Skip to main content

Particle Filter Application to Localization

  • Conference paper
Harbour Protection Through Data Fusion Technologies

Abstract

A simple particle filter algorithm with adaptive resampling is implemented showing that land-based range-only radio beacons can provide adequate localization within a port facility. Each beacon transmits its identity along with the range, thus solving the data association problem on the hardware level. The operation of the algorithm is assessed using a single set of experimental data with a 1,000 particle implementation. The associated computational load can be handled on-line by a standard PC. In the same environment the GPS absolute localization is likely to fail.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Arulampalam S, Maskell E, Gordon N, Clapp T. 2002. A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing, 50(2): 174–188.

    Article  Google Scholar 

  2. Durrant-Whyte HF. 1996. An autonomous guided vehicle for cargo handling applications. The International Journal of Robotics Research, 15(5):407–440.

    Article  Google Scholar 

  3. Doucet A, de Freitas N, Gordon N, editors. 2001. Sequential Monte Carlo Methods in Practice. Statistics for engineering and information science. Springer, New York.

    MATH  Google Scholar 

  4. Fox D. 2003. Adapting the Sample Size in Particle Filters Through KLD-Sampling. International Journal of Robotics Research, 22(12):985–1004.

    Article  Google Scholar 

  5. Gustafsson F, Gunnarsson F, Bergman N, Forssell U, Jansson J, Karlsson R, Nordlund PJ. 2002. Particle filters for positioning, navigation, and tracking. IEEE Transactions on Signal Processing: Special Issue on Monte Carlo Methods for Statistical Signal Processing, 50(2):425–437.

    Google Scholar 

  6. Kiriy E. 2002. A localization system for autonomous golf course mowers. Master’s thesis, McGill University, Montreal, Quebec, Canada.

    Google Scholar 

  7. Kotecha JH, Djurić PM. 2003. Gaussian Particle Filtering. IEEE Transactions on Signal Processing, 51(10):2592–2601.

    Article  MathSciNet  Google Scholar 

  8. Kurth D, Kantor GA, Singh S. 2003. Experimental results in range-only localization with radio. In 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS ’03), volume 1, pages 974–979.

    Google Scholar 

  9. Liu JS, Chen R, Logvinenko T. 2001. A theoretical framework for sequential importance sampling and resampling. In A. Doucet, N. de Freitas, and N.J. Gordon, editors, Sequential Monte Carlo in Practice. Springer, New York.

    Google Scholar 

  10. Lee DS, Chia NKK. 2002. A particle algorithm for sequential Bayesian parameter estimation and model selection. IEEE Transactions on Signal Processing, 50(2): 326–336.

    Article  Google Scholar 

  11. Pitt MK, Shephard N. 1999. Filtering via simulation: auxiliary particle filters. Journal of the American Statistical Association, 94(446): 590–599.

    Article  MATH  MathSciNet  Google Scholar 

  12. Maskell S, Briers M, Wright R. 2004. Distribution in a particle filter. IEEE Target Tracking 2004: Algorithms and Applications, pages 23–31, March, 23–24 2004.

    Google Scholar 

  13. Rekleitis I. 2002. Cooperative Localization and Multi-Robot Exploration. PhD thesis, McGill University, Montreal, Canada.

    Google Scholar 

  14. Ristic B, Arulampalam S, Gordon N. 2004. Beyond the Kaiman Filter. Artech House, Norwood, MA.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science + Business Media B.V.

About this paper

Cite this paper

Kiriy, E., Michalska, H., Michaud, G. (2009). Particle Filter Application to Localization. In: Shahbazian, E., Rogova, G., DeWeert, M.J. (eds) Harbour Protection Through Data Fusion Technologies. NATO Science for Peace and Security Series C: Environmental Security. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8883-4_37

Download citation

  • DOI: https://doi.org/10.1007/978-1-4020-8883-4_37

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-1-4020-8882-7

  • Online ISBN: 978-1-4020-8883-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics