Turbulence Error Modeling and Restriction for Satellite Attitude Determination System Based on Improved Maximum Correntropy Kalman Filter

• Main Authors: Jiongqi Wang, Yuyun Chen, Bowen Hou, Bowen Sun, Jian Peng, Zhangming He
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Satellite attitude; star tracker; turbulence influence; fading factor; maximum correntropy filter; accuracy analysis
• Online Access: https://ieeexplore.ieee.org/document/8848495/

In the process of satellite attitude determination, satellites or sensors themselves often encounter a variety of turbulence influences due to the complexity of space environments. Such influences can lead to the mutation and non-Gaussian noises for the attitude determination system. To solve these problems, in this paper, we construct a unified error model for the turbulence influences, which is a non-Gaussian noise model, and propose an improved attitude filter method to restrict the turbulence noises and the system mutation to enhance attitude determination accuracy and robustness. The unified error model combined with jitters and vibrations in the actual process of satellite attitude determination is firstly designed. Then an Improved Adaptive Kalman filter (IAKF) based on both the Strong Tracking Filter (STF) and the Maximum Correntropy Filter (MCKF) is put forward. By using of the optimization principle with both of fading factor and Maximum Correntropy Criterion (MCC), this proposed filter algorithm can suppress the influences of system mutations and non-Gaussian noises at the same time. It can eliminate the system mutations and the turbulence errors, and achieve excellent robustness and the attitude determination accuracy for the nonlinear system. Extensive simulations of the proposed filter are conducted under the conditions of the Gaussian noises, system mutation with large outliers, non-Gaussian noise with turbulence noises, and both the mutation and non-Gaussian turbulence error. The results demonstrate that our filter outperforms the existing attitude filter algorithms significantly.