive Engineering
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
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In: IEEE Transactions on Intelligent Transportation Systems, Vol. 26, No. 9, 16.09.2025, p. 13721-13733.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Feature Map Aided Robust High Precision GNSS Positioning in Harsh Urban Environments
AU - Ruwisch, Fabian
AU - Schön, Steffen
N1 - Publisher Copyright: © 2000-2011 IEEE.
PY - 2025/9/16
Y1 - 2025/9/16
N2 - In this contribution, we propose the GNSS Feature Map-aided robust extended Kalman filter, which can provide centimeter-to-decimeter-level GNSS RTK position accuracy in urban environments without the need of additional sensors, city model information or computational intensive ray tracing methods. In this approach, the information on the predicted observation error magnitudes from the generated GNSS Feature Map is combined with the concept of robust estimation. The RTK positioning performance comparison for a dynamic experiment under harsh signal propagation conditions reveals that GNSS Feature Map-aided weighting using the Geman-McClure loss function shows the best overall performance. The RMS of the horizontal position error is improved by 17 % compared to C/N0 weighting, while 3DMA NLOS exclusion even degrades the solution. Furthermore, the combination of Feature Map information with the robust Geman-McClure loss function is effectively enhancing the float solution and reducing the number of falsely fixed ambiguities.
AB - In this contribution, we propose the GNSS Feature Map-aided robust extended Kalman filter, which can provide centimeter-to-decimeter-level GNSS RTK position accuracy in urban environments without the need of additional sensors, city model information or computational intensive ray tracing methods. In this approach, the information on the predicted observation error magnitudes from the generated GNSS Feature Map is combined with the concept of robust estimation. The RTK positioning performance comparison for a dynamic experiment under harsh signal propagation conditions reveals that GNSS Feature Map-aided weighting using the Geman-McClure loss function shows the best overall performance. The RMS of the horizontal position error is improved by 17 % compared to C/N0 weighting, while 3DMA NLOS exclusion even degrades the solution. Furthermore, the combination of Feature Map information with the robust Geman-McClure loss function is effectively enhancing the float solution and reducing the number of falsely fixed ambiguities.
KW - GNSS feature map
KW - Robust statistics
KW - RTK
KW - urban GNSS
UR - http://www.scopus.com/inward/record.url?scp=105006544012&partnerID=8YFLogxK
U2 - 10.1109/TITS.2025.3569975
DO - 10.1109/TITS.2025.3569975
M3 - Article
VL - 26
SP - 13721
EP - 13733
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
SN - 1524-9050
IS - 9
ER -