Skip to main content
SpringerLink
Log in

Multi-sensor Optimal Data Fusion for INS/GNSS/CNS Integration Based on Unscented Kalman Filter

  • Regular Papers
  • Control Theory and Applications
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

This paper presents an unscented Kalman filter (UKF) based multi-sensor optimal data fusion methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integration based on nonlinear system model. This methodology is of two-level structure: at the bottom level, the UKF is served as local filters to integrate GNSS and CNS with INS respectively for generating the local optimal state estimates; and at the top level, a novel optimal data fusion approach is derived based on the principle of linear minimum variance for the fusion of local state estimates to obtain the global optimal state estimation. The proposed methodology refrains from the use of covariance upper bound to eliminate the correlation between local states. Its efficacy is verified through simulations, practical experiments and comparison analysis with the existing methods for INS/GNSS/CNS integration.

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. Y. Meng, S. S. Gao, Y. M. Zhong, G. G. Hu, and A. Subic, “Covariance matching based adaptive unscented Kalman filter for direct filtering in INS/GNSS integration,” Acta Astronautica, vol. 120, pp. 171–181, April 2016. [click]

    Article  Google Scholar 

  2. G. G. Hu, S. S. Gao, and Y. M. Zhong, “A derivative UKF for tightly coupled INS/GPS integrated navigation,” ISA transactions, vol. 56, pp. 135–144, May 2015. [click]

    Article  Google Scholar 

  3. A. Noureldin, T. B. Karamat, M. D. Eberts, and A. El-Shafie, “Performance enhancement of MEMS-based INS/GPS integration for low-cost navigation applications,” IEEE Transactions on Vehicular Technology, vol. 58, no. 3, pp. 1077–1096, March 2009. [click]

    Article  Google Scholar 

  4. W. H. Wei, S. S. Gao, Y. M. Zhong, C. F. Gu, and A. Subic, “Random weighting estimation for systematic error of observation model in dynamic vehicle navigation,” International Journal of Control, Automation and Systems, vol. 14, no. 2, pp. 514–523, April 2016. [click]

    Article  Google Scholar 

  5. Y. Zhao, S. S. Gao, J. Zhang, and Q. N. Sun, “Robust predictive augmented unscented Kalman filter,” International Journal of Control, Automation and Systems, vol. 12, no. 5, pp. 996–1004, October 2014. [click]

    Article  Google Scholar 

  6. S. Yun, Y. J. Lee, and S. Sung, “Range/optical flow-aided integrated navigation system in a strapdown sensor configuration,” International Journal of Control, Automation and Systems, vol. 14, no. 1, pp. 229–241, February 2016. [click]

    Article  Google Scholar 

  7. H. D. Hu, X. L. Huang, M. Li, and Z. Y. Song, “Federated unscented particle filtering algorithm for SINS/CNS/GPS system,” Journal of Central South University of Technology, vol. 17, no. 4, pp. 778–785, August 2010. [click]

    Article  Google Scholar 

  8. X. L. Ning, L. L. Liu, J. C. Fang, and W. R. Wu, “Initial position and attitude determination of lunar rovers by INS/CNS integration,” Aerospace Science and Technology, vol. 30, no. 1, pp. 323–332, October 2013. [click]

    Article  Google Scholar 

  9. Q. Y. Wang, Y. B. Li, M. Diao, W. Gao, and Z. Qi, “Performance enhancement of INS/CNS integration navigation system based on particle swarm optimization back propagation neural network,” Ocean Engineering, vol. 108, pp. 33–45, November 2015. [click]

    Article  Google Scholar 

  10. G. G. Hu, S. S. Gao, Y. M. Zhong, B. B. Gao, and A. Subic, “Modified federated Kalman filter for INS/GNSS/CNS integration,” Proceedings of the Institution of Mechanical Engineers Part G: Journal of Aerospace Engineering, vol. 230, no. 1, pp. 30–44, February 2016.

    Article  Google Scholar 

  11. W. Gao, Y. Zhang, and S. Qian, “An asynchronous fusion algorithm of the SINS/GPS/CNS based on factor graph,” IEEE Chinese Control Conference, pp. 4995–4999, 2013.

