Huber-based novel robust unscented Kalman filter

Huber-based novel robust unscented Kalman filter

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Science, Measurement & Technology — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This study concerns the unscented Kalman filter (UKF) for the non-linear dynamic systems with error statistics following non-Gaussian probability distributions. A novel robust unscented Kalman filter (NRUKF) is proposed. In the NRUKF the measurement information (measurements or measurements noise) is reformulated using Huber cost function, then the standard unscented transformation (UT) is applied to exact non-linear measurement equation. Compared with the conventional Huber-based unscented Kalman filter (HUKF) which is derived by applying the Huber technique to modify the measurement update equations of the standard UKF, the NRUKF, without linear (statistical linear) approximation, has much-improved performance and versatility with maintaining the robustness. Then the NRUKF is applied to the target tracking problem. The validity of the algorithm is demonstrated through numerical simulation study.


    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
      • Merwe, R.V.D.: `Sigma-point Kalman filters for probabilistic inference in dynamic state-space models', 2004, PhD, Oregon Health and Science University, OGI School of Science and Engineering, Portland, USA.
    7. 7)
    8. 8)
      • D. Williamson . (1991) Digital control and implementation.
    9. 9)
    10. 10)
    11. 11)
      • G. Durharasad , S.S. Thakur . Robust dynamic state estimation of power system based on M-estimation and realistic modeling of system dynamics. IEEE Trans. Power Syst. IEEE Trans. Power Electron. , 2 , 1331 - 1336
    12. 12)
    13. 13)
      • A. Doucet , N. de Freitas , N. Gordon . (2001) Sequential Monte Carlo methods in practice.
    14. 14)
      • P.J. Huber . (1981) Robust statistics.
    15. 15)
      • Boncelet, C.G., Dickinson, B.W.: `An approach to robust Kalman filtering', 22ndIEEE Conf. on Decision and Control, Institute of Electrical and Electronics Engineers, 1983, New York, NY, p. 304–305.
    16. 16)
      • R.A. Maronna , R.D. Martin , V.J. Yohai . (2006) Robust statistic: theory and methods.
    17. 17)
    18. 18)
    19. 19)
    20. 20)
      • Karlgaard, C.D.: `Robust adaptive estimation for autonomous rendezvous in elliptical orbit', June 2010, PhD, Virginia Polytechnic Institute and State University, Department of Aerospace and Ocean Engineering, Blacksburg, VA.
    21. 21)
    22. 22)
      • A. Gelb . (1974) Applied optimal estimation.
    23. 23)
    24. 24)
      • Julier, S.: `The scaled unscented transformation', Proc. American Control Conf., May 2002, Anchorage, AK, p. 4555–4559.
    25. 25)
      • Julier, S.: `The spherical simplex unscented transformation', Proc. American Control Conf., June 2003, Denver, Colorado, p. 2430–2434.
    26. 26)
    27. 27)
      • F.R. Hampel , E.M. Ronchetti , P.J. Rousseeuw , W.A. Stahel . (1986) Robust statistics: the approach based on influence functions.
    28. 28)
    29. 29)

Related content

This is a required field
Please enter a valid email address