Skip to main content

Mapping the Radio World to Find Us

  • Chapter
  • First Online:
Multi-Technology Positioning

Abstract

Locating users in an indoor scenario is a challenging task. While there are several systems capable of tackling it, most of them are impractical to deploy in a worldwide scale due to the costs associated with its infrastructure. Therefore, this chapter guides the reader through a popular indoor positioning technique, fingerprinting, which relies on existing infrastructure to provide an estimation of the user’s location. While any kind of signal can be used, such as acoustic and electromagnetic signals, the focus is put on wireless local area network signals, which are ubiquitous in most current buildings. Along the way, the chapter introduces path-loss models, advantages, disadvantages and a thorough description of the inner workings of this technique.

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 EPUB and 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
Hardcover Book
USD 159.99
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

References

  1. N. Alsindi et al., An empirical evaluation of a probabilistic RF signature for WLAN location fingerprinting. IEEE Trans. Wirel. Commun. 13 (6), 3257–3268 (2014). ISSN:15361276. doi:10.1109/TWC.2014.041714.131113

    Google Scholar 

  2. P. Bahl, V.N. Padmanabhan, RADAR: an in-building RF-based user location and tracking system, in Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064) (2000). https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/infocom2000.pdf

  3. A.D. Cheok, L. Yue, A novel light-sensor-based information transmission system for indoor positioning and navigation. IEEE Trans. Instrum. Meas. 60 (1), 290–299 (2011). ISSN:0018-9456. doi:10.1109/TIM.2010.2047304

    Google Scholar 

  4. D. Cichon, T. Kurner, Propagation prediction models. COST 231 Final Report, pp. 116–208 (1995)

    Google Scholar 

  5. A. Coluccia, F. Ricciato, G. Ricci, Positioning based on signals of opportunity. IEEE Commun. Lett. 18 (2), 356–359 (2014). doi:10.1109/LCOMM.2013.123013.132297

    Article  Google Scholar 

  6. A. Dammann, S. Sand, R. Raulefs, Signals of opportunity in mobile radio positioning, in Signal Processing Conference, Eusipco (IEEE, Bucharest, 2012), pp. 549–553

    Google Scholar 

  7. B. Dawes, K.-W. Chin, A comparison of deterministic and probabilistic methods for indoor localization. J. Syst. Softw. 84 (3), 442–451 (2011). ISSN:01641212. doi:10.1016/j.jss.2010.11.888

    Google Scholar 

  8. Z. Deng, Y. Yu, X. Yuan, Situation and development tendency of indoor positioning. China Commun. 10, 42–55 (2013). doi:10.1109/CC.2013.6488829

    Article  Google Scholar 

  9. R. Faragher, R. Harle, Location fingerprinting with bluetooth low energy beacons. IEEE J. Sel. Areas Commun. 33, 2418–2428 (2015). ISSN:0733-8716. doi:10.1109/JSAC.2015.2430281

    Google Scholar 

  10. Federal Communications Commission. Fourth Report and Order (2015)

    Google Scholar 

  11. J.-A. Francisco, R.P. Martin, A method of characterizing radio signal space for wireless device localization. Tsinghua Sci. Technol. 20 (4), 385–408 (2015). ISSN:1007-0214. doi:10.1109/TST.2015.7173454

    Google Scholar 

  12. C. Gentile et al., Geolocation techniques, principles and applications (2013), pp. 59–97. ISBN:9781461418351. doi:10.1007/978-1-4614-1836-8

    Google Scholar 

  13. D. Gingras, An overview of positioning and data fusion techniques applied to land vehicle navigation systems (2009). http://www.gel.usherbrooke.ca/LIV/index_htm_files/denis\%20gingras\%20chapter.pdf. http://www.igi-global.com/chapter/overview-positioning-data-fusion-techniques/5489

  14. P.C. Gomez, Bayesian signal processing techniques for GNSS receivers: from multipath mitigation to positioning. Ph.D. Universitat Politcnica de Catalunya, 2009

    Google Scholar 

  15. F. Gustafsson, F. Gunnarsson, Mobile positioning using wireless networks: possibilities and fundamental limitations based on available wireless network measurements. IEEE Signal Process. Mag. 22 (4), 41–53 (2005). ISSN:1053-5888. doi:10.1109/MSP.2005.1458284

    Google Scholar 

  16. S. Han et al., Cosine similarity based fingerprinting algorithm in WLAN indoor positioning against device diversity, in 2015 IEEE International Conference Communications (ICC), 61401119 (2015), pp. 2710–2714

    Google Scholar 

  17. H. Hashemi, The indoor radio propagation channel. Proc. IEEE 81 (7), 943–968 (1993). ISSN:00189219. doi:10.1109/5.231342

    Google Scholar 

  18. V. Honkavirta et al., A comparative survey of WLAN location fingerprinting methods, in 2009 6th Workshop Positioning, Navigation Communication (IEEE, New York, 2009), pp. 243–251. ISBN:978-1-4244-3292-9. doi:10.1109/WPNC.2009.4907834

