Robust Kalman Filtering with Single and Multiple Scale Factors for Small Satellite Attitude Estimation

Abstract

In case of normal operational conditions for a satellite, a conventional Kalman Filter gives sufficiently good attitude estimation results. On the other hand, when there is a fault in the measurements then the Kalman filter fails about providing the required accuracy and may even collapse by time. In this paper, a Robust Kalman filtering method is proposed for the attitude estimation problem. By using the proposed method both the Extended Kalman Filter and Unscented Kalman Filter are modified and the new algorithms, which are robust against the measurement malfunctions, are called as the Robust Extended Kalman Filter (REKF) and Robust Unscented Kalman Filter (RUKF), respectively. The adaptation is performed following both single and multiple scale factor based schemes. As an application example the proposed algorithms are applied for attitude estimation of a small satellite and the performance of the robust Kalman filters are compared in case of different measurement faults.