Skip to main content
Log in

Robust Kalman Filter with Fading Factor Under State Transition Model Mismatch and Outliers Interference

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

In practical applications, due to the complexity of the system, the process equation of the state space model is difficult to match the actual state transition model. In addition, the unreliability of the sensor will cause the measurement to be accompanied by outliers. In this paper, a novel robust Kalman filter with fading factor is proposed to improve the accuracy of state estimation for the linear system under state transition model mismatch and outliers interference. Firstly, in order to modify the state transition model, this filter introduces a fading factor which is modelled as the inverse gamma distribution to update the state prediction covariance. Then, aiming at the phenomenon that the measurement noise does not follow the Gaussian distribution and has nonzero mean characteristics due to outliers interference, the measurement noise is modelled as the generalized hyperbolic skew Student’s t distribution. Finally, the state estimation is realized by using the variational Bayesian. The simulation results show that the estimation accuracy of the proposed filter is higher than that of the Kalman filter and the strong tracking filter.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data Availability

The datasets generated during and/or analyzed during this current study are available from the corresponding author on reasonable request.

References

  1. K. Aas, The generalized hyperbolic skew Student’s t-distribution. J. Financ. Econ. 4(2), 275–309 (2006)

    Google Scholar 

  2. G. Agamennoni, J.I. Nieto, E.M. Nebot, Approximate inference in state-space models with heavy-tailed noise. IEEE Trans. Signal Process. 60(10), 5024–5037 (2012)

    Article  MathSciNet  Google Scholar 

  3. Y. Bar-Shalom, K.C. Chang, H.A.P. Blom, Tracking a maneuvering target using input estimation versus the interacting multiple model algorithm. IEEE Trans. Aerosp. Electron. Syst. 25(2), 296–300 (1989)

    Article  Google Scholar 

  4. Y.T. Chan, A.G.C. Hu, J.B. Plant, A Kalman filter based tracking scheme with input estimation. IEEE Trans. Aerosp. Electron. Syst. 15(2), 237–244 (1979)

    Article  Google Scholar 

  5. J.A. Hage, P. Xu, P. Bonnifait, Student’s t information filter with adaptive degree of freedom for multi-sensor fusion, in International Conference on Information Fusion (2019)

  6. Y. He, Q. Song, Y.L. Dong, Adaptive tracking algorithm based on modified strong tracking filter, in International Conference on Radar (2006)

  7. Y.L. Huang, Y.G. Zhang, N. Li, Z. Shi, Design of Gaussian approximate filter and smoother for nonlinear systems with correlated noises at one epoch apart. Circuits Syst. Signal Process. 35(11), 3981–4008 (2016)

    Article  Google Scholar 

  8. Y.L. Huang, Y.G. Zhang, N. Li, A novel robust Student’s t-based Kalman filter. IEEE Trans. Aerosp. Electron. Syst. 53(3), 1545–1554 (2017)

    Article  Google Scholar 

  9. Y.L. Huang, Y.G. Zhang, L. Mihaylova, J. Chambers, Robust Rauch–Tung–Striebel smoothing framework for heavy-tailed and/or skew noises. IEEE Trans. Aerosp. Electron. Syst. 56(1), 415–441 (2020)

    Article  Google Scholar 

  10. Y.L. Huang, Y.G. Zhang, L. Mihaylova, J. Chambers, A novel robust Rauch–Tung–Striebel smoother based on slash and generalized hyperbolic skew Student’s T-distributions, in International Conference on Information Fusion (2018)

  11. Y.L. Huang, Y.G. Zhang, B. Xu, Z.M. Wu, J. Chambers, A new outlier-robust Student’s t based Gaussian approximate filter for cooperative localization. IEEE ASME Trans. Mechatron. 22(5), 2380–2386 (2017)

    Article  Google Scholar 

  12. Y.L. Huang, Y.G. Zhang, Y. Zhao, J. Chambers, A novel robust Gaussian-Student’s t mixture distribution based Kalman filter. IEEE Trans. Signal Process. 67(13), 3606–3620 (2019)

