Table of Contents Author Guidelines Submit a Manuscript
International Journal of Aerospace Engineering
Volume 2015, Article ID 381478, 9 pages
http://dx.doi.org/10.1155/2015/381478
Research Article

A New Adaptive Square-Root Unscented Kalman Filter for Nonlinear Systems with Additive Noise

1School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China
2School of Electrical and Control Engineering, Xi’an University of Science & Technology, Xi’an 710054, China
3713th Institute of China Shipbuilding Industry Corporation, Zhengzhou 450002, China

Received 27 February 2015; Accepted 18 May 2015

Academic Editor: Paul Williams

Copyright © 2015 Yong Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. E. Kalman, “A new approach to linear filtering and prediction problems,” Journal of Basic Engineering, vol. 82, no. 1, pp. 35–45, 1960. View at Publisher · View at Google Scholar
  2. Y. Sunahara, “An approximate method of state estimation for nonlinear dynamical systems,” in Proceedings of the Joint Automatic Control Conference, University of Colorado, Boulder, Colo, USA, 1969.
  3. R. S. Bucy and K. D. Senne, “Digital synthesis of non-linear filters,” Automatica, vol. 7, no. 3, pp. 287–298, 1971. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  4. S. J. Julier, J. K. Uhlmann, and H. F. Durrant-Whyte, “New approach for filtering nonlinear systems,” in Proceedings of the American Control Conference, pp. 1628–1632, Evanston, Ill, USA, June 1995. View at Scopus
  5. S. J. Julier, J. K. Uhlmann, and H. F. Durrant-Whyte, “A new method for the nonlinear transformation of means and covariances in filters and estimators,” IEEE Transactions on Automatic Control, vol. 45, no. 3, pp. 477–482, 2000. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  6. S. J. Julier and H. Durrant-Whyte, “Navigation and parameter estimation of high speed road vehicles,” in Proceedings of the Robotics and Automation Conference, Nogoya, Japan, 1995.
  7. R. van der Merwe and E. A. Wan, “Efficient derivative-free Kalman filters for online learning,” in Proceedings of European Symposium on Artificial Neural Networks (ESANN '01), Bruges, Belgium, 2001.
  8. P. H. Li and T. W. Zhang, “Unscented Kalman filter for visual curve tracking,” in Proceedings of the Workshop Statistical Methods Video Processing, pp. 13–18, Copenhagen, Denmark, June 2002.
  9. P. N. Dwivedi, P. G. Bhale, A. Bhattacharya, and R. Padhi, “State estimation using UKF and predictive guidance for engaging barrel roll aircrafts,” in Proceedings of the 1st American Control Conference (ACC' 13), pp. 6175–6180, June 2013. View at Scopus
  10. M. Rhudy, Y. Gu, J. Gross, and M. R. Napolitano, “Evaluation of matrix square root operations for UKF within a UAV GPS/INS sensor fusion application,” International Journal of Navigation and Observation, vol. 2011, Article ID 416828, 11 pages, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Qi, J. Ha, and Z. Wu, “Rotorcraft UAV actuator failure estimation with KF-based adaptive UKF algorithm,” in Proceedings of the American Control Conference (ACC '08), Seattle, Wash, USA, June 2008.
  12. H. E. Soken and S. Sakai, “Adaptive tuning of the unscented Kalman filter for satellite attitude estimation,” Journal of Aerospace Engineering, vol. 28, no. 3, 2015. View at Publisher · View at Google Scholar
  13. R. van der Merwe and E. A. Wan, “The square-root unscented Kalman filter for state and parameter estimation,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, Salt Lake City, Utah, USA, 2001.
  14. W. Ding, J. Wang, C. Rizos, and D. Kinlyside, “Improving adaptive kalman estimation in GPS/INS integration,” The Journal of Navigation, vol. 60, no. 3, pp. 517–529, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. D.-J. Jwo and S.-H. Wang, “Adaptive fuzzy strong tracking extended Kalman filtering for GPS navigation,” IEEE Sensors Journal, vol. 7, no. 5, pp. 778–789, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. R. K. Mehra, “On the identification of variances and adaptive Kalman filtering,” IEEE Transactions on Automatic Control, vol. 15, pp. 175–184, 1970. View at Google Scholar · View at MathSciNet
  17. R. K. Mehra, “Approaches to adaptive filtering,” IEEE Transactions on Automatic Control, vol. AC-17, no. 5, pp. 693–698, 1972. View at Google Scholar · View at MathSciNet
  18. P. S. Maybeck, Stochastic Models, Estimation and Control (Volumes 2), Academic Press, New York, NY, USA, 1982.
  19. D. J. Lee and K. T. Alfriend, “Adaptive sigma point filtering for state and parameter estimation,” in Proceedings of the AIAA/AAS Astrodynamics Specialist Conference and Exhibit, Providence, RI, USA, 2001.
  20. F. D. Busse, Precise formation-state estimation in low earth orbit using carrier differential GPS [Ph.D. dissertation], Stanford University, 2003.
  21. Y. Shi, C. Han, and Y. Liang, “Adaptive UKF for target tracking with unknown process noise statistics,” in Proceedings of the 12th International Conference on Information Fusion (FUSION' 09), pp. 1815–1820, Seattle, Wash, USA, July 2009. View at Scopus
  22. Y. Zhou, Y. F. Zhang, and J. Z. Zhang, “A new adaptive square-root unscented kalman filter for nonlinear systems,” Applied Mechanics and Materials, vol. 300-301, pp. 623–626, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. A. U. Mageswari, J. J. Ignatious, and R. Vinodha, “A comparitive study of Kalman filter, extended kalman filter and unscented Kalman filter for harmonic analysis of the non-stationary signals,” International Journal of Scientific & Engineering Research, vol. 3, no. 7, pp. 1–9, 2012. View at Google Scholar
  24. A. H. Mohamed and K. P. Schwarz, “Adaptive Kalman filtering for INS/GPS,” Journal of Geodesy, vol. 73, no. 4, pp. 193–203, 1999. View at Publisher · View at Google Scholar · View at Scopus
  25. A. P. Sage and G. W. Husa, “Adaptive filtering with unknown prior statistics,” in Proceedings of the Joint Automatic Control Conference, pp. 760–769, Boulder, Colo, USA, 1969.
  26. Q. Xia, M. Rao, Y. Ying, S. Shen, and Y. Sun, “A new state estimation algorithm-adaptive fading Kalman filter,” in Proceedings of the 31st IEEE Conference on Decision and Control, pp. 1216–1221, IEEE, Tucson, Ariz, USA, December 1992. View at Publisher · View at Google Scholar
  27. Q. Xia, M. Rao, Y. Ying, and X. Shen, “Adaptive fading Kalman filter with an application,” Automatica, vol. 30, no. 8, pp. 1333–1338, 1994. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  28. Z. Deng and Y. Guo, “Dynamic prediction of the oil and water outputs in oil field,” Acta Automatica Sinica, vol. 9, pp. 121–126, 1983. View at Google Scholar
  29. J. H. Kotecha and P. M. Djuric, “Gaussian particle filtering,” IEEE Transactions on Signal Processing, vol. 51, no. 10, pp. 2592–2601, 2003. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus