Angular velocity estimation from measurement vectors of star tracker.

Area of Science:

• Spacecraft attitude determination and control
• Estimation theory
• Sensor data processing

Background:

• Accurate estimation of spacecraft angular rate is critical for mission success.
• Star trackers provide essential vector measurements for attitude determination.
• Existing methods may require detailed dynamic models or are sensitive to noise.

Purpose of the Study:

• To develop and validate new algorithms for direct angular rate estimation from star tracker measurements.
• To design an adaptive Kalman filter capable of noise reduction without prior dynamic model information.
• To assess the performance, accuracy, and robustness of the proposed methods.

Main Methods:

• Least squares estimation approach for angular rate.
• Development of a novel adaptive Kalman filter for noise suppression.
• Utilizing only vector measurements and sampling interval from star trackers.
• Simulations using CHAMP satellite orbit data and experimental night-sky observations for validation.

Main Results:

• The proposed least squares and adaptive Kalman filter methods accurately estimate angular rate.
• The adaptive Kalman filter effectively filters noise without requiring dynamic model or inertia dyadic information.
• Both simulation and experimental results confirm the high accuracy and robustness of the estimation approaches.

Conclusions:

• The developed estimation techniques provide a reliable solution for determining spacecraft angular rate.
• The adaptive Kalman filter offers a noise-robust estimation method, even with limited system knowledge.
• The validated approaches enhance spacecraft navigation and control capabilities.