Table of Contents
Journal of Computational Engineering
Volume 2014 (2014), Article ID 175820, 6 pages
http://dx.doi.org/10.1155/2014/175820
Research Article

An Improved Unscented Particle Filter with Global Sampling Strategy

Hefei Electronic and Engineering Institute, Hefei 230000, China

Received 13 July 2014; Accepted 22 November 2014; Published 10 December 2014

Academic Editor: Hongli Dong

Copyright © 2014 Yi-zheng Zhao. 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. H. Dong, Z. Wang, and H. Gao, “Distributed H filtering for a class of markovian jump nonlinear time-delay systems over lossy sensor networks,” IEEE Transactions on Industrial Electronics, vol. 60, no. 10, pp. 4665–4672, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. H. Dong, Z. Wang, and H. Gao, “Distributed filtering for a class of time-varying systems over sensor networks with quantization errors and successive packet dropouts,” IEEE Transactions on Signal Processing, vol. 60, no. 6, pp. 3164–3173, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  3. Z. Wang, H. Dong, B. Shen, and H. Gao, “Finite-horizon H filtering with missing measurements and quantization effects,” IEEE Transactions on Automatic Control, vol. 58, no. 7, pp. 1707–1718, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  4. Y. Yoshida, K. Hayashi, H. Sakai, and W. Bocquet, “Marginalized particle filter for blind signal detection with analog imperfections,” IEICE Transactions on Communications, vol. 93, no. 2, pp. 336–344, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. F. Wang, Q. Guo, and Y. Lin, “A Kalman particle filter for bearing-only target tracking,” Journal of Computational Information Systems, vol. 7, no. 15, pp. 5628–5635, 2011. View at Google Scholar · View at Scopus
  6. D. Creal, “A survey of sequential Monte Carlo methods for economics and finance,” Econometric Reviews, vol. 31, no. 3, pp. 245–296, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. O. Cappe, S. J. Godsill, and E. Moulines, “An overview of existing methods and recent advances in sequential Monte Carlo,” Proceedings of the IEEE, vol. 95, no. 5, pp. 899–924, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. R. van der Merwe, A. Doucet, N. de Freitas, and E. Wan, “The unscented particle filter,” Tech. Rep. CUED/F-INFENG/TR 380, Department of Engineering, Cambridge University, 2000. View at Google Scholar
  9. S. J. Julier and J. K. Uhlmann, “Unscented filtering and nonlinear estimation,” Proceedings of the IEEE, vol. 92, no. 3, pp. 401–422, 2004. View at Publisher · View at Google Scholar · View at Scopus
  10. W. Fasheng and L. Yuejin, “Improving particle filter with a new sampling strategy,” in Proceedings of the 4th International Conference on Computer Science and Education, pp. 408–412, July 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. F. Wang, Y. Lin, T. Zhang, and J. Liu, “Particle filter with hybrid proposal distribution for nonlinear state estimation,” Journal of Computers, vol. 6, no. 11, pp. 2491–2501, 2011. View at Publisher · View at Google Scholar · View at Scopus