Skip to main content

Mobile Phone Positioning in GSM Networks Based on Information Retrieval Methods and Data Structures

  • Conference paper
Digital Information Processing and Communications (ICDIPC 2011)

Abstract

In this article we present a novel method for mobile phone positioning using a vector space model, suffix trees and an information retrieval approach. The method works with parameters which can be acquired from any common mobile phone without the necessity of installing additional hardware and is handset based. The algorithm is based on a database of previous measurements which are used as an index which looks for the nearest neighbor toward the query measurement. The accuracy of the algorithm is in most cases good enough to accomplish the E9-1-1 requirements on tested data.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ashkenazi, I. B., Moshe, B.: Radio-maps: an experimental study. Report, Ben-Gurion University (2005), http://www.cs.bgu.ac.il/benmoshe/RadioMaps

  2. Assisted GPS: A Low-infrastructure approach. GPS World (March 1, 2002), http://www.gpsworld.com/gps/assisted-gps-a-low-infrastructure-approach-734

  3. Baeza-Yates, R., Ribeiro-Neto, B.: Modern information retrieval. Addison Wesley, Reading (1999)

    Google Scholar 

  4. Drane, C., Macnaughtan, M., Scott, G.: Positioning GSM telephones. IEEE Communication Magazine 36(4), 46–54 (1998)

    Article  Google Scholar 

  5. Ehrenfeucht, A., Haussler, D.: A new distance metric on strings computable in linear time. Discrete Applied Math. 20(3), 191–203 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  6. The FCC. Fact Sheet-FCC Wireless 911 Requirements, FCC (January 2001)

    Google Scholar 

  7. Gusfield, D.: Algorithms on strings, trees and sequences: Computer Science and Computational Biology. Cambridge University Press, Cambridge (1997)

    Book  MATH  Google Scholar 

  8. Kennemann, O.: Pattern recognition by hidden Markov models for supporting handover decisions in the GSM system. In: Proc. 6th Nordic Seminar Dig. Mobile Radio Comm., Stockholm, Sweden, pp. 195–202 (1994)

    Google Scholar 

  9. Kennemann, O.: Continuous location of moving GSM mobile stations by pattern recognition techniques. In: Proc. 5th Int. Symp. Personal, Indoor, Mobile, Radio Comm., Den Haag, Holland, pp. 630–634 (1994)

    Google Scholar 

  10. Kim, S.C., Lee, J.C., Shin, Y.S., Cho, K.-R.: Mobile tracking using fuzzy multi-criteria decision making. In: Jia, X., Wu, J., He, Y. (eds.) MSN 2005. LNCS, vol. 3794, pp. 1051–1058. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  11. Lee, D.L., Chuang, H., Seamons, K.E.: Document ranking and the vector-space model. IEEE Software, 67–75 (1997)

    Google Scholar 

  12. Lee, J.C., Yoo, S.-J., Lee, D.C.: Fuzzy logic adaptive mobile location estimation. In: Jin, H., Gao, G.R., Xu, Z., Chen, H. (eds.) NPC 2004. LNCS, vol. 3222, pp. 626–634. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  13. Manzuri, M.T., Naderi, A.M.: Mobile positioning using enhanced signature database method and error reduction in location grid. In: WRI International Conference on Communications and Mobile Computing, vol. 2, pp. 175–179 (2009)

    Google Scholar 

  14. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to information retrieval. Cambridge University Press, Cambridge (2008)

    Book  MATH  Google Scholar 

  15. Martinovič, J., Novosád, T., Snášel, V.: Vector model improvement using suffix trees. In: IEEE ICDIM, pp. 180–187 (2007)

    Google Scholar 

  16. McCreight, E.: A space-economical suffix tree construction algorithm. Journal of the ACM 23, 262–272 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  17. van Rijsbergen, C.J.: Information retrieval, 2nd edn. Butterworths, London (1979)

    MATH  Google Scholar 

  18. Rodeh, M., Pratt, V.R., Even, S.: Linear algorithm for data compression via string matching. Journal of the ACM 28(1), 16–24 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  19. Salcic, Z., Chan, E.: Mobile station positioning using GSM cellular phone and artificial neural networks. Wireless Personal Communications 14, 235–254 (2000)

    Article  Google Scholar 

  20. Salcic, Z.: GSM mobile station location using reference stations and artificial neural networks. Wireless Personal Communications 19, 205–226 (2001)

    Google Scholar 

  21. Simic, M.I., Pejovic, P.V.: An algorithm for determining mobile station location based on space segmentation. IEEE Communications Letters 12(7) (2008)

    Google Scholar 

  22. Simic, M.I., Pejovic, P.V.: A probabilistic approach to determine mobile station location with application in cellular networks. Annals of Telecommunications 64(9-10), 639–649 (2009)

    Article  Google Scholar 

  23. Simic, M.I., Pejovic, P.V.: A comparison of three methods to determine mobile station location in cellular communication systems. European Transactions on Telecommunications 20(8), 711–721 (2009)

    Article  Google Scholar 

  24. Song, H.L.: Automatic vehicle location in cellular communication systems. IEEE Transactions on Vehicular Technology 43, 902–908 (1994)

    Article  Google Scholar 

  25. Sun, G., Chen, J., Guo, W., Liu, K.J.R.: Signal processing techniques in network aided positioning a survey of state of the art positioning designs. IEEE Signal Processing Mag. 22(4), 12–23 (2005)

    Google Scholar 

  26. Ukkonen, E.: On-line construction of suffix trees. Algorithmica 14, 249–260 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  27. Wang, S., Min, J., Yi, B.K.: Location based services for mobiles: Technologies and standards. In: IEEE International Conference on Communication (ICC), Beijing, China (2008)

    Google Scholar 

  28. Weiner, P.: Linear pattern matching algorithms. In: The 14th Annual Symposium on Foundations of Computer Science, pp. 1–11 (1973)

    Google Scholar 

  29. Zamir, O., Etzioni, O.: Web document clustering: A feasibility demonstration. In: SIGIR 1998, pp. 46–54 (1998)

    Google Scholar 

  30. Zamir, O.: Clustering web documents: A phrase-based method for grouping search engine results. In: Doctoral dissertation, University of Washington (1999)

    Google Scholar 

  31. Zhao, Y.: Standardization of mobile phone positioning for 3G systems. IEEE Communications Mag. 40(7), 108–116 (2002)

    Article  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

Novosád, T., Martinovič, J., Scherer, P., Snášel, V., Šebesta, R., Klement, P. (2011). Mobile Phone Positioning in GSM Networks Based on Information Retrieval Methods and Data Structures. In: Snasel, V., Platos, J., El-Qawasmeh, E. (eds) Digital Information Processing and Communications. ICDIPC 2011. Communications in Computer and Information Science, vol 189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22410-2_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-22410-2_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22409-6

  • Online ISBN: 978-3-642-22410-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics