An Adaptive Fuzzy Strong Tracking Kalman Filter for GPS/INS Navigation

The Kalman filtering is a form of recursive optimal estimation, which has been widely applied to the navigation designs. Kalman filter requires that all the plant dynamics and noise processes are exactly known, and the noise process is zero mean white noise. If the theoretical behavior of a filter and its actual behavior do not agree, divergence problems will occur. The adaptive algorithm has been one of the approaches to prevent divergence problem of the Kalman filter when precise knowledge on the system models are not available. One of the adaptive methods called the strong tracking Kalman filter (STKF) properly employs a scaling factor, by monitoring the innovation information. Traditional approach for selecting the parameter in the STKF heavily relies on personal experience or computer simulation. In order to resolve this shortcoming, a novel scheme called the adaptive fuzzy strong tracking Kalman filter (AFSTKF) is proposed. In the AFSTKF, the fuzzy logic reasoning approach is incorporated into the STKF. By monitoring parameters based on the innovation information, the fuzzy logic adaptive system (FLAS) is designed for dynamically adjusting the parameter according to the change in vehicle dynamics. Integrated navigation processing using the AFSTKF will be conducted to validate the effectiveness of the proposed strategy. The performance of the proposed AFSTKF scheme will be assessed and compared to those of conventional EKF and STKF.

[1]  S. Chiu,et al.  Applying fuzzy logic to the Kalman filter divergence problem , 1993, Proceedings of IEEE Systems Man and Cybernetics Conference - SMC.

[2]  P. Frank,et al.  Strong tracking filtering of nonlinear time-varying stochastic systems with coloured noise: application to parameter estimation and empirical robustness analysis , 1996 .

[3]  J. Z. Sasiadek,et al.  Fuzzy adaptive Kalman filtering for INS/GPS data fusion , 1999, Proceedings of the 2000 IEEE International Symposium on Intelligent Control. Held jointly with the 8th IEEE Mediterranean Conference on Control and Automation (Cat. No.00CH37147).

[4]  R. Mehra.  On-line identification of linear dynamic systems with applications to Kalman filtering , 1971 .

[5]  Kazuyuki Kobayashi,et al.  Estimation of absolute vehicle speed using fuzzy logic rule-based Kalman filter , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[6]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

[7]  Raman K. Mehra,et al.  Approaches to adaptive filtering , 1970 .

[8]  Jun Liu,et al.  Particle Filter Based on Strong Tracking Filter , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[9]  Robert Sutton,et al.  Soft computing techniques in the design of a navigation, guidance and control system for an autonomous underwater vehicle , 2007 .

[10]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[11]  Per Enge,et al.  Gps Navigation Algorithms , 1996 .

[12]  A. A. Soloviev,et al.  Fuzzy Adaptive Stabilization of Higher Order Kalman Filters in Application to Precision Kinematic GPS , 1996 .

[13]  J. Farrell,et al.  The global positioning system and inertial navigation , 1999 .

[14]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.