Onboard Pointing Error Detection and Estimation of Observation Satellite Data Using Extended Kalman Filter.

Area of Science:

• Satellite Technology
• Aerospace Engineering
• Artificial Intelligence

Background:

• Satellite communication, navigation, and observation are crucial for modern applications.
• Secure handling of detailed satellite data is essential due to its importance.
• Accurate onboard error detection and estimation are vital for reliable satellite operations.

Purpose of the Study:

• To propose a lightweight deep learning algorithm for detecting and estimating onboard satellite pointing errors.
• To enhance the accuracy and autonomy of satellite attitude and orbit determination.
• To reduce dependency on ground tracking systems for satellite positioning.

Main Methods:

• Implementation of a lightweight deep learning algorithm based on the Extended Kalman Filter (EKF).
• Utilizing EKF for detecting onboard pointing errors, including attitude and orbit determination.
• Comparing observed satellite data with accurate gravity models for error detection.

Main Results:

• The proposed EKF-based model accurately detects and estimates onboard pointing errors.
• The method demonstrates superior accuracy compared to other existing methodologies.
• Reduced dependence on ground tracking systems for satellite determination was achieved.

Conclusions:

• The Extended Kalman Filter is an optimal method for estimating orbital parameters and detecting linearization errors.
• This EKF implementation significantly contributes to various space missions, including autonomous operations and Space Situational Awareness.
• The algorithm offers a robust solution for enhancing the precision and reliability of satellite systems.