Skip to main content

Mitigation of the Ground Reflection Effect in Real-Time Locating Systems

  • Conference paper
  • 865 Accesses

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 91))

Abstract

Real-Time Locating Systems (RTLS) are one of the most promising applications based on Wireless Sensor Networks and represent a currently growing market. However, accuracy in indoor RTLS is still a problem requiring novel solutions. One of the main challenges is to deal with the problems that arise from the effects of the propagation of radio frequency waves, such as attenuation, diffraction, reflection and scattering. These effects can lead to other undesired problems, such as multipath and the ground reflection effect. This paper presents an innovative mathematical model for improving the accuracy of RTLS, focusing on the mitigation of the ground reflection effect by using Artificial Neural Networks.

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

Buying options

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Barclay, L.W., I.O.E. Engineers.: Propagation of Radiowaves. Iet (2003)

    Google Scholar 

  2. Kim, E.S., Kim, J.I., Kang, I.-S., Park, C.G., Lee, J.G.: Simulation Results of Ranging Performance in Two-Ray Multipath Model. In: International Conference on Control, Automation and Systems, ICCAS 2008, pp. 734–737 (2008)

    Google Scholar 

  3. Goldsmith, A.: Wireless Communications. Cambridge University Press, Cambridge (2005)

    Google Scholar 

  4. Liu, H., Darabi, H., Banerjee, P., Liu, J.: Survey of Wireless Indoor Positioning Techniques and Systems. IEEE Transactions On Systems, Man, and Cybernetics, Part C: Applications and Reviews 37(6), 1067–1080 (2007)

    Article  Google Scholar 

  5. Kaemarungsi, K., Krishnamurthy, P.: Modeling Of Indoor Positioning Systems Based On Location Fingerprinting. In: Twenty-Third Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2004, vol. 2, pp. 1012–1022 (2004)

    Google Scholar 

  6. Katsuura, H., Sprecher, D.: Computational Aspects of Kolmogorov’s Superposition Theorem. Neural Networks 7(3), 455–461 (1994)

    Article  MATH  Google Scholar 

  7. Lecun, Y., Bottou, L., Orr, G.B., Müller, K.R.: Efficient Backprop. LNCS, pp. 5–50. Springer, Heidelberg (1998)

    Google Scholar 

  8. N-Core, N-Core: A Faster and Easier Way to Create Wireless Sensor Networks (2010), http://Www.N-Core.Info (retrieved October 27, 2010)

  9. Nerguizian, C., Despins, C., Affès, S.: Indoor Geolocation with Received Signal Strength Fingerprinting Technique and Neural Networks. In: de Souza, J.N., Dini, P., Lorenz, P. (eds.) ICT 2004. LNCS, vol. 3124, pp. 866–875. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  10. Nguyen, H., Chan, C.: Multiple Neural Networks for a Long Term Time Series Forecast. Neural Computing & Applications 13(1), 90–98 (2004)

    Article  Google Scholar 

  11. Ray, J.K., Cannon, M.E., Fenton, P.C.: Mitigation Of Static Carrier-Phase Multipath Effects Using Multiple Closely Spaced Antennas. Navigation-Washington 46(3), 193–202 (1999)

    Google Scholar 

  12. Salcic, Z., Chan, E.: Mobile Station Positioning Using GSM Cellular Phone and Artificial Neural Networks. Wireless Personal Communications 14(3), 235–254 (2000)

    Article  Google Scholar 

  13. Schmitz, A., Wenig, M.: The Effect of the Radio Wave Propagation Model in Mobile Ad Hoc Networks. In: Proceedings of the 9th ACM International Symposium on Modeling Analysis and Simulation of Wireless and Mobile Systems, Terromolinos, Spain, pp. 61–67 (2006)

    Google Scholar 

  14. Tapia, D.I., De Paz, J.F., Rodríguez, S., Bajo, J., Corchado, J.M.: Multi-Agent System For Security Control On Industrial Environments. International Transactions on System Science and Applications Journal 4(3), 222–226 (2008)

    Google Scholar 

  15. Vapnik, V.N.: Statistical Learning Theory. Wiley Interscience, Hoboken (1998)

    MATH  Google Scholar 

  16. Xie, J.J., Palmer, R., Wild, D.: Multipath Mitigation Technique in RF Ranging. In: Canadian Conference on Electrical and Computer Engineering, pp. 2139–2142 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Tapia, D.I., De Paz, J.F., Pinzón, C.I., Bajo, J. (2011). Mitigation of the Ground Reflection Effect in Real-Time Locating Systems. In: Abraham, A., Corchado, J.M., González, S.R., De Paz Santana, J.F. (eds) International Symposium on Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 91. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19934-9_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-19934-9_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19933-2

  • Online ISBN: 978-3-642-19934-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics