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

Abstract

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.

Centered Error Entropy-Based Sigma-Point Kalman Filter for Spacecraft State Estimation with Non-Gaussian Noise

“…Although the SPKF algorithm performs well in ideal Gaussian white noise, the actual operating conditions of the spacecraft in orbit are complicated. Space environmental interference, solar panel jitter, and flicker noise will make the noise no longer meet the Gaussian distribution and present a heavy-tailed non-Gaussian situation [19][20][21]. In the nonlinear non-Gaussian system, the classical SPKF filtering method is no longer applicable, and there will be obvious accuracy degradation or even filtering divergence.…”