Study of robust H(infinity) filtering application in loosely coupled INS/GPS system

Since a celebrate linear minimum mean square (MMS) Kalman filter in integration GPS/INS system cannot guarantee the robustness performance, a H(infinity) filtering with respect to polytopic uncertainty is designed. The purpose of this paper is to give an illustration of this application and a contrast with traditional Kalman filter. A game theory H(infinity) filter is first reviewed; next we utilize linear matrix inequalities (LMI) approach to design the robust H(infinity) filter. For the special INS/GPS model, unstable model case is considered. We give an explanation for Kalman filter divergence under uncertain dynamic system and simultaneously investigate the relationship between H(infinity) filter and Kalman filter. A loosely coupled INS/GPS simulation system is given here to verify this application. Result shows that the robust H(infinity) filter has a better performance when system suffers uncertainty; also it is more robust compared to the conventional Kalman filter.

[1]  Y. Q. Chen,et al.  Design of minimax robust filtering for an integrated GPS/INS system , 1999 .

[2]  Fang Jiancheng,et al.  SINS/ANS/GPS Integration Using Federated Kalman Filter Based on Optimized Information-Sharing Coefficients , 2005 .

[3]  Xuemin Shen,et al.  Game theory approach to discrete H∞ filter design , 1997, IEEE Trans. Signal Process..

[4]  Xiao-kui Yue,et al.  H∞ Sub-optimal Filter for Low-cost Integrated Navigation System , 2004 .

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

[6]  Alexandre Trofino,et al.  Robust H∞, filtering for uncertain linear systems with unstable modes , 2004 .

[7]  Alan J. Laub,et al.  The LMI control toolbox , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[8]  D. Simon.  Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[9]  Lihua Xie,et al.  Robust filtering for uncertain systems with unstable modes , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

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

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

[12]  A. Tsourdos,et al.  Robust nonlinear filtering for INS/GPS UAV localization , 2008, 2008 16th Mediterranean Conference on Control and Automation.

[13]  WuLigang,et al.  Control and filtering for discrete linear repetitive processes with $${\fancyscript{H}_{\infty}}$$ and l2---l∞ performance , 2009 .

[14]  E. Rogers,et al.  Control and filtering for discrete linear repetitive processes with $${\fancyscript{H}_{\infty}}$$ and ℓ2–ℓ∞ performance , 2009, Multidimens. Syst. Signal Process..

[15]  S. Jin,et al.  Robust H/sub /spl infin// filtering for polytopic uncertain systems via convex optimisation , 2001 .

[16]  Aboelmagd Noureldin,et al.  Performance Enhancement of MEMS-Based INS/GPS Integration for Low-Cost Navigation Applications , 2009, IEEE Transactions on Vehicular Technology.

[17]  Johan Löfberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004 .

[18]  Garry A. Einicke,et al.  Bounded Constrained Filtering for GPS/INS Integration , 2013, IEEE Transactions on Automatic Control.

[19]  U. Shaked,et al.  H,-OPTIMAL ESTIMATION: A TUTORIAL , 1992 .