Real-Time Orbit Determination of Micro-Nano Satellite Using Robust Adaptive Filtering

Area of Science:

• Aerospace Engineering
• Satellite Navigation
• Control Systems

Background:

• Micro-nano satellites often experience challenges with low-performing GPS receivers during attitude maneuvers.
• Weak GPS signals and attitude instability impact precise orbit determination (POD) for these small satellites.

Purpose of the Study:

• To develop and evaluate a modified robust adaptive hierarchical filtering algorithm for enhanced GPS-based POD in micro-nano satellites.
• To improve the adaptability and robustness of filtering algorithms in dynamic and challenging space environments.

Main Methods:

• A novel Inertial-aided Adaptive Robust Kalman Filter (IARKF) was proposed, incorporating robust adaptive filtering and a fading memory weighted method.
• A segmented adaptive filtering strategy was implemented for flexible parameter adjustment.
• On-orbit GPS data from a micro-nano satellite during slow rotation and Earth-pointing stabilization were analyzed.

Main Results:

• The IARKF demonstrated superior performance over traditional Extended Kalman Filters (EKF) and other improved algorithms.
• The algorithm effectively reduced post-fit residuals and improved orbit prediction accuracy.
• Three-axes orbit determination internal consistency precision reached the millimeter level.

Conclusions:

• The IARKF algorithm offers a robust and feasible approach for high-performance orbit determination in micro-nano satellites.
• The proposed method enhances adaptability to dynamic environments, weak GPS signals, and satellite attitude instability.