Robust multiple model adaptive estimation for spacecraft autonomous navigation

Abstract This paper focuses on the development of a robust multiple model adaptive estimation (RMMAE) algorithm and its performance analysis. The main goal of this work is to enhance the robustness of the estimator against the model parameter identification error. A proof is provided that shows the convergence property of the proposed algorithm. Further analysis shows that the RMMAE algorithm guarantees a bounded energy gain from the model parameter identification error to the estimation error. The performance of the RMMAE is evaluated via simulations for spacecraft autonomous navigation. Simulation results demonstrate the effectiveness of the new algorithm compared with the extended Kalman filter (EKF), the unscented Kalman filter (UKF), the robust Kalman filter (RKF) and the multiple model adaptive estimation (MMAE).

[1]  Josef Shinar,et al.  Efficient Multiple Model Adaptive Estimation in Ballistic Missile Interception Scenarios , 2002 .

[2]  B. Anderson,et al.  Optimal Filtering , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  X. Kai,et al.  Robust extended Kalman filtering for nonlinear systems with multiplicative noises , 2011 .

[4]  W. Zheng,et al.  Research of Pulsars-Based Timing by Using Kalman Filter , 2009 .

[5]  S. Sheikh.  The Use Of Variable Celestial X-ray Sources For Spacecraft Navigation , 2005 .

[6]  Kai Xiong,et al.  Robust Extended Kalman Filtering for Nonlinear Systems With Stochastic Uncertainties , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[7]  Y. Bar-Shalom,et al.  The interacting multiple model algorithm for systems with Markovian switching coefficients , 1988 .

[8]  Jyh-Ching Juang,et al.  Design and verification of a magnetometer-based orbit determination and sensor calibration algorithm , 2012 .

[9]  V. Jilkov,et al.  Survey of maneuvering target tracking. Part V. Multiple-model methods , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[10]  Y. Bar-Shalom,et al.  Multiple-model estimation with variable structure , 1996, IEEE Trans. Autom. Control..

[11]  Lennart Svensson,et al.  A New Multiple Model Filter With Switch Time Conditions , 2010, IEEE Transactions on Signal Processing.

[12]  Feng Laiping,et al.  Visibility Analysis of X-ray Pulsar Navigation , 2009 .

[13]  X. Rong Li,et al.  Best Model Augmentation for Variable-Structure Multiple-Model Estimation , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[14]  Virgil H. Lucke.  Dynasar - Analysis methods developed for the dynamic systems analyzer , 1965 .

[15]  Rong Yang,et al.  RF Emitter Geolocation using Amplitude Comparison with Auto-Calibrated Relative Antenna Gains , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[16]  D. Pines,et al.  SPACECRAFT NAVIGATION USING X-RAY PULSARS , 2006 .

[17]  Ying Liu,et al.  Application of STEKF in X-ray Pulsar Based Autonomous Navigation , 2012 .

[18]  Jason L. Speyer,et al.  X-Ray Pulsar-Based Relative Navigation using Epoch Folding , 2011, IEEE Transactions on Aerospace and Electronic Systems.

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

[20]  Inseok Hwang,et al.  Algorithm for Performance Analysis of the IMM Algorithm , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[21]  Fuwen Yang,et al.  Robust Kalman filtering for discrete time-varying uncertain systems with multiplicative noises , 2002, IEEE Trans. Autom. Control..

[22]  Jie Ma,et al.  Pulsar navigation for interplanetary missions using CV model and ASUKF , 2012 .

[23]  D. Magill.  Optimal adaptive estimation of sampled stochastic processes , 1965 .

[24]  Afshin Izadian,et al.  Fault Diagnosis of MEMS Lateral Comb Resonators Using Multiple-Model Adaptive Estimators , 2010, IEEE Transactions on Control Systems Technology.

[25]  Mohammad Abdelrahman,et al.  Simultaneous spacecraft attitude and orbit estimation using magnetic field vector measurements , 2011 .

[26]  Jason L. Speyer,et al.  Relative Navigation Between Two Spacecraft Using X-ray Pulsars , 2011, IEEE Transactions on Control Systems Technology.

[27]  Tao Zhang,et al.  Navigation using binary pulsars , 2012 .

[28]  Jiancheng Fang,et al.  Spacecraft autonomous navigation using unscented particle filter-based celestial/Doppler information fusion , 2008 .

[29]  L. D. Liu,et al.  Robust Kalman filtering for discrete-time nonlinear systems with parameter uncertainties , 2012 .

[30]  Jie Ma,et al.  X-ray pulsar navigation method for spacecraft with pulsar direction error , 2010 .

[31]  Li Zhang,et al.  Algorithm Analysis of Autonomous Navigation of Spacecraft Based on X-ray Pulsars , 2012 .

[32]  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.

[33]  Jianye Liu,et al.  Augmentation of XNAV System to an Ultraviolet Sensor-Based Satellite Navigation System , 2009, IEEE Journal of Selected Topics in Signal Processing.

[34]  Garry A. Einicke,et al.  Robust extended Kalman filtering , 1999, IEEE Trans. Signal Process..

[35]  S. R. Olsen,et al.  An in situ rapid heat–quench cell for small-angle neutron scattering , 2008 .

[36]  Uri Shaked,et al.  Robust discrete-time minimum-variance filtering , 1996, IEEE Trans. Signal Process..

[37]  Peter S. Maybeck,et al.  Multiple-model adaptive estimation using a residual correlation Kalman filter bank , 2000, IEEE Trans. Aerosp. Electron. Syst..

[38]  Yang Cheng,et al.  Generalized Multiple-Model Adaptive Estimation using an Autocorrelation Approach , 2011, IEEE Transactions on Aerospace and Electronic Systems.