An attitude tracking method for star sensor under dynamic conditions

Abstract:

The tracking performance of star sensor degrades seriously under dynamic conditions. To improve the tracking accuracy and efficiency, an attitude tracking method based on unscented Kalman filter (UKF) and singular value decomposition (SVD) is proposed in this paper. The star sensor is modeled as a nonlinear stochastic system, the state of which is attitude quaternion. The quaternion can be estimated by UKF, then the predicted attitude and corresponding star positions are obtained. To ensure the stability of attitude tracking, SVD is applied to obtain the sigma points in UKF continuously. The experimental results indicate that the proposed method yields high accuracy and efficiency in attitude tracking. This method provides a practical approach to ensure the tracking performance of star sensor under dynamic conditions.