Adaptive Robust Nonlinear Filtering for Spacecraft Attitude Estimation Based on Additive Quaternion

This paper proposes a new Huber-based and covariance matching-based adaptive robust spacecraft attitude estimation algorithm. By constructing a nonlinear regression model, the measurement noise based on robust filtering is derived. The error function is used to improve the Huber density function, which can enhance the estimation accuracy of the robust algorithm. In addition, the process noise is corrected by introducing a fading factor. The Kalman filter gain derived from the additive quaternion is employed to satisfy the quaternion normalization constraint and avoid the covariance singularity. Simulation results show that the proposed adaptive robust attitude estimation algorithms are more robust than the existing methods under large initial attitude error conditions.