Improved velocity estimation in urban areas using Doppler observations

authored by: Ankit Jain, Dennis Kulemann, Steffen Schön
Abstract: In urban areas, Global navigation satellite system (GNSS) velocity estimation suffers due to signal obstruction and multipath effects. This paper explores two different variance models, known as elevation dependent and SIGMA-ϵ to account for the different quality of the Doppler observations accurately in an urban surrounding. As the name suggests, the elevation dependent model relies on the elevation angles of the satellites whereas the SIGMA-ϵ model is based on the measured carrier-to-noise density ratio (C/N0). GNSS and inertial measurement unit (IMU) data are recorded in an urban area with a vehicle for about two hours with multiple devices. In order to find the impact of different variance models, Doppler data captured are processed post kinematic experiment and velocity estimates are computed with least squares (LS) and linearized Kalman filter (LKF) method. The computed velocities are then compared with a reference trajectory and errors are evaluated for all the receivers with regards to the variance models. For all the receivers, the estimated velocity root mean square error (RMSE) is less than at least 16% up to a maximum of about 41% with the SIGMA-ϵ model in comparison to elevation dependent model. Also, most of the magnitude of the maximum deviations are reduced with SIGMA-ϵ model. It must also be noted that SIGMA-ϵ model does not incorporate any additional computational load apart from an initial calibration in an open-sky static environment.
Organisation(s): Institute of Geodesy
Type: Conference contribution
Publication date: 2021
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Computer Networks and Communications, Aerospace Engineering, Control and Optimization
Electronic version(s): https://doi.org/10.1109/icl-gnss51451.2021.9452243 (Access: Closed)
: Details in the research portal "Research@Leibniz University"

BibTeX

@inproceedings{6bf735f91a6b424698088af5d9993567,
title = "Improved velocity estimation in urban areas using Doppler observations",
abstract = "In urban areas, Global navigation satellite system (GNSS) velocity estimation suffers due to signal obstruction and multipath effects. This paper explores two different variance models, known as elevation dependent and SIGMA-ϵ to account for the different quality of the Doppler observations accurately in an urban surrounding. As the name suggests, the elevation dependent model relies on the elevation angles of the satellites whereas the SIGMA-ϵ model is based on the measured carrier-to-noise density ratio (C/N0). GNSS and inertial measurement unit (IMU) data are recorded in an urban area with a vehicle for about two hours with multiple devices. In order to find the impact of different variance models, Doppler data captured are processed post kinematic experiment and velocity estimates are computed with least squares (LS) and linearized Kalman filter (LKF) method. The computed velocities are then compared with a reference trajectory and errors are evaluated for all the receivers with regards to the variance models. For all the receivers, the estimated velocity root mean square error (RMSE) is less than at least 16% up to a maximum of about 41% with the SIGMA-ϵ model in comparison to elevation dependent model. Also, most of the magnitude of the maximum deviations are reduced with SIGMA-ϵ model. It must also be noted that SIGMA-ϵ model does not incorporate any additional computational load apart from an initial calibration in an open-sky static environment.",
keywords = "Doppler observations, Kalman filter, Least Squares, urban velocity estimation, variance models",
author = "Ankit Jain and Dennis Kulemann and Steffen Sch{\"o}n",
note = "Funding Information: ACKNOWLEDGMENT The authors would like to thank Dr. Thomas Krawinkel, Fabian Ruwisch and Lucy Icking for conducting the kinematic experiment. Dennis Kulemann thanks the DFG research training group i.c.sens for funding his research in the last quarter of the year 2020. Ankit Jain is an associated member of the DFG group i.c.sens to which he is really thankful.; 2021 International Conference on Localization and GNSS (ICL-GNSS) ; Conference date: 01-06-2021 Through 03-06-2021",
year = "2021",
doi = "10.1109/icl-gnss51451.2021.9452243",
language = "English",
isbn = "978-1-7281-9645-9",
series = "International Heat Transfer Conference Proceedings",
publisher = "IEEE Computer Society",
editor = "Jari Nurmi and Elena-Simona Lohan and Joaquin Torres-Sospedra and Heidi Kuusniemi and Aleksandr Ometov",
booktitle = "2021 International Conference on Localization and GNSS, ICL-GNSS 2021 - Proceedings",
address = "United States",
}