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Huber-based novel robust unscented Kalman filter

Huber-based novel robust unscented Kalman filter

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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.

References

    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)
      • Digital control and implementation
    9. 9)
    10. 10)
    11. 11)
      • Robust dynamic state estimation of power system based on M-estimation and realistic modeling of system dynamics
    12. 12)
    13. 13)
      • Sequential Monte Carlo methods in practice
    14. 14)
      • 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)
      • 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)
      • 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)
      • Robust statistics: the approach based on influence functions
    28. 28)
    29. 29)
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