Performance Assessment of GNSS RTK Positioning in Urban Environments

Outlier Detection versus 3DMA-FDE

authored by

Fabian Ruwisch, Steffen Schön

Abstract

Urban navigation applications, e.g., autonomous driving, require high quality in terms of accuracy and integrity. The Global Navigation Satellite System (GNSS) sensor is the only one providing absolute positioning information and thus the demand of high accuracy and high precision GNSS-based positioning models is increasing. In order to reach centimeter to decimeter accuracy, e.g., for lane level accuracy applications, carrier-phase based positioning techniques have to be used. The main error source of GNSS positioning in urban environments is non-line-of-sight (NLOS) signal reception, leading to potentially unbounded and positive ranging errors. One widely used method to mitigate multipath errors is 3D Mapping-Aided (3DMA) GNSS. Using the information from building models, NLOS signals can be identified and consequently excluded from the positioning algorithm. On the other hand, outlier detection approaches are implemented in positioning algorithms to detect and exclude faulty GNSS measurements from the estimation process in an iterative manner. The impact of 3DMA-FDE techniques and outlier detection is already widely developed and analyzed in terms of pseudorange-based GNSS positioning for urban navigation. However, a detailed exploration of these methods implemented in carrier-phase based GNSS positioning is not yet fully exploited. We study the assessed performance of both approaches in static and kinematic GNSS real-time-kinematic (RTK)positioning. In static applications, the 3DMA-FDE approach outperforms the outlier detection strategy in terms of convergence time, accuracy (float root mean squared errors (RMSE) are halved) and ambiguity fixing rate (7 % compared to 88 %). Based on a kinematic experiment, we show that the most accurate result is achieved by a combined solution. We obtain float RMSE of 64 cm, 60 cm and 81 cm in north, east and up direction, respectively. Using the ambiguity dilution-of-precision (ADOP) and ratio test values, we show that reliable ambiguity fixing is not possible due to poor geometry and observation quality. Finally, we show that our recently presented GNSS Feature Map can help to avoid computationally intensive operations at the rover due to epoch-wise ray tracing. Using a precomputed 5 m grid of way points, each containing ray tracing information in a 360

^◦×90

^◦ grid, a classification accuracy of 95 % is achieved resulting in a very similar positioning performance.

Organisation(s)

Institute of Geodesy

Type

Conference contribution

Pages

2649-2663

No. of pages

Publication date

2022

Publication status

Published

Peer reviewed

Yes

Electronic version(s)

https://doi.org/10.33012/2022.18510 (Access: Closed)

Details in the research portal "Research@Leibniz University"

BibTeX

@inproceedings{c03e5fcb753f4d9fa12fa15e8db2e334,
title = "Performance Assessment of GNSS RTK Positioning in Urban Environments: Outlier Detection versus 3DMA-FDE",
abstract = "Urban navigation applications, e.g., autonomous driving, require high quality in terms of accuracy and integrity. The Global Navigation Satellite System (GNSS) sensor is the only one providing absolute positioning information and thus the demand of high accuracy and high precision GNSS-based positioning models is increasing. In order to reach centimeter to decimeter accuracy, e.g., for lane level accuracy applications, carrier-phase based positioning techniques have to be used. The main error source of GNSS positioning in urban environments is non-line-of-sight (NLOS) signal reception, leading to potentially unbounded and positive ranging errors. One widely used method to mitigate multipath errors is 3D Mapping-Aided (3DMA) GNSS. Using the information from building models, NLOS signals can be identified and consequently excluded from the positioning algorithm. On the other hand, outlier detection approaches are implemented in positioning algorithms to detect and exclude faulty GNSS measurements from the estimation process in an iterative manner. The impact of 3DMA-FDE techniques and outlier detection is already widely developed and analyzed in terms of pseudorange-based GNSS positioning for urban navigation. However, a detailed exploration of these methods implemented in carrier-phase based GNSS positioning is not yet fully exploited. We study the assessed performance of both approaches in static and kinematic GNSS real-time-kinematic (RTK)positioning. In static applications, the 3DMA-FDE approach outperforms the outlier detection strategy in terms of convergence time, accuracy (float root mean squared errors (RMSE) are halved) and ambiguity fixing rate (7 % compared to 88 %). Based on a kinematic experiment, we show that the most accurate result is achieved by a combined solution. We obtain float RMSE of 64 cm, 60 cm and 81 cm in north, east and up direction, respectively. Using the ambiguity dilution-of-precision (ADOP) and ratio test values, we show that reliable ambiguity fixing is not possible due to poor geometry and observation quality. Finally, we show that our recently presented GNSS Feature Map can help to avoid computationally intensive operations at the rover due to epoch-wise ray tracing. Using a precomputed 5 m grid of way points, each containing ray tracing information in a 360 ◦×90 ◦ grid, a classification accuracy of 95 % is achieved resulting in a very similar positioning performance.",
author = "Fabian Ruwisch and Steffen Sch{\"o}n",
note = "Funding Information: The results were obtained in the project KOMET, which is managed by T{\"U}V-Rheinland (PT-T{\"U}V) 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. The authors would like to thank Lucy Icking, Ali Karimidoona and Dr. Tobias Kersten for providing GNSS data of the experiments.",
year = "2022",
doi = "10.33012/2022.18510",
language = "English",
pages = "2649--2663",
booktitle = "Proceedings of the 35rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022)",
}