Fault Tolerant Attitude Estimation for Pico Satellites Using Robust Adaptive UKF

Abstract Unscented Kalman Filter (UKF) is a filtering algorithm which gives sufficiently good estimation results for estimation problems of nonlinear systems even when high nonlinearity is in question. However, in case of system uncertainty or measurements malfunctions the UKF becomes to be inaccurate and diverges by time. This study, introduces a fault tolerant attitude estimation algorithm for pico satellites. The algorithm uses a Robust Adaptive Unscented Kalman Filter (RAUKF) which performs correction for process noise covariance (Q-adaptation) or measurement noise covariance (R-adaptation) depending on the type of the fault. By the use of a newly proposed adaptation scheme for the conventional UKF algorithm, the fault is detected; isolated and the essential adaptation procedure is followed in accordance with the fault type. The proposed algorithm is tested as a part of the attitude estimation algorithm of a pico satellite, a satellite type for which computational convenience is necessary because of the design limitations.

[1]  Xuemin Tian,et al.  An Adaptive UKF Algorithm for Process Fault Prognostics , 2009, 2009 Second International Conference on Intelligent Computation Technology and Automation.

[2]  R. Mehra.  Approaches to adaptive filtering , 1972 .

[3]  James B. Rawlings,et al.  A new autocovariance least-squares method for estimating noise covariances , 2006, Autom..

[4]  Chingiz Hajiyev,et al.  Fault diagnosis and reconfiguration in flight control systems , 2003 .

[5]  F. Markley,et al.  Unscented Filtering for Spacecraft Attitude Estimation , 2003 .

[6]  Chingiz Hajiyev,et al.  Sensor and control surface/actuator failure detection and isolation applied to F‐16 flight dynamic , 2005 .

[7]  Halil Ersin Soken,et al.  Reconfigurable UKF for In-Flight Magnetometer Calibration and Attitude Parameter Estimation , 2011 .

[8]  H.F. Durrant-Whyte,et al.  A new approach for filtering nonlinear systems , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[9]  A. H. Mohamed,et al.  Adaptive Kalman Filtering for INS/GPS , 1999 .

[10]  I.M. Ross,et al.  NPSAT1 Parameter Estimation Using Unscented Kalman Filtering , 2007, 2007 American Control Conference.

[11]  Jinling Wang,et al.  Adaptive estimation of multiple fading factors in Kalman filter for navigation applications , 2008 .

[12]  T. Moore,et al.  Adaptive Kalman filtering algorithms for integrating GPS and low cost INS , 2004, PLANS 2004. Position Location and Navigation Symposium (IEEE Cat. No.04CH37556).

[13]  Donald M. Wiberg,et al.  An online parameter estimator for quick convergence and time-varying linear systems , 2000, IEEE Trans. Autom. Control..

[14]  Halil Ersin Soken,et al.  Adaptive Kalman Filter with Multiple Fading Factors for UAV State Estimation , 2009 .

[15]  R. Mehra.  On the identification of variances and adaptive Kalman filtering , 1970 .

[16]  James R. Wertz,et al.  Spacecraft attitude determination and control , 1978 .

[17]  M. Simandl,et al.  Methods for Estimating State and Measurement Noise Covariance Matrices: Aspects and Comparison , 2009 .

[18]  Chongzhao Han,et al.  Adaptive UKF for target tracking with unknown process noise statistics , 2009, 2009 12th International Conference on Information Fusion.

[19]  Chan Gook Park,et al.  Adaptive two‐stage Kalman filter in the presence of unknown random bias , 2006 .

[20]  Mingquan Lu,et al.  An Adaptive UKF Filtering Algorithm for GPS Position Estimation , 2009, 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing.

[21]  Yuqing He,et al.  Adaptive unscented Kalman filter for estimation of modelling errors for helicopter , 2009, 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO).