GMM-based adaptive extended Kalman filter design for satellite attitude estimationi under thruster induced disturbances

Abstract

in addition, it also impacts on the star image because the impulsive torque creates a sudden change in the angular velocity in the satellite dynamics. This makes the noise of the star image non-Gaussian, which may require introducing a method for dealing with non-Gaussian measurement noise. To meet this goal, in this study, an attitude extended Kalman filter is implemented to predict measurement vectors with predicted states. The GMM (Gaussian mixture model) is connected in this sequence, giving weighting parameters to each Gaussian density and resulting in the better prediction of measurement vectors. Simulation results show that the GMM-EKF exhibits a better performance than the EKF, UKF and saturation based adaptive Kalman filter for attitude estimation. Therefore, the GMM-EKF could be a more attractive approach for satellite missions involving thruster utilization.

Star images from star trackers are usually defocused to capture stars over an exposure time for better centroid measurements. While a satellite is maneuvering, the star point on the screen of the camera is affected by the satellite, which results in the degradation of centroid measurement accuracy. Additionally, this could result in a worse star vector outcome. Before satellites maneuvering, onboard thrusters are used to maintain the commanded satellite attitude under orbit disturbances. Since there are disturbances from the thruster misalignment and force uncertainty, and torque is generated by an impulsive shape signal from the torque command, it is difficult to generate target torque