A Measurement-Based Robust Adaptive Kalman Filtering Algorithm

In the case that the accuracy of standard kalman filter (SKF) declines when the noise statistical characteristics are unknown or changing, a measurement-based adaptive kalman filtering algorithm (MAKF) is presented. Based on the contrastive analysis of measurement characteristics of different measurement systems, MAKF is put forward to estimate adaptively the measurement noise variance R by co-difference measurement sequences. Simulation is performed by applying this algorithm to the GPS/INS integrated navigation system, the results show that MAKF can track the GPS measurement noise in real time on condition that the GPS measurement noise is unknown or changing, and the filtering accuracy and robustness are superior to those of SKF and an improved Sage-Husa adaptive kalman filtering algorithm.

[1]  Xiyu Li,et al.  A novel information fusion algorithm for GPS/INS navigation system , 2009, 2009 International Conference on Information and Automation.

[2]  Jian Wang,et al.  Study on Information Fusion Algorithm in Embedded Integrated Navigation System , 2008, 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA).

[3]  Peng Yue,et al.  Research on adaptive Kalman filter algorithm based on fuzzy neural network , 2010, The 2010 IEEE International Conference on Information and Automation.

[4]  Aini Hussain,et al.  ANFIS-Based Model for Real-time INS/GPS Data Fusion for Vehicular Navigation System , 2009, 2009 International Conference on Computer Technology and Development.

[5]  Jin Zhi-hua,et al.  The Innovation-Based Estimation Adaptive Kalman Filter Algorithm for INS/GPS Integrated Navigation System , 2006 .

[6]  Gyu-In Jee,et al.  The Stability Analysis of the Adaptive Fading Extended Kalman Filter , 2007, 2007 IEEE International Conference on Control Applications.

[7]  Cui Pingyuan,et al.  Fuzzy Adaptive Interacting Multiple Model Algorithm for INS/GPS , 2007, 2007 International Conference on Mechatronics and Automation.

[8]  Dah-Jing Jwo,et al.  Adaptive Fuzzy Strong Tracking Extended Kalman Filtering for GPS Navigation , 2007, IEEE Sensors Journal.

[9]  Liu Qian.  An improved GPS /DR integrated position adaptive filtering algorithm , 2009 .

[10]  Bo Han,et al.  Adapt the steady-state Kalman gain using the normalized autocorrelation of innovations , 2005, IEEE Signal Processing Letters.

[11]  Antonio F. Gómez-Skarmeta,et al.  Multiple Model based Lane Change Prediction for Road Vehicles with Low Cost GPS/IMU , 2007, 2007 IEEE Intelligent Transportation Systems Conference.

[12]  Audrey Giremus,et al.  Fault detection combining interacting multiple model and multiple solution separation for aviation satellite navigation system , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[13]  Andrew P. Sage,et al.  Adaptive filtering with unknown prior statistics , 1969 .