    Google Scholar 

  12. A. Jamshaid and J. C. Fang, “SINS/ANS/GPS integration using federated Kalman filter based on optimized information-sharing coefficients,” AIAA Guidance, Navigation, and Control Conference and Exhibit, pp. 1–13, 2005. [click]

    Google Scholar 

  13. W. Quan, B. Q. Liu, X. L. Gong, and J. C. Fang, INS/CNS/GNSS Integrated Navigation Technology, National Defense Industrial Press, Beijing, 2011.

    Google Scholar 

  14. D. J. Jwo and T. P. Weng, “An adaptive sensor fusion method with applications in integrated navigation,” Journal of Navigation, vol. 61, no. 4, pp. 705–721, August 2008. [click]

    Article  Google Scholar 

  15. Y. Gao, E. J. Krakiwsky, M. A. Abousalem, and J. F. Mclellan, “Comparison and analysis of centralized, decentralized, and federated filters,” Navigation, vol. 40, no. 1, pp. 69–86, March 1993. [click]

    Article  Google Scholar 

  16. S. S. Gao, Y. M. Zhong, X. Y. Zhang, and B. Shirinzadeh, “Multi-sensor optimal data fusion for INS/GPS/SAR integrated navigation system,” Aerospace Science and Technology, vol. 13, no. 4, pp. 232–237, June 2009. [click]

    Article  Google Scholar 

  17. H. Z. Qiu, H. Y. Zhang, and H. Jin, “Fusion algorithm of correlated local estimates,” Aerospace Science and Technology, vol. 8, no. 7, pp. 619–626, October 2004.

    Article  MATH  Google Scholar 

  18. S. S. Gao, Y. M. Zhong, and W. Li, “Random weighting method for multisensor data fusion,” IEEE Sensors Journal, vol. 11, no. 9, pp. 1955–1961, September 2011. [click]

    Article  Google Scholar 

  19. J. Zhou, Y. M. Zhu, Z. S. You, and E. B. Song, “An efficient algorithm for optimal linear estimation fusion in distributed multisensor systems,” IEEE Transactions on Systems Man and Cybernetics-Part A Systems and Humans, vol. 36, no. 5, pp. 1000–1009, September 2006. [click]

    Article  Google Scholar 

  20. N. A. Carlson and M. P. Berarducci, “Federated Kalman filter simulation results,” Navigation, vol. 41, no. 3, pp. 297–322, June 1994. [click]

    Article  Google Scholar 

  21. H. D. Hu and X. L. Huang, “SINS/CNS/GPS integrated navigation algorithm based on UKF,” Journal of Systems Engineering and Electronics, vol. 21, no. 1, pp. 102–109, February 2010. [click]

    Article  Google Scholar 

  22. S. L. Sun and Z. L. Deng, “Multi-sensor optimal information fusion Kalman filter,” Automatica, vol. 40, no. 6, pp. 1017–1023, June 2004. [click]

    Article  MathSciNet  MATH  Google Scholar 

  23. G. G. Hu, S. S. Gao, Y. M. Zhong, and B. B. Gao, “Stochastic stability of the derivative unscented Kalman filter,” Chinese Physics B, vol. 24, no. 7, pp. 070202, July 2015.

    Article  Google Scholar 

  24. G. G. Hu, S. S. Gao, Y. M. Zhong, B. B. Gao, and A. Subic, “Modified strong tracking unscented Kalman filter for nonlinear state estimation with process model uncertainty,” International Journal of Adaptive Control and Signal Processing, vol. 29, no. 12, pp. 1561–1577, December 2015. [click]

    Article  MathSciNet  MATH  Google Scholar 

  25. J. Kim, K. Cho, and S. Choi, “Lumped disturbance compensation using extended Kalman filter for permanent magnet linear motor system,” International Journal of Control, Automation and Systems, vol. 14, no. 5, pp. 1244–1253, October 2016. [click]

    Article  Google Scholar 

  26. H. D. Choi, J. M. Pak, M. T. Lim, and M. K. Song, “A Gaussian distributed resampling algorithm for mitigation of sample impoverishment in particle filters,” International Journal of Control, Automation and Systems, vol. 13, no. 4, pp. 1032–1036, August 2015. [click]

    Article  Google Scholar 

  27. B. B. Gao, S. S. Gao, Y. M. Zhong, G. G. Hu, and C. F. Gu, “Interacting multiple model estimation-based adaptive robust unscented Kalman filter,” International Journal of Control, Automation and Systems, vol. 15, no. 5, pp. 2013–2025, October 2017.

