Regularized robust filter for attitude determination system with relative installation error of star trackers

Abstract As one of the most critical issues for high-accuracy satellite attitude determination, the relative installation error of star tracker usually leads to inconsistency of the output attitude information. In this paper, an approach named regularized robust filter algorithm is proposed to control the relative installation error of star tracker in the attitude measurement data. Based on the uncertainty model established for the attitude measurement system, the weighted least square solution is presented and the regularized robust filter is deduced firstly. The algorithm parameters are then optimized with the design indices in order to minimize the upper boundary for the variance of the estimated error. Compared with the traditional Kalman filter, the regularized robust filter takes into consideration the effects of model uncertainty, which can be used to optimize the filter parameters during its design stage. Thus, the information of both the system model and the measurement data can be applied effectively. Moreover, the existence conditions need not be validated in the proposed filter algorithm, which is convenient for on-orbit application. Finally, simulation results demonstrate the validity and efficiency of the proposed method. The relative installation error of attitude determination is mostly reduced and the estimation precision is improved greatly.

[1]  Haiyin Zhou,et al.  Low-frequency Periodic Error Identification and Compensation for Star Tracker Attitude Measurement , 2012 .

[2]  Ludovic Blarre,et al.  SED16 Autonomous Star Sensor Product Line in Flight Results, New Developments and Improvements in Progress , 2005 .

[3]  M. Terra,et al.  Robust Kalman filter for descriptor systems , 2004 .

[4]  Mark E. Pittelkau,et al.  Kalman Filtering for Spacecraft System Alignment Calibration , 2001 .

[5]  Carlos E. de Souza,et al.  Robust /spl Hscr//sub /spl infin// filtering for discrete-time linear systems with uncertain time-varying parameters , 2006, IEEE Transactions on Signal Processing.

[6]  Ian R. Petersen,et al.  A Discrete-Time Robust Extended Kalman Filter for Uncertain Systems With Sum Quadratic Constraints , 2009, IEEE Transactions on Automatic Control.

[7]  F. Markley.  Attitude Error Representations for Kalman Filtering , 2003 .

[8]  U. Schmidt,et al.  ASTRO 15 Star Tracker Flight Experience and Further Improvements towards the ASTRO APS Star Tracker , 2008 .

[9]  K. Xiong,et al.  Adaptive robust extended Kalman filter for nonlinear stochastic systems , 2008 .

[10]  Takanori Iwata,et al.  Precision Attitude and Orbit Control System for the Advanced Land Observing Satellite , 2003 .

[12]  Ali H. Sayed,et al.  Regularized robust filters for time-varying uncertain discrete-time systems , 2004, IEEE Transactions on Automatic Control.

[13]  Shie Mannor,et al.  A Kalman Filter Design Based on the Performance/Robustness Tradeoff , 2008, IEEE Transactions on Automatic Control.

[14]  Malcolm D. Shuster,et al.  BATCH ESTIMATION OF SPACECRAFT SENSOR ALIGNMENTS II. Absolute Alignment Estimation , 2006 .

[15]  Huijun Gao,et al.  ${\cal H}_{\infty}$ Estimation for Uncertain Systems With Limited Communication Capacity , 2007, IEEE Transactions on Automatic Control.

[16]  Jang Gyu Lee,et al.  An Extended Robust H$_infty$Filter for Nonlinear Constrained Uncertain Systems , 2006, IEEE Transactions on Signal Processing.