Fault-Tolerant Attitude Estimation for Satellite Using Federated Unscented Kalman Filter

Abstract

Satellites provide various services essential to the modern life of human being. For example, satellite images are used for many applications such as reconnaissance, geographic information system, etc. Therefore, design and operation requirements of the satellite system have become more severe, and also the system reliability during the operation is required. Satellite attitude control systems including sensors and actuators are critical subsystems, and any fault in the satellite control system can result in serious problems. To deal with this problem, various attitude estimation algorithms using multiple sensors have been actively studied for fault tolerant satellite system (Edelmayer & Miranda, 2007; Jiancheng & Ali, 2005; Karlgaard & Schaub, 2008; Kerr, 1987; Xu, 2009). Satellites use various attitude sensors such as gyroscopes, sun sensors, star sensors, magnetometers, and so on. With these sensors, satellite attitude information can be obtained using the estimation algorithms including Kalman filter, extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter. Agrawal et al. and Nagendra et al. presented the attitude estimation algorithm based on Kalman filter for satellite system (Agrawal & Palermo, 2002; Nagendra et al., 2002). Mehra and Bayard dealt with the problems of satellite attitude estimation based on the EKF algorithm using the gyroscope and star tracker as attitude sensors (Mehra & Bayard, 1995). In the EKF algorithm, the nonlinearities of the satellite system are approximated by the first-order Taylor series expansion, and therefore it sometimes provides undesired estimates when the system has severe nonlinearities. Recently, researches on UKF have been performed because the UKF can capture the posterior mean and covariance to the third order of nonlinear system. It is known that the UKF can provide better results for the estimation of highly nonlinear systems than EKF (Crassidis & Markley, 2003; Jin et al., 2008; Julier & Uhlmann, 2004). Crassidis and Markley proposed the attitude estimation algorithm based on unscented filter, and showed that the fast convergence can be obtained even with inaccurate initial conditions. The UKF was used to solve the relative attitude estimation problem using the modified Rodrigure parameter (MRP), where the gyroscope, star tracker, and laser rendezvous radar were employed as the attitude sensors (Jin et al., 2008). For multi-sensor systems, there are two different filter schemes for the measured sensor data process: centralized Kalman fileter (CKF) and decentralized Kalman filter (DKF) (Kim & Hong, 2003). In the CKF, all measured sensor data are processed in the center site, and