    Book  Google Scholar 

  19. ITU-R, Recommendation ITU-R P.1238-7 (2012)

    Google Scholar 

  20. C. Laoudias, C.G. Panayiotou, P. Kemppi, On the RBF-based positioning using WLAN signal strength fingerprints, in Small (2010), pp. 93–98

    Google Scholar 

  21. H. Liu et al., Survey of wireless indoor positioning techniques and systems. IEEE Trans. Syst. Man Cybern. C (Appl. Rev.) 37 (6), 1067–1080 (2007). ISSN:1094-6977. doi:10.1109/TSMCC.2007.905750

    Google Scholar 

  22. L. Mailaender, On the CRLB scaling law for received signal strength (RSS) geolocation, in 2011 45th Annual Conference on Information Science and Systems, CISS 2011 (2011), pp. 2–7. doi:10.1109/CISS.2011.5766210

    Google Scholar 

  23. R. Mautz, Indoor positioning technologies. Ph.D. thesis, ETH Zurich, 2012

    Google Scholar 

  24. C. Mensing, S. Sand, A. Dammann, GNSS positioning in critical scenarios: hybrid data fusion with communications signals, in 2009 IEEE International Conference Communication Work, 2 June 2009, pp. 1–6. doi:10.1109/ICCW.2009.5207983

    Google Scholar 

  25. V. Moghtadaiee, A.G. Dempster, S. Lim, Indoor localization using FM radio signals: a fingerprinting approach, in 2011 International Conference Indoor Position. Indoor Navigation Sept 2011, pp. 1–7. doi:10.1109/IPIN.2011.6071932

    Google Scholar 

  26. V. Pasku et al., A positioning system based on low frequency magnetic fields. IEEE Trans. Ind. Electron. 63, 2457–2468 (2016). ISSN: 0278-0046. doi:10.1109/TIE.2015.2499251

    Google Scholar 

  27. G. Piggott, Number of properties in London (2014). https://www.cityoflondon.gov.uk/business/economic-research-and-information/statistics/Pages/default.aspx

    Google Scholar 

  28. M. Robinson, R. Ghrist, Topological localization via signals of opportunity. IEEE Trans. Signal Process. 60 (5), 2362–2373 (2012). ISSN:1053-587X. doi:10.1109/TSP.2012.2187518

    Google Scholar 

  29. G. Seco-Granados et al., Challenges in indoor global navigation satellite systems, in IEEE Signal, Feb 2012, pp. 108–131

    Google Scholar 

  30. J. Seitz et al., Sensor data fusion for pedestrian navigation using WLAN and INS, in Proceedings of the Symposium Gyro Technology 2007 (Karlsruhe 2007), pp. 1–10. J. Seitz, L. Patino-Studencki, B. Schindler, S. Haimerl, J. Gutierrez, S. Meyer, J. Thielecke, Sensor data fusion for pedestrian navigation using WLAN and INS, in Symposium Gyro Technology (2007). https://cris.fau.de/converis/publicweb/Publication/1023736

  31. M. Shafi, S. Ogose, T. Hattori, PathLoss measurements for wireless mobile systems, in Wireless Communications in the 21st Century (IEEE, New York, 2009), pp. 185–194. doi:10.1109/9780470547076.ch10

    Google Scholar 

  32. subpos.org. SubPos (2016)

    Google Scholar 

  33. J. Talvitie, M. Renfors, E.S. Lohan, Novel indoor positioning mechanism via spectral compression. IEEE Commun. Lett. 20 (2), 352–355 (2016). ISSN:1089-7798. doi:10.1109/LCOMM.2015.2504097

    Google Scholar 

  34. X. Wu et al., Privacy preserving RSS map generation for a crowdsensing network. IEEE Wirel. Commun. 22 (4), 42–48 (2015). ISSN:1536-1284. doi:10.1109/MWC.2015.7224726

    Google Scholar 

  35. Y. Yuan et al., Estimating crowd density in an RF-based dynamic environment. IEEE Sensors J. 13 (10), 3837–3845 (2013). ISSN:1530-437X. doi:10.1109/JSEN.2013.2259692

    Google Scholar 

Download references

Acknowledgements

This work was financially supported by EU FP7 Marie Curie Initial Training Network MULTI-POS (Multi-technology Positioning Professionals) under grant nr. 316528.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pedro Figueiredo e Silva .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Figueiredo e Silva, P., Ferrara, N.G., Daniel, O., Nurmi, J., Lohan, ES. (2017). Mapping the Radio World to Find Us. In: Nurmi, J., Lohan, ES., Wymeersch, H., Seco-Granados, G., Nykänen, O. (eds) Multi-Technology Positioning. Springer, Cham. https://doi.org/10.1007/978-3-319-50427-8_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-50427-8_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50426-1

  • Online ISBN: 978-3-319-50427-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics