Comparison of EKF&UKF for GNSS Based Micro Satellite Orbital State Estimation

• Published in: 2023 10th International Conference on Recent Advances in Air and Space Technologies (RAST) pp. 01 - 07
• Main Authors: Sever, Mert, Erkec, Tuncay Yunus, Hajiyev, Chingiz
• Format: Conference Proceeding
• Language: English
• Published: IEEE 07-06-2023
• Subjects: extended kalman filter; Filtering algorithms; Global navigation satellite system; GNSS; localization; orbit; Receivers; satellite; Satellites; Small satellites; Space missions; Space technology; unscented kalman filter
• DOI: 10.1109/RAST57548.2023.10198010

In recent times, there has been a growing interest in improving satellite orbital state estimation techniques. This is mainly driven by the need to reduce the cost of space missions, accommodate complex mission requirements, and overcome the limitations associated with the use of a single satellite. In this study, we propose a GNSS-based actual distance model (Pseudo-running model) for estimating satellite states. Additionally, we utilize the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) methods to acquire precise state estimations for both mother and tracker satellites. The focus of this study is to compare the performance of the different estimation models. Furthermore, we incorporate J2 satellite orbital disturbance in our state predictions using EKF and UKF methods. Overall, the findings from this study contribute to the body of knowledge in satellite state estimation and provide useful insights for future space missions.