Precise orbit determination of BeiDou satellites focusing on solar radiation pressure models

Research output: ThesisMaster's Thesis


The Chinese BeiDou Navigation Satellite System (BeiDou) has become established as one of the four Global Navigation Satellite System (GNSS). Initially constructed as a regional system, China has launched 30 satellites within three years and declared the global system operational in 2020. A total of 55 satellites are in orbit, with three different types of orbit constellations. The precise determination of satellite orbits is a prerequisite for high-precision GNSS applications. However, the accuracy of BeiDou orbits cannot yet compete with established systems such as the Global Positioning System (GPS). Modeling of perturbing accelerations acting on the satellites caused by conservative and non-conservative forces is crucial for the quality of the orbit. Solar radiation pressure (SRP) is the dominant error source in precise orbit determination. The modeling of this influence is further complicated by the use of different satellite
types and orbit constellations in the BeiDou system. The aim of this thesis was to integrate BeiDou into the processing of GNSS constellations at Graz University of Technology (TUG) and in particular to investigate the impact of SRP. Several SRP models were assessed and their applicability for BeiDou was analyzed. The period from June to December 2020 was investigated to determine the most suitable parameterization of the models for the different satellite types. The quality of the orbits was evaluated by internal orbit consistency checks, in the form of deviations at the midnight epoch of two consecutive 24 h orbit arcs. Based on the analyses conducted within the scope of this thesis, significant improvements in the orbit consistency could be achieved when specific variations of empirical SRP models were used. The BeiDou orbits obtained in this study are comparable to solutions by the analysis centers of the International GNSS Service (IGS).
Original languageEnglish
QualificationMaster of Science
Awarding Institution
  • Graz University of Technology (90000)
  • Mayer-Gürr, Torsten, Supervisor
Award date20 Jan 2022
Publication statusPublished - 20 Jan 2022

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