LSM-Aided Estimation Filter based Geostationary Satellite Navigation with Available GNSS Signals

This paper presents Adaptive Extended Kalman Filter (AEKF) in geostationary satellite navigation, focusing on the integration of Global Navigation Satellite System (GNSS) signals and the Least Squares Method (LSM). Commencing with a comprehensive overview of the mathematical model governing geostationary satellite orbits, encompassing intricate dynamics and gravitational effects, the paper delves into measurement models tailored for GNSS-based and LSM-processed data, elucidating their unique advantages. Acknowledging the challenges posed by signal obstructions from Earth's body and the surface interactions, the paper addresses inherent constraints in accessing Geostationary (GEO) satellites. At the core of the discussion lies the AEKF-based orbit estimation filter, designed with adaptive strategies to enhance precision and resilience in navigation. Simulation outcomes underscore the efficacy of integrating GNSS signals with LSM techniques, yielding substantial enhancements in navigation accuracy and robustness. Notably, the LSM-aided AEKF approach demonstrates its capability in providing appropriate solutions irrespective of the presence of signal disruptions.