Kinematic orbit determination of low earth orbitsatellites using GPS and Galileo observations

Kılıç, Ozan
The GNSS code measurements are used to calculate the orbits of low earth orbiting satellites. Kinematic orbit determination is an approach which is based on satellite to satellite tracking of the GNSS receivers that are mounted onboard the satellites. This approach of orbit determination is independent of satellite dynamics (e.g. gravity field, air-drag etc.) and orbit characteristics (e.g. orbit height, eccentricity etc.) and kinematic precise orbit determination can be subsequently used in gravity field estimation procedures. Inclusion of Galileo measurements besides GPS observations can increase the reliability, robustness and accuracy of real-time navigation system of the spacecraft. The major aim of this thesis is to determine the orbit of a Low Earth Orbit (LEO) satellite with Kinematic Orbit Determination approach using GPS and Galileo observations. However, the observations from Galileo constellation are not fully available, since the system has not reached Full Operational Capability (FOC) yet. Hence, the corresponding observations are simulated. Real GPS data and simulated Galileo observations are used in a Kalman Filter to estimate the position and the velocity of a LEO satellite. An Adaptive Robust Extended Kalman Filter algorithm which is an extension of Kalman filter for non-linear systems is used particularly to enhance the filter performance in terms of accuracy. To obtain precise orbit of the satellite, the ionospheric effects are removed by taking ionosphere free linear combination of the dual-frequency GNSS measurements. Additionally, a Helmert variance component estimation (HVCE) is adopted for the estimation of variance components of each GNSS sensor measurement. In order to determine the accuracy of the estimated orbit, the results of Adaptive Robust Extended Kalman Filter algorithm for real LEO satellite data are compared with publicly available very precise ephemerides from Jet Propulsion Laboratory (JPL). Filter results from simulated navigation data are also compared with the true orbit generated by the simulation. Adaptive Robust Extended Kalman Filter algorithm is shown to provide an improvement of more than 70 cm in 3D RMS results for 24 hours of navigation data. In the simulation scenario, addition of simulated Galileo observations and implementation of HVCE approach led to achieve 20 cm better 3D RMS results.