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Improving Adaptive Kalman Estimation in GPS/INS Integration

Published online by Cambridge University Press:  09 August 2007

Weidong Ding ,
Jinling Wang ,
Chris Rizos  and
Doug Kinlyside
Weidong Ding*
Affiliation:
(The University of New South Wales)
Jinling Wang
Affiliation:
(The University of New South Wales)
Chris Rizos
Affiliation:
(The University of New South Wales)
Doug Kinlyside
Affiliation:
(Department of Lands, NSW)
*
(Email: [email protected])
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Abstract

The central task of GPS/INS integration is to effectively blend GPS and INS data together to generate an optimal solution. The present data fusion algorithms, which are mostly based on Kalman filtering (KF), have several limitations. One of those limitations is the stringent requirement on precise a priori knowledge of the system models and noise properties. Uncertainty in the covariance parameters of the process noise (Q) and the observation errors (R) may significantly degrade the filtering performance. The conventional way of determining Q and R relies on intensive analysis of empirical data. However, the noise levels may change in different applications. Over the past few decades adaptive KF algorithms have been intensively investigated with a view to reducing the influence of the Q and R definition errors. The covariance matching method has been shown to be one of the most promising techniques. This paper first investigates the utilization of an online stochastic modelling algorithm with regards to its parameter estimation stability, convergence, optimal window size, and the interaction between Q and R estimations. Then a new adaptive process noise scaling algorithm is proposed. Without artificial or empirical parameters being used, the proposed adaptive mechanism has demonstrated the capability of autonomously tuning the process noise covariance to the optimal magnitude, and hence improving the overall filtering performance.

Keywords

GPSINSAdaptive Kalman filter
Type
Research Article
Information
The Journal of Navigation , Volume 60 , Issue 3 , September 2007 , pp. 517 - 529
Copyright
Copyright © The Royal Institute of Navigation 2007

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References

REFERENCES

Brown, R. G. and Hwang, P. Y. C. (1997). Introduction to Random Signals and Applied Kalman Filtering. John Willey & Sons, New York.Google Scholar
Grewal, M. S. and Andrews, A. P. (1993). Kalman Filtering Theory and Practice. Prentice Hall, USA.Google Scholar
Grewal, M. S. and Weil, L. R. (2001). Global Positioning Systems, Inertial Navigation, and Integration. John Wiley & Sons, USA.Google Scholar
Hide, C., Michaud, F. and Smith, M. (2004a). Adaptive Kalman filtering algorithms for integrating GPS and low cost INS, IEEE Position Location and Navigation Symposium, Monterey California, 227–233.Google Scholar
Hide, C., Moore, T. and Smith, M. (2004b). Multiple model Kalman filtering for GPS and low-cost INS integration, ION GNSS 17th international technical meeting of the satellite division, Long Beach California, 1096–1103.Google Scholar
Hu, C., Chen, W., Chen, Y. and Liu, D. (2003). Adaptive Kalman filtering for vehicle navigation, Journal of Global Positioning Systems, 2(1), 42–47.CrossRefGoogle Scholar
Mehra, R. K. (1970). On the identification of variances and adaptive Kalman filtering. IEEE Transactions on automatic control, AC-15(2): 175–184.CrossRefGoogle Scholar
Mohamed, A. H. and Schwarz, K. P. (1999). Adaptive Kalman filtering for INS/GPS, Journal of Geodesy, 73, 193–203.CrossRefGoogle Scholar
Salychev, O. S. (2004). Applied Inertial Navigation Problems and Solutions. BMSTU press, Moscow.Google Scholar
Wang, J. (2000). Stochastic modelling for RTK GPS/GLONASS positioning and navigation, Journal of the US Institute of Navigation, 46(4), 297–305.CrossRefGoogle Scholar
Wang, J., Stewart, M. and Tsakiri, M. (1999). Online stochastic modelling for INS/GPS integration, ION GPS-99 proceedings, Nashville, Tennessee, 1887–1895.Google Scholar
Yang, Y. (2005). Comparison of adaptive factors in Kalman filters on navigation results. The Journal of Navigation, 58, 471–478.CrossRefGoogle Scholar
Yang, Y. and Gao, W. (2006). An optimal adaptive Kalman filter. Journal of Geodesy, 80(4), 177–183.CrossRefGoogle Scholar
Yang, Y. and Xu, T. (2003). An adaptive Kalman filter based on sage windowing weights and variance components. The Journal of Navigation, 56, 231–240.CrossRefGoogle Scholar