Detection of Degraded Star Observation Using Singular Values for Improved Attitude Determination

Area of Science:

• Aerospace Engineering
• Astrodynamics
• Computational Science

Background:

• Accurate attitude determination is crucial for spacecraft navigation and control.
• Existing methods may be sensitive to noise and observation errors.
• Quantifying observation quality is key to robust attitude estimation.

Purpose of the Study:

• To develop a novel algorithm for enhancing attitude determination accuracy.
• To introduce a method for computing star observation quality using singular value analysis.
• To integrate quantized error levels as weighting factors in attitude determination.

Main Methods:

• Leveraging the invariance of singular values under attitude transformations.
• Assessing error magnitude via the deviation of singular values.
• Applying p-value hypothesis testing for error level quantization.
• Using quantized error levels as weights in the attitude determination process.

Main Results:

• The proposed algorithm effectively computes star observation quality.
• Quantized error levels were successfully derived using p-value hypothesis testing.
• Simulations demonstrated that the calculated weights significantly improve attitude determination performance.
• The method shows enhanced robustness against observation errors.

Conclusions:

• The novel approach provides a quantifiable measure of star observation quality.
• Integrating these quality measures as weights enhances attitude determination accuracy.
• This method offers a promising advancement for robust spacecraft attitude determination.