Adaptive Unscented Kalman Filter with multiple fading factors for pico satellite attitude estimation

Thus far, Kalman filter based attitude estimation algorithms have been used in many space applications. When the issue of pico satellite attitude estimation is taken into consideration, general linear approach to Kalman filter becomes insufficient and Extended Kalman Filters (EKF) are the types of filters, which are designed in order to overrun this problem. However, in case of attitude estimation of a pico satellite via magnetometer data, where the nonlinearity degree of both dynamics and measurement models are high, EKF may give inaccurate results. Unscented Kalman Filter (UKF) that does not require linearization phase and so Jacobians can be preferred instead of EKF in such circumstances. Nonetheless, if the UKF is built with an adaptive manner, such that, faulty measurements do not affect attitude estimation process, accurate estimation results even in case of measurement malfunctions can be guaranteed. In this study an Adaptive Unscented Kalman Filter with multiple fading factors based gain correction is introduced and tested on the attitude estimation system of a pico satellite by the use of simulations.

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

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

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

[4]  P. J. Escamilla-Ambrosio,et al.  Hybrid Kalman filter-fuzzy logic adaptive multisensor data fusion architectures , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[5]  Wu Chen,et al.  Adaptive Kalman Filtering for Vehicle Navigation , 2003 .

[6]  Dah-Jing Jwo,et al.  A practical note on evaluating Kalman filter performance optimality and degradation , 2007, Appl. Math. Comput..

[7]  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).

[8]  E. J. Lefferts,et al.  Kalman Filtering for Spacecraft Attitude Estimation , 1982 .

[9]  Mark L. Psiaki,et al.  N 8 9 - 1 5 9 5 1 Three-Axis Attitude Determination via Kalman Filtering of Magnetometer Data , 2003 .

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

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

[12]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

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

[14]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[15]  Ch. Hajiyev,et al.  Adaptive filtration algorithm with the filter gain correction applied to integrated INS/radar altimeter , 2007 .