Improved velocity estimation in urban areas using Doppler observations

verfasst von
Ankit Jain, Dennis Kulemann, Steffen Schön

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.

Institut für Erdmessung
Aufsatz in Konferenzband
ASJC Scopus Sachgebiete
Computernetzwerke und -kommunikation, Luft- und Raumfahrttechnik, Steuerung und Optimierung
Elektronische Version(en) (Zugang: Geschlossen)

Details im Forschungsportal „Research@Leibniz University“