Details
| Original language | English |
|---|---|
| Title of host publication | 2021 International Conference on Localization and GNSS, ICL-GNSS 2021 - Proceedings |
| Editors | Jari Nurmi, Elena-Simona Lohan, Joaquin Torres-Sospedra, Heidi Kuusniemi, Aleksandr Ometov |
| Publisher | IEEE Computer Society |
| ISBN (electronic) | 9781728196442 |
| ISBN (print) | 978-1-7281-9645-9 |
| Publication status | Published - 2021 |
| Event | 2021 International Conference on Localization and GNSS (ICL-GNSS) - Tampere, Finland Duration: 1 Jun 2021 → 3 Jun 2021 |
Publication series
| Name | International Heat Transfer Conference Proceedings |
|---|---|
| Volume | 2021 |
| 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
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Engineering(all)
- Aerospace Engineering
- Mathematics(all)
- Control and Optimization
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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 proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Improved velocity estimation in urban areas using Doppler observations
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.
PY - 2021
Y1 - 2021
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.
KW - Doppler observations
KW - Kalman filter
KW - Least Squares
KW - urban velocity estimation
KW - variance models
UR - http://www.scopus.com/inward/record.url?scp=85112865253&partnerID=8YFLogxK
U2 - 10.1109/icl-gnss51451.2021.9452243
DO - 10.1109/icl-gnss51451.2021.9452243
M3 - Conference contribution
SN - 978-1-7281-9645-9
T3 - International Heat Transfer Conference Proceedings
BT - 2021 International Conference on Localization and GNSS, ICL-GNSS 2021 - Proceedings
A2 - Nurmi, Jari
A2 - Lohan, Elena-Simona
A2 - Torres-Sospedra, Joaquin
A2 - Kuusniemi, Heidi
A2 - Ometov, Aleksandr
PB - IEEE Computer Society
T2 - 2021 International Conference on Localization and GNSS (ICL-GNSS)
Y2 - 1 June 2021 through 3 June 2021
ER -