Hostname: page-component-76fb5796d-dfsvx Total loading time: 0 Render date: 2024-04-25T14:10:52.409Z Has data issue: false hasContentIssue false
  • English
  • Français

Robust Adaptive Kalman Filtering – a method based on quasi-accurate detection and plant noise variance–covariance matrix tuning

Published online by Cambridge University Press:  28 September 2016

Xiaowen Luo  and
Haitao Wang
Xiaowen Luo*
Affiliation:
(Second Institute of Oceanography, State Oceanic Administration, 36 Baochu Bei lu, Hangzhou 310012, China)
Haitao Wang
Affiliation:
(Institute of Geodesy and Geophysics, Chinese Academy of Sciences, 340 Xudong Road, Wuhan 430077, China)
*
(E-mail: cdslxw@163.com)
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper, we propose an algorithm for tuning both the kinematic and measurement noise Variance–Covariance (VCV) matrices to produce a more robust and adaptive Kalman filter. The proposed algorithm simultaneously considers both observation outliers and abrupt changes. This algorithm may be divided into two basic parts: 1. Robust estimation, from which the position components of the filtering estimates and the equivalent weight factor matrix can be obtained; and 2. Adaptive estimation, from which the adaptive kinematic noise VCV tuning matrix is calculated. To demonstrate the efficiency of our algorithm, we process a set of kinematic Global Positioning System (GPS) data received from a rover mounted on an aeroplane. The processing results are found to be very satisfactory, with observation outliers and abrupt changes detected and dealt with accordingly. The detailed calculation procedure for the adaptive VCV tuning matrix is also described.

Keywords

Robust adaptive Kalman filteringSystem kinematic noise VCV matrixQuasi-accurate detectionAbrupt change detection
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 1 , January 2017 , pp. 137 - 148
Copyright
Copyright © The Royal Institute of Navigation 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Baarda, W. (1968). A Testing Procedure for Use in Geodetic Networks. Publications on Geodesy, New Series, Vol. 2, No. 5, Netherlands Geodetic Commission, Delft.CrossRefGoogle Scholar
Cui, X.Q. and Yang, Y.X. (2006). Adaptively robust filtering with classified adaptive factors. Progress in Natural Science, 16(8), 846–851.Google Scholar
Gao, S.S., Zhong, Y.M. and Li, W. (2011). Robust adaptive filtering method for SINS/SAR integrated navigation system. Aerospace Science & Technology, 15(6), 425–430.Google Scholar
Gelb, A., Kasper, J.F., Nash, R.A., Price, C.F. and Sutherland, A.A. (2001). Applied Optimal Estimation. MIT Press, Cambridge, MA, 16th ed. Google Scholar
Geng, Y.R. and Wang, J.L. (2008). Adaptive estimation of multiple fading factors in Kalman filter for navigation applications. GPS Solutions, 12(4), 273–279.CrossRefGoogle Scholar
Guo, J.F., Ou, J.K. and Wang, H.T. (2007). Quasi-Accurate Detection of Outliers for Correlated Observations. Journal of Surveying Engineering, 133(3), 129–133.Google Scholar
Gustafsson, F. (2000). Adaptive filtering and Change detection. Wiley, New York.Google Scholar
Hajiyev, C. and Soken, H.E. (2013). Robust adaptive Kalman filter for estimation of UAV dynamics in the presence of sensor/actuator faults. Aerospace Science & Technology, 28(1), 376–383.Google Scholar
Jazwinski, A.H. (1970). Stochastic Processes and Filtering Theory. Academic Press, New York and London.Google Scholar
Koch, K.R. and Yang, Y.X. (1998). Robust Kalman filter for rank deficient observation models. Journal of Geodesy, 72(8), 436–441.Google Scholar
Li, W., Gong, D.R., Liu, M.H., Chen, J.A. and Duan, D.P. (2013). Adaptive robust Kalman filter for relative navigation using global position system. IET Radar, Sonar and Navigation 7(5), 471–479.CrossRefGoogle Scholar
Ou, J.K. (1999). Quasi-Accurate Detection of gross errors (QUAD). Acta Geodaetica et Cartographica Sinica, 28(1), 15–20.Google Scholar
Teunissen, P.J.G. (1990). Quality control in integrated navigation systems. Aerospace and Electronic Systems Magazine, IEEE, 5(7), 35–41.CrossRefGoogle Scholar
Wang, H.T., Ou, J.K. and Guo, J.F. (2007). Strict Weight Matrix Definition of GPS Carrier-Smoothed Code and its Application to Relative Positioning of Satellite Formation Flying. Journal of Astronautics, 28(2), 282–286.Google Scholar
Yang, Y.X., He, H.B. and Xu, G.C. (2001). Adaptively robust filtering for kinematic geodetic positioning. Journal of Geodesy, 75(2–3), 109–116.Google Scholar
Yang, Y.X. and Gao, W.G. (2005). Comparison of Adaptive Factors in Kalman Filters on Navigation Results. Journal of Navigation, 58(3), 471–478.Google Scholar
Yang, Y.X. and Gao, W.G. (2006). An optimal adaptive Kalman filter. Journal of Geodesy, 80(4), 177–183.Google Scholar