Advertisement

A New Resampling Parameter Algorithm for Kullback-Leibler Distance with Adjusted Variance and Gradient Data Based on Particle Filter

  • Nga Ly-TuEmail author
  • Thuong Le-Tien
  • Linh Mai
Conference paper
  • 568 Downloads
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 221)

Abstract

In this paper, we propose a new resampling method of particle filter (PF) to monitor target position. The target location is to improve enhancing the effect of the received signal strength (RSS) variations. The key issue of our technique is to determine a new resampling parameter that finding the optimal bound error and lower bound variance values for Kullback-Leibler distance (KLD)-resampling adjusted variance and gradient data based on PF to ameliorate the effect of the RSS variations by generating a sample set near the high-likelihood region. To find these values, these optimal algorithms are proposed based on the maximum mean number of particles used of our proposal and other KLD-resampling methods. Our experiments show that the new technique does not only enhance the estimation accuracy but also improves the efficient number of particles compared to the traditional methods.

Keywords

SIR Bound error KLD-resampling RSS 

Notes

Acknowledgments

This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number T2016-02-IT.

References

  1. 1.
    Seppänen, A., et al.: State estimation in process tomography—three-dimensional impedance imaging of moving fluids. Int. J. Numer. Meth. Eng. 73(11), 1651–1670 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Schön, T.B.: Solving Nonlinear State Estimation Problems Using Particle Filters-An Engineering Perspective. Department of Automatic Control, Linköping University, Linköping (2010)Google Scholar
  3. 3.
    Li, T., Bolic, M., Djuric, P.: Resampling methods for particle filtering. IEEE Sig. Process. Mag. 32(3), 70–86 (2015)CrossRefGoogle Scholar
  4. 4.
    Li, T., Sattar, T.P., Sun, S.: Deterministic resampling: unbiased sampling to avoid sample impoverishment in particle filters. Sig. Process. 92(7), 1637–1645 (2012)CrossRefGoogle Scholar
  5. 5.
    Arulampalam, M.S., et al.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Sig. Process. 50(2), 174–188 (2002)CrossRefGoogle Scholar
  6. 6.
    Gordon, N.J., Salmond, D.J., Smith, A.F.: Novel approach to nonlinear/non-Gaussian Bayesian state estimation. In: IEE Proceedings F-Radar and Signal Processing. IET (1993)Google Scholar
  7. 7.
    Li, T., Sun, S., Sattar, T.P.: Adapting sample size in particle filters through KLD-resampling. Electron. Lett. 49(12), 740–742 (2013)CrossRefGoogle Scholar
  8. 8.
    Fox, D.: Adapting the sample size in particle filters through KLD-sampling. Int. J. Robot. Res. 22(12), 985–1003 (2003)CrossRefGoogle Scholar
  9. 9.
    Ly-Tu, N., et al.: Performance of sampling/resampling-based particle filters applied to non-linear problems. REV J. Electron. Commun. 4(3–4) (2014)Google Scholar
  10. 10.
    Park, S.-H., et al.: Improved adaptive particle filter using adjusted variance and gradient data. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2008. IEEE (2008)Google Scholar
  11. 11.
    Redondi, A., et al.: An integrated system based on wireless sensor networks for patient monitoring, localization and tracking. Ad Hoc Netw. 11(1), 39–53 (2013)CrossRefGoogle Scholar
  12. 12.
    Ly-Tu, N., Le-Tien, T., Mai, L.: A modified particle filter through Kullback-Leibler distance based on received signal strength. In: 2016 3rd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS). IEEE (2016)Google Scholar
  13. 13.
    Wang, Z., Zhao, X., Qian, X.: Unscented particle filter with systematic resampling localization algorithm based on RSS for mobile wireless sensor networks. In: 2012 Eighth International Conference on Mobile Ad-hoc and Sensor Networks (MSN). IEEE (2012)Google Scholar
  14. 14.
    Ly-Tu, N., Mai, L., Le-Tien, T.: KLD-resampling with adjusted variance and gradient data-based particle filter applied to wireless sensor networks. In: 2015 2nd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS). IEEE (2015)Google Scholar
  15. 15.
    Ly-Tu, N., Le-Tien, T., Mai, L.: A study on particle filter based on KLD-resampling for wireless patient tracking. Int. J. Industr. Eng. Manage. Syst. 92–102 (2017). ISSN 1598-7248 (Print). ISSN 2234-6473 (Online). Publisher: The Korean Institute of Industrial EngineersGoogle Scholar
  16. 16.
    Wang, Z., Zhao, X., Qian, X.: The analysis of localization algorithm of unscented particle filter based on RSS for linear wireless sensor networks. In: 2013 32nd Chinese Control Conference (CCC). IEEE (2013)Google Scholar
  17. 17.
    Ly-Tu, N., Le-Tien, T., Vo-Thi-Luu, P., Mai, L.: Particle filter through Kullback-Leibler distance resampling with adjusted variance and gradient data for wireless biomedical sensor networks. In: Proceedings on International Conference on Ubiquitous Information Management and Communication (IMCOM 2015), Bali, Indonesia, 8–10 January 2015. ISBN 978-1-4503-3377-1. http://dx.doi.org/10.1145/2701126.2701221

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringInternational University-VNUHCMHo Chi Minh CityVietnam
  2. 2.Department of Electrical and Electronics EngineeringUniversity of Technology, VNUHanoiVietnam
  3. 3.VNUHCMHo Chi Minh CityVietnam
  4. 4.School of Electrical and EngineeringInternational University-VNUHCMHo Chi Minh CityVietnam

Personalised recommendations