    Article  Google Scholar 

  28. K. C. Chang, T. Zhi, and R. K. Saha, “Performance evaluation of track fusion with information matrix filter,” IEEE Transactions on Aerospace and Electronic Systems, vol. 38, no. 2, pp. 455–466, April 2002.

    Article  Google Scholar 

  29. S. S. Gao, Y. M. Zhong, and B. Shirinzadeh, “Random weighting estimation for fusion of multi-dimensional position data,” Information Sciences, vol. 180, no. 24, pp. 4999–5007, December 2010. [click]

    Article  MathSciNet  Google Scholar 

  30. G. G. Hu, S. S. Gao, Y. M. Zhong, B. B. Gao, and A. Subic, “Matrix weighted multi-sensor data fusion for INS/GNSS/CNS integration,” Proceedings of the Institution of Mechanical Engineers Part G: Journal of Aerospace Engineering, vol. 230, no. 6, pp. 1011–1026, December 2016.

    Article  Google Scholar 

  31. Q. T. Wang and X. L. Hu, “Improved Kalman filtering algorithm for passive BD/SINS integrated navigation system based on UKF,” Journal of Systems Engineering and Electronics, vol. 32, no. 2, pp. 376–379, April 2010.

    Google Scholar 

  32. Y. Y. Qin, Inertial Navigation, Science Press, Beijing, 2006.

    Google Scholar 

  33. K. Xiong, L. D. Liu, and H. Y. Zhang, “Modified unscented Kalman filtering and its application in autonomous satellite navigation,” Aerospace Science and Technology, vol. 13, no. 4, pp. 238–246, July 2009. [click]

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Automatics, Northwestern Polytechnical University, Xian, 710072, China

    Bingbing Gao, Gaoge Hu & Shesheng Gao

  2. School of Engineering, RMIT University, Bundoora, VIC, 3083, Australia

    Yongmin Zhong & Chengfan Gu

Authors
  1. Bingbing Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gaoge Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shesheng Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongmin Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chengfan Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bingbing Gao.

Additional information

Recommended by Associate Editor Young Soo Suh under the direction of Editor Duk-Sun Shim. The work of this paper was supported by the National Natural Science Foundation of China (Project Number: 41704016, 61174193) and the Specialized Research Fund for the Doctoral Program of Higher Education (Project Number: 20136102110036).

Bingbing Gao is a Ph.D. student at the School of Automatics, Northwestern Polytechnical University, China. His research interests include information fusion, nonlinear filtering and integrated navigation.

Gaoge Hu received the Ph.D. in control theory and control engineering from North-western Polytechnical University in 2016. His research interests include information fusion, nonlinear filtering and integrated navigation.

Shesheng Gao is a Professor at the School of Automatics, Northwestern Polytechnical University, China. His research interests include control theory and engineering, navigation, guidance and control, and information fusion.

Yongmin Zhong is an Associate Professor in the School of Engineering at RMIT University. His research interests include computational engineering, virtual reality, haptics, soft tissue modelling and surgery simulation, aerospace navigation and control, intelligent systems and robotics.

Chengfan Gu is a lecturer in the School of Engineering at RMIT University, Australia. Prior to this, she was an ARC DECRA Fellow with UNSW Australia. Her research interests include bio/nano materials characterization and analysis, materials processing, computational modelling and optimization analysis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, B., Hu, G., Gao, S. et al. Multi-sensor Optimal Data Fusion for INS/GNSS/CNS Integration Based on Unscented Kalman Filter. Int. J. Control Autom. Syst. 16, 129–140 (2018). https://doi.org/10.1007/s12555-016-0801-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-016-0801-4

Keywords

Search

Navigation