Diffraction Modeling for Improved 3DMA GNSS Urban Navigation

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Original languageEnglish
Title of host publication35th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2022
Pages1902-1916
Number of pages15
ISBN (Electronic)9781713871361
Publication statusPublished - 2022

Abstract

Positioning with Global Navigation Satellite Systems (GNSS) in urban areas is still a challenging task. Obstacles in the vicinity of the antenna may block the Line-of-Sight (LOS) ray or degrade the signal resulting in lower signal strengths, signal interruptions and extra path delays of the signal. Ray tracing algorithms are therefore a commonly used 3D map aided (3DMA) approach to detect signals affected by the environment. Unlike signal reflection, diffraction is rarely considered as error source. A commonly used approach to mitigate the influence of diffracted signals on the position solution is the weighting of observations based on their Carrier-to-Noise Power Density Ratio (C/N0), e.g. by the SIGMA-ε model. However, in the case of diffraction of a LOS signal, the signal can be amplified, although the quality of the observation has decreased. Consequently, the C/N0 does not always provide reliable information about the quality of the signal. In this contribution, we present a detailed study of the behavior of diffraction in urban areas based on a Level of Detail (LoD)2 building model. A new layer is added to the existing classes of LOS, multipath (MP), Non-Line-of-Sight (NLOS) and blocked as well as the received signal strength is predicted. We validate our algorithm by both simulations and real data in static and kinematic scenarios. We show that diffraction affects all types of signal classes, resulting in extra path delays in cm-range and a loss in signal strength of up to 17 dB-Hz. Finally, an adapted weighting scheme is developed combining the SIGMA-ε model with ray tracing and diffraction modeling information. Its performance is tested in multi-GNSS Single Point Positioning (SPP) in a kinematic scenario in the city of Hannover. Our adapted weighting scheme delivers significant benefits in both 3D and 2D accuracy compared to the conventional SIGMA-ε weighting. In urban trenches, combining the 8-class model with the C/N0 dependent SIGMA-ε model results in reductions of the RMSE of up to 20 %. Large outliers in the positioning solution, especially for user positions in narrow urban trenches, could be drastically reduced using the combined weighting scheme.

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Diffraction Modeling for Improved 3DMA GNSS Urban Navigation. / Schaper, Anat; Ruwisch, Fabian; Schön, Steffen.
35th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2022. 2022. p. 1902-1916.

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Schaper, A, Ruwisch, F & Schön, S 2022, Diffraction Modeling for Improved 3DMA GNSS Urban Navigation. in 35th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2022. pp. 1902-1916. https://doi.org/10.33012/2022.18541
Schaper, A., Ruwisch, F., & Schön, S. (2022). Diffraction Modeling for Improved 3DMA GNSS Urban Navigation. In 35th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2022 (pp. 1902-1916) https://doi.org/10.33012/2022.18541
Schaper A, Ruwisch F, Schön S. Diffraction Modeling for Improved 3DMA GNSS Urban Navigation. In 35th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2022. 2022. p. 1902-1916 doi: 10.33012/2022.18541
Schaper, Anat ; Ruwisch, Fabian ; Schön, Steffen. / Diffraction Modeling for Improved 3DMA GNSS Urban Navigation. 35th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2022. 2022. pp. 1902-1916
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abstract = "Positioning with Global Navigation Satellite Systems (GNSS) in urban areas is still a challenging task. Obstacles in the vicinity of the antenna may block the Line-of-Sight (LOS) ray or degrade the signal resulting in lower signal strengths, signal interruptions and extra path delays of the signal. Ray tracing algorithms are therefore a commonly used 3D map aided (3DMA) approach to detect signals affected by the environment. Unlike signal reflection, diffraction is rarely considered as error source. A commonly used approach to mitigate the influence of diffracted signals on the position solution is the weighting of observations based on their Carrier-to-Noise Power Density Ratio (C/N0), e.g. by the SIGMA-ε model. However, in the case of diffraction of a LOS signal, the signal can be amplified, although the quality of the observation has decreased. Consequently, the C/N0 does not always provide reliable information about the quality of the signal. In this contribution, we present a detailed study of the behavior of diffraction in urban areas based on a Level of Detail (LoD)2 building model. A new layer is added to the existing classes of LOS, multipath (MP), Non-Line-of-Sight (NLOS) and blocked as well as the received signal strength is predicted. We validate our algorithm by both simulations and real data in static and kinematic scenarios. We show that diffraction affects all types of signal classes, resulting in extra path delays in cm-range and a loss in signal strength of up to 17 dB-Hz. Finally, an adapted weighting scheme is developed combining the SIGMA-ε model with ray tracing and diffraction modeling information. Its performance is tested in multi-GNSS Single Point Positioning (SPP) in a kinematic scenario in the city of Hannover. Our adapted weighting scheme delivers significant benefits in both 3D and 2D accuracy compared to the conventional SIGMA-ε weighting. In urban trenches, combining the 8-class model with the C/N0 dependent SIGMA-ε model results in reductions of the RMSE of up to 20 %. Large outliers in the positioning solution, especially for user positions in narrow urban trenches, could be drastically reduced using the combined weighting scheme. ",
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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. The work was also partly funded in the framework of the i.c.sens research training group funded by DFG under grant GRK2159. The authors would like to thank Lucy Icking, Ali Karimidoona and Dr. Tobias Kersten for providing GNSS data of the experiments.

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