Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2022 International Technical Meeting of The Institute of Navigation |
| Place of Publication | Long Beach, California |
| Pages | 701-711 |
| Number of pages | 11 |
| ISBN (electronic) | 9780936406305 |
| Publication status | Published - 2022 |
Publication series
| Name | Proceedings of the International Technical Meeting of The Institute of Navigation, ITM |
|---|---|
| Volume | 2022-January |
| ISSN (Print) | 2330-3662 |
| ISSN (electronic) | 2330-3646 |
Abstract
Urban environments are challenging for GNSS (Global Navigation Satellite System) signal propagation. Surrounding buildings cause signal reflection and blockage resulting in numerous non-line-of-sight (NLOS) and multipath (LOS plus reflection) signal receptions. In a kinematic application, like e.g., pedestrian navigation and autonomous driving, the main error sources in urban areas (NLOS biases, multipath) have a complex spatiotemporal behaviour, i.e. their occurrence and magnitude depend on the satellite ray direction, the user antenna location and properties as well as on the buildings in the surrounding area. In this study, we present a new method for mapping GNSS signal propagation-related features, which depend on both the varying user antenna location and satellite position, into one common GNSS Feature Map. Our proposed approach improves the understanding of GNSS signal propagation in challenging environments, especially for kinematic trajectories in urban trenches. Based on this map, users can identify environmental structures and critical trajectory sections for both the user locations and satellite positions and thus exploit the map for trajectory planning. Furthermore, we show how the GNSS Feature Map can contribute to the correction of NLOS biases. Ray tracing can be performed offline at given positions in the 3D model which provides the predicted errors to users that will pass at that location simultaneously minimizing the computation load at the rover. Our proposed approach of NLOS bias correction reduces the observed DD biases due to NLOS reception by 50% for the 68%-quantile. Thereby the distance to the reference way point of the map is not important if we assume a search space of 5 m around the ground truth to make sure we are comparing positions in the same street.
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Proceedings of the 2022 International Technical Meeting of The Institute of Navigation. Long Beach, California, 2022. p. 701-711 (Proceedings of the International Technical Meeting of The Institute of Navigation, ITM; Vol. 2022-January).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - GNSS Feature Map
T2 - Representation of Signal Propagation-related Features in Urban Trenches
AU - Ruwisch, Fabian
AU - Schön, Steffen
N1 - Funding Information: The results were obtained in the project KOMET, which is managed by TÜV-Rheinland (PT-TÜV) under the grant 19A20002C and is funded by the German Federal Ministry for Economic Affairs and Energy (BMWI), based on a resolution by the German Bundestag.
PY - 2022
Y1 - 2022
N2 - Urban environments are challenging for GNSS (Global Navigation Satellite System) signal propagation. Surrounding buildings cause signal reflection and blockage resulting in numerous non-line-of-sight (NLOS) and multipath (LOS plus reflection) signal receptions. In a kinematic application, like e.g., pedestrian navigation and autonomous driving, the main error sources in urban areas (NLOS biases, multipath) have a complex spatiotemporal behaviour, i.e. their occurrence and magnitude depend on the satellite ray direction, the user antenna location and properties as well as on the buildings in the surrounding area. In this study, we present a new method for mapping GNSS signal propagation-related features, which depend on both the varying user antenna location and satellite position, into one common GNSS Feature Map. Our proposed approach improves the understanding of GNSS signal propagation in challenging environments, especially for kinematic trajectories in urban trenches. Based on this map, users can identify environmental structures and critical trajectory sections for both the user locations and satellite positions and thus exploit the map for trajectory planning. Furthermore, we show how the GNSS Feature Map can contribute to the correction of NLOS biases. Ray tracing can be performed offline at given positions in the 3D model which provides the predicted errors to users that will pass at that location simultaneously minimizing the computation load at the rover. Our proposed approach of NLOS bias correction reduces the observed DD biases due to NLOS reception by 50% for the 68%-quantile. Thereby the distance to the reference way point of the map is not important if we assume a search space of 5 m around the ground truth to make sure we are comparing positions in the same street.
AB - Urban environments are challenging for GNSS (Global Navigation Satellite System) signal propagation. Surrounding buildings cause signal reflection and blockage resulting in numerous non-line-of-sight (NLOS) and multipath (LOS plus reflection) signal receptions. In a kinematic application, like e.g., pedestrian navigation and autonomous driving, the main error sources in urban areas (NLOS biases, multipath) have a complex spatiotemporal behaviour, i.e. their occurrence and magnitude depend on the satellite ray direction, the user antenna location and properties as well as on the buildings in the surrounding area. In this study, we present a new method for mapping GNSS signal propagation-related features, which depend on both the varying user antenna location and satellite position, into one common GNSS Feature Map. Our proposed approach improves the understanding of GNSS signal propagation in challenging environments, especially for kinematic trajectories in urban trenches. Based on this map, users can identify environmental structures and critical trajectory sections for both the user locations and satellite positions and thus exploit the map for trajectory planning. Furthermore, we show how the GNSS Feature Map can contribute to the correction of NLOS biases. Ray tracing can be performed offline at given positions in the 3D model which provides the predicted errors to users that will pass at that location simultaneously minimizing the computation load at the rover. Our proposed approach of NLOS bias correction reduces the observed DD biases due to NLOS reception by 50% for the 68%-quantile. Thereby the distance to the reference way point of the map is not important if we assume a search space of 5 m around the ground truth to make sure we are comparing positions in the same street.
UR - http://www.scopus.com/inward/record.url?scp=85135375608&partnerID=8YFLogxK
U2 - 10.33012/2022.18171
DO - 10.33012/2022.18171
M3 - Conference contribution
T3 - Proceedings of the International Technical Meeting of The Institute of Navigation, ITM
SP - 701
EP - 711
BT - Proceedings of the 2022 International Technical Meeting of The Institute of Navigation
CY - Long Beach, California
ER -