TY - GEN

T1 - GNSS Feature Map Aided RTK Positioning 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 Federal Ministry for Economic Affairs and Climate Action (BMWK), based on a resolution of the German Bundestag.

PY - 2023

Y1 - 2023

N2 - Accurate localization in urban environments plays a crucial role in many applications and emerging technologies, such as autonomous driving or pedestrian navigation. The demand for high accuracy and high integrity positioning models using the Global Navigation Satellite System (GNSS) sensor is increasing due to the fact that the GNSS sensor is the only one providing absolute positioning information. The main error source for GNSS-based positioning in urban environments is multipath and non-line-of-sight (NLOS) signal reception caused by high-raised buildings in the vicinity of the antenna, leading to ranging errors which can reach up to several hundreds of meters. In kinematic urban GNSS applications, the main error sources have a complex spatio-temporal behaviour. Hence, many studies focus on multipath mitigation methods based on ray tracing algorithms in conjunction with 3D city models. Performing epoch-wise ray tracing to exclude NLOS satellites is computationally intensive and requires special computation techniques for real-time applicability. In this contribution, we generate a fully populated GNSS Feature Map based on collected real GNSS data containing information on potential pseudorange errors. This map is integrated into a GNSS real-time kinematic (RTK) positioning algorithm to adapt the weighting model and consequently improving the positioning solution without the need of high computation load and external city model information. By applying our proposed approach, root mean squared errors of horizontal position deviations are improved by 90.5 % and the maximum horizontal position error is reduced from 2.45 m to 0.52 m. The increased accuracy of the positioning solution also leads to more nominal operation modes (95.8 % compared to 77.4 %) and misleading information and hazardous operations epochs are minimized.

AB - Accurate localization in urban environments plays a crucial role in many applications and emerging technologies, such as autonomous driving or pedestrian navigation. The demand for high accuracy and high integrity positioning models using the Global Navigation Satellite System (GNSS) sensor is increasing due to the fact that the GNSS sensor is the only one providing absolute positioning information. The main error source for GNSS-based positioning in urban environments is multipath and non-line-of-sight (NLOS) signal reception caused by high-raised buildings in the vicinity of the antenna, leading to ranging errors which can reach up to several hundreds of meters. In kinematic urban GNSS applications, the main error sources have a complex spatio-temporal behaviour. Hence, many studies focus on multipath mitigation methods based on ray tracing algorithms in conjunction with 3D city models. Performing epoch-wise ray tracing to exclude NLOS satellites is computationally intensive and requires special computation techniques for real-time applicability. In this contribution, we generate a fully populated GNSS Feature Map based on collected real GNSS data containing information on potential pseudorange errors. This map is integrated into a GNSS real-time kinematic (RTK) positioning algorithm to adapt the weighting model and consequently improving the positioning solution without the need of high computation load and external city model information. By applying our proposed approach, root mean squared errors of horizontal position deviations are improved by 90.5 % and the maximum horizontal position error is reduced from 2.45 m to 0.52 m. The increased accuracy of the positioning solution also leads to more nominal operation modes (95.8 % compared to 77.4 %) and misleading information and hazardous operations epochs are minimized.

UR - http://www.scopus.com/inward/record.url?scp=85186522183&partnerID=8YFLogxK

U2 - 10.1109/itsc57777.2023.10422176

DO - 10.1109/itsc57777.2023.10422176

M3 - Conference contribution

SN - 2153-0009

T3 - IEEE Conference on Intelligent Transportation Systems

SP - 5798

EP - 5804

BT - 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

ER -