CubeSat attitude determination with decomposed Kalman filters

Abstract

CubeSats are the cost-effective entry to space research and applications. As mission requirements increase to carry out more complex tasks, the constraints on the satellite challenge how attitude estimation and control systems are designed. Limited energy, sensors, and computational capacity require compromises. In this paper, we propose a Kalman filter architecture to reduce the computational cost of attitude estimation, leveraging the conditional independence structure of its physical model. Our method decomposes attitude dynamics and kinematics, leading to a linear attitude quaternion and a nonlinear angular velocity filter. As accommodating all vector measurements would require a nonlinear filter, we propose the virtual sensor paradigm that transforms the nonlinear observation model into a linear one, without relying on approximations. Our numerical experiments showcase superior error dynamics and robustness to epistemic uncertainty compared to a nonlinear quaternionic filter, and we also investigate performance against star tracker measurement frequency and sensitivity to the angle between Sun and Earth magnetic field measurements.