    Article  MathSciNet  Google Scholar 

  13. Y.L. Huang, Y.G. Zhang, J. Chambers, A novel Kullback–Leilber divergence minimization-based adaptive Student’s t-filter. IEEE Trans. Signal Process. 67(20), 5417–5432 (2019)

    Article  MathSciNet  Google Scholar 

  14. Y.L. Huang, Y.G. Zhang, P. Shi, Robust Kalman filters based on gaussian scale mixture distributions with application to target tracking. IEEE Trans. Syst. Man Cybern. Syst. 49(10), 2082–2096 (2019)

    Article  Google Scholar 

  15. K. Jiang, H. Zhang, H.R. Karimi, J. Lin, L. Song, Simultaneous input and state estimation for integrated motor-transmission systems in a controller area network environment via an adaptive unscented Kalman filter. IEEE Trans. Syst. Man Cybern. Syst. 50(4), 1570–1579 (2020)

    Article  Google Scholar 

  16. R.E. Kalman, A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35–45 (1960)

    Article  MathSciNet  Google Scholar 

  17. C.D. Karlgaard, H. Schaub, Huber-based divided difference filtering. J. Guid. Control Dyn. 30(3), 885–891 (2007)

    Article  Google Scholar 

  18. T. Lin, Robust mixture modelling using multivariate skew t distributions. Stat. Comput. 20(3), 343–356 (2009)

    Article  Google Scholar 

  19. H. Nurminen, T. Ardeshiri, R. Piche, Robust inference for state-space models with skewed measurement noise. IEEE Signal Process. Lett. 22(11), 1898–1902 (2015)

    Article  Google Scholar 

  20. M. Roth, E. Özkan, F. Gustafsson, A Student’s t filter for heavy tailed process and measurement noise, in International Conference on Acoustics, Speech and Signal Processing (2013)

  21. S.Y. Wang, C. Yin, S.K. Duan, A modified variational Bayesian noise adaptive Kalman filter. Circuits Syst. Signal Process. 36(10), 4260–4277 (2017)

    Article  Google Scholar 

  22. Y. Wang, X. Wang, Q. Pan, Covariance correction filter with unknown disturbance associated to system state, in American Control Conference (2016)

  23. P.L. Wu, X.X. Li, L.Z. Zhang, Y.M. Bo, Tracking algorithm with radar and IR sensors using a novel adaptive grid interacting multiple model. IET Sci. Meas. Technol. 8(5), 270–276 (2014)

    Article  Google Scholar 

  24. B. Xia, H. Wang, M. Wang, A new method for state of charge estimation of lithium ion battery based on strong tracking cubature Kalman filter. Energies 8(12), 13458–13472 (2015)

    Article  Google Scholar 

  25. P. Yun, P.L. Wu, S. He, Pearson type VII distribution-based robust Kalman filter under outliers interference. IET Radar Sonar Navig. 13(8), 1389–1399 (2019)

    Article  Google Scholar 

  26. P. Yun, P.L. Wu, S. He, An IMM-VB algorithm for hypersonic vehicle tracking with heavy tailed measurement noise, in The 2018 International Conference on Control Automation & Information Sciences (2018)

  27. D.H. Zhou, A suboptimal multiple fading extended Kalman filter. Acta Autom. Sin. 17(6), 689–695 (1991)

    MATH  Google Scholar 

  28. D.H. Zhou, P.M. Frank, Strong tracking filtering of nonlinear time varying stochastic systems with colored noise application to parameter estimation and empirical robustness analysis. Int. J. Control 16(2), 295–307 (1996)

    Article  Google Scholar 

  29. H. Zhu, H. Leung, Z. He, A variational Bayesian approach to robust sensor fusion based on Student-t distribution. Inf. Sci. 221, 201–214 (2013)

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by National Natural Science Foundation of China (61473153), Aeronautical Science Foundation of China (2016ZC59006).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Panlong Wu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (RAR 220 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yun, P., Wu, P., He, S. et al. Robust Kalman Filter with Fading Factor Under State Transition Model Mismatch and Outliers Interference. Circuits Syst Signal Process 40, 2443–2463 (2021). https://doi.org/10.1007/s00034-020-01582-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-020-01582-9

Keywords

Navigation