Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30225
A New Version of Unscented Kalman Filter

Authors: S. A. Banani, M. A. Masnadi-Shirazi


This paper presents a new algorithm which yields a nonlinear state estimator called iterated unscented Kalman filter. This state estimator makes use of both statistical and analytical linearization techniques in different parts of the filtering process. It outperforms the other three nonlinear state estimators: unscented Kalman filter (UKF), extended Kalman filter (EKF) and iterated extended Kalman filter (IEKF) when there is severe nonlinearity in system equation and less nonlinearity in measurement equation. The algorithm performance has been verified by illustrating some simulation results.

Keywords: extended Kalman filter, unscented kalman filter, Iterated EKF, Nonlinearstate estimator

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2406


[1] H. J. Kushner, ''Dynamical equations for optimum nonlinear filtering''. J. Different. Equat., vol. 3, pp.179-190, 1967.
[2] A. H. Jazwinski, Stochastic Processes and Filtering Theory. San Diego, CA: Academic, 1970.
[3] H. W. Sorenson, Ed., Kalman Filtering: Theory and Application. Piscataway, NJ: IEEE, 1985.
[4] Y. Bar-Shalom and Li X.R., Estimation and Tracking: Principles, Techniques, and Software, Artech House, 1993.
[5] T.H., Kerr, ''Streamlining measurement iteration for EKF target tracking,''IEEE Trans. on Aerospace and Electronic Systems, vol.27, No.2, pp. 408-421, 1991.
[6] S. Julier, J. Uhlmann, and H.F. Durrant-White, ''A new method for nonlinear transformation of means and covariances in filters and estimators,'' IEEE Trans. Automatic Control, vol.45, pp.477- 482, March 2000.
[7] E. A. Wan and R. van der Merwe, ''The unscented Kalman filter for nonlinear estimation,'' in Proc. IEEE Symp. Adaptive Systems for Signal Proc. Comm. and Control (AS-SPCC), (Lake Louise, Alberta, Canada), pp.153-158, 2000.
[8] T. S. Schei, ''A finite-difference method for linearization in nonlinear estimation algorithm,'' Automatica, vol. 33, no. 11, pp. 2053-2058, 1997.
[9] K. Ito and K. Xiong, ''Gaussian filters for nonlinear filtering problems,'' IEEE Trans. Automatic Control, vol. 45, pp. 910-927, May 2000.
[10] T. Lefebvre. H. Bruyninckx, and J.De Schutter, “Kalman Filters for Nonlinear Systems: A Comparison of Performance.” Intl. J. of Control, vol. 77, no. 7, pp. 639-653, May 2004.
[11] Z. Zhang and O. Faugeras, 3D Dynamic Scene Analysis: A Stereo Based Approach, Springer-Verlag, Berlin, Germany, 1992.