Performance analysis of Fast Unscented Kalman Filters for Attitude Determination

Attitude determination performance analysis of two newly developed Fast Unscented Kalman Filters for CubeSat platforms is presented. The attitude determination scenario of UNSWs EC0 CubeSat developed by the Australian Centre for Space Engineering Research (ACSER) is used for the simulation experiment. A gyro, a magnetometer, earth and sun sensor observations are simulated and used in various estimation algorithms for state estimation. The state vector consists of the satellites attitude and the gyro bias vector. The EKF, UKF and the new UKFs called the Single Propagation Unscented Kalman Filter (SPUKF) and the Extrapolated Single Propagation Unscented Kalman Filter (ESPUKF) are implemented separately in MATLAB. The computation time and accuracy of all the estimation algorithms are compared. The SPUKF and ESPUKF can reduce the computation time of the UKF by 92.4% and 85.9% respectively in this application, while retaining the estimation accuracy level of the UKF. This makes them more effective than both the EKF and UKF in the resource-constrained case of CubeSats. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.