Improved velocity estimation in urban areas using Doppler observations

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Original languageEnglish
Title of host publication2021 International Conference on Localization and GNSS, ICL-GNSS 2021 - Proceedings
EditorsJari Nurmi, Elena-Simona Lohan, Joaquin Torres-Sospedra, Heidi Kuusniemi, Aleksandr Ometov
PublisherIEEE Computer Society
ISBN (electronic)9781728196442
ISBN (print)978-1-7281-9645-9
Publication statusPublished - 2021
Event2021 International Conference on Localization and GNSS (ICL-GNSS) - Tampere, Finland
Duration: 1 Jun 20213 Jun 2021

Publication series

NameInternational Heat Transfer Conference Proceedings
Volume2021
ISSN (Print)2325-0747
ISSN (electronic)2325-0771

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

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Cite this

Improved velocity estimation in urban areas using Doppler observations. / Jain, Ankit; Kulemann, Dennis; Schön, Steffen.
2021 International Conference on Localization and GNSS, ICL-GNSS 2021 - Proceedings. ed. / Jari Nurmi; Elena-Simona Lohan; Joaquin Torres-Sospedra; Heidi Kuusniemi; Aleksandr Ometov. IEEE Computer Society, 2021. 9452243 (International Heat Transfer Conference Proceedings; Vol. 2021).

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

Jain, A, Kulemann, D & Schön, S 2021, Improved velocity estimation in urban areas using Doppler observations. in J Nurmi, E-S Lohan, J Torres-Sospedra, H Kuusniemi & A Ometov (eds), 2021 International Conference on Localization and GNSS, ICL-GNSS 2021 - Proceedings., 9452243, International Heat Transfer Conference Proceedings, vol. 2021, IEEE Computer Society, 2021 International Conference on Localization and GNSS (ICL-GNSS), Tampere, Finland, 1 Jun 2021. https://doi.org/10.1109/icl-gnss51451.2021.9452243
Jain, A., Kulemann, D., & Schön, S. (2021). Improved velocity estimation in urban areas using Doppler observations. In J. Nurmi, E.-S. Lohan, J. Torres-Sospedra, H. Kuusniemi, & A. Ometov (Eds.), 2021 International Conference on Localization and GNSS, ICL-GNSS 2021 - Proceedings Article 9452243 (International Heat Transfer Conference Proceedings; Vol. 2021). IEEE Computer Society. https://doi.org/10.1109/icl-gnss51451.2021.9452243
Jain A, Kulemann D, Schön S. Improved velocity estimation in urban areas using Doppler observations. In Nurmi J, Lohan ES, Torres-Sospedra J, Kuusniemi H, Ometov A, editors, 2021 International Conference on Localization and GNSS, ICL-GNSS 2021 - Proceedings. IEEE Computer Society. 2021. 9452243. (International Heat Transfer Conference Proceedings). doi: 10.1109/icl-gnss51451.2021.9452243
Jain, Ankit ; Kulemann, Dennis ; Schön, Steffen. / Improved velocity estimation in urban areas using Doppler observations. 2021 International Conference on Localization and GNSS, ICL-GNSS 2021 - Proceedings. editor / Jari Nurmi ; Elena-Simona Lohan ; Joaquin Torres-Sospedra ; Heidi Kuusniemi ; Aleksandr Ometov. IEEE Computer Society, 2021. (International Heat Transfer Conference Proceedings).
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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.",
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AU - Jain, Ankit

AU - Kulemann, Dennis

AU - Schön, Steffen

N1 - 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.

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N2 - 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.

AB - 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.

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ER -

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