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 -