Algorithm with Patterned Singular Value Approach for Highly Reliable Autonomous Star Identification.

Area of Science:

• Spacecraft Attitude Control
• Autonomous Navigation
• Star Pattern Recognition

Background:

• Star trackers often fail during rapid spacecraft maneuvers due to dynamic conditions.
• Existing tracking methods require initial orientation information, limiting autonomous capabilities.
• Agile spacecraft demand robust attitude determination systems that function independently.

Purpose of the Study:

• To develop a novel autonomous star pattern identification algorithm for agile spacecraft operating under dynamic conditions.
• To enhance the robustness and speed of star identification algorithms, overcoming limitations of existing methods.
• To address challenges posed by reduced star counts and centroid inaccuracies in star tracker data.

Main Methods:

• A novel autonomous identification strategy combining two existing classes of star pattern recognition.
• Incorporation of additional constraints to handle reduced star visibility and centroid accuracy.
• Performance analysis using representative algorithms from actual space applications for comparative evaluation.

Main Results:

• The proposed method demonstrates high robustness against positional noise and false stars.
• Numerical simulations confirm a fast run-time suitable for high-speed spacecraft applications.
• The algorithm provides a unique and reliable method for star identity determination.

Conclusions:

• The developed algorithm offers a robust and rapid solution for autonomous star pattern identification in dynamic environments.
• This advancement is crucial for improving the autonomy and reliability of spacecraft attitude and control systems.
• The method's efficiency and accuracy make it suitable for next-generation agile spacecraft missions.