Skip to main content
SpringerLink
Log in

Robust range-parameterized cubature Kalman filter for bearings-only tracking

  • Mechanical Engineering, Control Science and Information Engineering
  • Published:
Journal of Central South University Aims and scope Submit manuscript

Abstract

In order to improve tracking accuracy when initial estimate is inaccurate or outliers exist, a bearings-only tracking approach called the robust range-parameterized cubature Kalman filter (RRPCKF) was proposed. Firstly, the robust extremal rule based on the pollution distribution was introduced to the cubature Kalman filter (CKF) framework. The improved Turkey weight function was subsequently constructed to identify the outliers whose weights were reduced by establishing equivalent innovation covariance matrix in the CKF. Furthermore, the improved range-parameterize (RP) strategy which divides the filter into some weighted robust CKFs each with a different initial estimate was utilized to solve the fuzzy initial estimation problem efficiently. Simulations show that the result of the RRPCKF is more accurate and more robust whether outliers exist or not, whereas that of the conventional algorithms becomes distorted seriously when outliers appear.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. WANG S Y, FENG J C, TSE C K. Analysis of the characteristic of the Kalman gain for 1-D chaotic maps in cubature Kalman filter [J]. IEEE Signal Processing Letters, 2013, 20(3): 229–232.

    Article  Google Scholar 

  2. SON H S, PARK J B, JOO Y H. Fuzzy c-means clustering-based smart tracking model for three-dimensional manoeuvring target including unknown acceleration input [J]. IET Radar, Sonar and Navigation, 2013, 7(6): 623–634.

    Article  Google Scholar 

  3. ITO K, XIONG K Q. Gaussian filters for nonlinear filtering problems [J]. IEEE Transactions on Automatic Control, 2000, 45(5): 910–927.

    Article  MathSciNet  MATH  Google Scholar 

  4. JAUFFRET C, PILLON D, PIGNOL A C. Bearings-only maneuvering target motion analysis from a nonmaneuvering platform [J]. IEEE Transactions on Aerospace Electronic Systems, 2010, 46(4): 1934–1949.

    Article  Google Scholar 

  5. YANG T, MEHTA P G, MEYN S P. Feedback particle filter [J]. IEEE Transactions on Automatic Control, 2013, 58(10): 2465–2480.

    Article  MathSciNet  Google Scholar 

  6. DOUCET A, GADSILL S, ANDRIEU C. On sequential Monte Carlo sampling methods for Bayesian filtering [J]. Statistics and Computing, 2000, 50(2): 736–746.

    Google Scholar 

  7. ZUO J Y. Dynamic resampling for alleviating sample impoverishment of particle filter [J]. IET Radar, Sonar & Navigation, 2013, 7(9): 968–977.

    Article  Google Scholar 

  8. MACAGNANO D, de ABREU G T F. Adaptive gating for multitarget tracking with Gaussian mixture filters [J]. IEEE Transactions on Signal Processing, 2012, 60(3): 1533–1538.

    Article  MathSciNet  Google Scholar 

  9. BILIK I, TABRIKIAN J. Maneuvering target tracking in the presence of glint using the nonlinear Gaussian mixture Kalman filter [J]. IEEE Transactions on Aerospace Electronic Systems, 2010, 46(1): 240–262.

    Article  Google Scholar 

  10. CLARK J M C, KOUNTOURIOTIS P A, VINTER R B. A Gaussian mixture filter for range-only tracking [J]. IEEE Transactions on Automatic Control, 2011, 56(3): 602–613.

    Article  MathSciNet  Google Scholar 

  11. AHMED N U, RADAIDEH S M. Modified extended Kalman filtering [J]. IEEE Transactions on Automatic Control, 1994, 36(6): 1322–1326.

    Article  MathSciNet  MATH  Google Scholar 

  12. LIU C, SHUI P, LI S. Unscented extended Kalman filter for target tracking [J]. Journal of Systems Engineering and Electronics, 2011, 22(2): 188–192.

    Article  Google Scholar 

  13. JULIER S J, UHLMANN J K. A new method for the nonlinear transformation of means and covariances in filters andestimators [J]. IEEE Transactions on Automatic Control, 2000, 45(3): 477–482.

    Article  MathSciNet  MATH  Google Scholar 

  14. JULIER S J, UHLMANN J K. Unscented filtering and nonlinear estimation [J]. Proceedings of the IEEE, 2004, 93(3): 401–422.

    Article  Google Scholar 

  15. CHANG G B. Marginal unscented kalman filter for cross-correlated process and observation noise at the same epoch [J]. IET Radar, Sonar & Navigation, 2014, 8(1): 54–64.

    Article  Google Scholar 

  16. LIU Kai-Zhou, LI Jing, GUO Wei, ZHU Pu-qiang, WANG Xiao-hui. Navigation system of a class of underwater vehicle based on adaptive unscented Kalman filter algorithm [J]. Journal of Central South University, 2014, 21(2): 550–557.

    Article  Google Scholar 

  17. ARASARATNAM I, HAYKIN S. Cubature Kalman filters [J]. IEEE Transactions on Automatic Control, 2009, 54(06): 1254–1269.

    Article  MathSciNet  MATH  Google Scholar 

  18. JIA B, XIN M, CHENG Y. High-degree cubature Kalman filter [J]. Automatica, 2013, 49(2): 510–518.

    Article  MathSciNet  MATH  Google Scholar 

  19. ARASARATNAM I, HAYKIN S. Cubature Kalman smoothers [J]. Automatica, 2011, 47(10): 2245–2250.

    Article  MathSciNet  MATH  Google Scholar 

  20. PESONEN H, PICHE’ R. Cubature-based Kalman filters for positionin [M]. IEEE 2010 7th Workshop on Positioning, Navigation and Communication. 2010: 45–49.

    Chapter  Google Scholar 

  21. YANG Y X. Adaptive navigation and kinematic positioning [M]. Beijing, China: Surveying and Mapping Press, 2006. (in Chinese)

    Google Scholar 

  22. LEONG P H, ARULAMPALAM S, LANAHEWA T A, ABHAYAPALA T D. A Gaussian-sum based cubature Kalman filter for bearings-only tracking [J]. IEEE Transactions on Aerospace Electronic Systems, 2013, 49(2): 1161–1176.

    Article  Google Scholar 

  23. TUKEY J W. A survey of sampling from contaminated distributions in contributions to probability and statistics [M]. Stanford Calif: Stanford University Press, 1960.

    Google Scholar 

  24. LI W, LIU M H, DUAN D P. Improved robust Huber-based divided difference filtering [J]. Proceedings of the Institution of Mechanical Engineers. Part G, Journal of Aerospace Engineering, 2014, 228(11): 2123–2129.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Information and Navigation College, Air Force Engineering University, Xi’an, 710077, China

    Hao Wu  (吴昊), Shu-xin Chen  (陈树新), Bin-feng Yang  (杨宾峰) & Xi Luo  (罗玺)

Authors
  1. Hao Wu  (吴昊)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shu-xin Chen  (陈树新)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bin-feng Yang  (杨宾峰)
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xi Luo  (罗玺)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hao Wu  (吴昊).

Additional information

Foundation item: Projects(51377172, 51577191) supported by the National Natural Science Foundation of China

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, H., Chen, Sx., Yang, Bf. et al. Robust range-parameterized cubature Kalman filter for bearings-only tracking. J. Cent. South Univ. 23, 1399–1405 (2016). https://doi.org/10.1007/s11771-016-3192-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11771-016-3192-z

Keywords

Search

Navigation