Lookup Lateration: Mapping of Received Signal Strength to Position for Geo-Localization in Outdoor Urban Areas

  • Andrey Shestakov
  • Danila Doroshin
  • Dmitri Shmelkin
  • Attila Kertész-FarkasEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11179)


The accurate geo-localization of mobile devices based upon received signal strength (RSS) in an urban area is hindered by obstacles in the signal propagation path. Current localization methods have their own advantages and drawbacks. Triangular lateration (TL) is fast and scalable but employs a monotone RSS-to-distance transformation that unfortunately assumes mobile devices are on the line of sight. Radio frequency fingerprinting (RFP) methods employ a reference database, which ensures accurate localization but unfortunately hinders scalability.

Here, we propose a new, simple, and robust method called lookup lateration (LL), which incorporates the advantages of TL and RFP without their drawbacks. Like RFP, LL employs a dataset of reference locations but stores them in separate lookup tables with respect to RSS and antenna towers. A query observation is localized by identifying common locations in only associating lookup tables. Due to this decentralization, LL is two orders of magnitude faster than RFP, making it particularly scalable for large cities. Moreover, we show that analytically and experimentally, LL achieves higher localization accuracy than RFP as well. For instance, using grid size 20 m, LL achieves 9.11 m and 55.66 m, while RFP achieves 72.50 m and 242.19 m localization errors at 67% and 95%, respectively, on the Urban Hannover Scenario dataset.


Non-line-of-sight Outdoor mobile device geo-localization Fingerprinting 


  1. 1.
    Directive 2002/58/EC on privacy and electronic communicationsGoogle Scholar
  2. 2.
    Bahl, P., Padmanabhan, V.N.: Radar: an in-building RF-based user location and tracking system. In: Proceedings of Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2000, vol. 2, pp. 775–784. IEEE (2000)Google Scholar
  3. 3.
    Campos, R.S., Lovisolo, L.: A fast database correlation algorithm for localization of wireless network mobile nodes using coverage prediction and round trip delay. In: VTC Spring. IEEE (2009)Google Scholar
  4. 4.
    Campos, R.S., Lovisolo, L.: Mobile station location using genetic algorithm optimized radio frequency fingerprinting. In: Proceedings of ITS 2010 International Telecommunications Symposium (2010)Google Scholar
  5. 5.
    Cong, L., Zhuang, W.: Nonline-of-sight error mitigation in mobile location. IEEE Trans. Wirel. Commun. 4(2), 560–573 (2005)CrossRefGoogle Scholar
  6. 6.
    FCC: Revision of the commission rule to ensure compatibility with enhanced 911 emergency calling system. Technical report RM-8143, Federal Communications Commission (FCC), Washington, DC (2015)Google Scholar
  7. 7.
    Gentile, C., Alsindi, N., Raulefs, R., Teolis, C.: Geolocation Techniques: Principles and Applications. Springer, Heidelberg (2012)Google Scholar
  8. 8.
    Gezici, S.: A survey on wireless position estimation. Wirel. Pers. Commun. 44(3), 263–282 (2008)CrossRefGoogle Scholar
  9. 9.
    He, S., Chan, S.H.G.: Tilejunction: mitigating signal noise for fingerprint-based indoor localization. IEEE Trans. Mobile Comput. 15(6), 1554–1568 (2016)CrossRefGoogle Scholar
  10. 10.
    Ma, C., Klukas, R., Lachapelle, G.: A nonline-of-sight error-mitigation method for TOA measurements. IEEE Trans. Veh. Technol. 56(2), 641–651 (2007)CrossRefGoogle Scholar
  11. 11.
    Magro, M.J., Debono, C.J.: A genetic algorithm approach to user location estimation in UMTS networks. In: The International Conference on “Computer as a Tool”, EUROCON 2007, pp. 1136–1139. IEEE (2007)Google Scholar
  12. 12.
    Marco, A., Casas, R., Asensio, A., Coarasa, V., Blasco, R., Ibarz, A.: Least median of squares for non-line-of-sight error mitigation in GSM localization. In: 2008 IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications, pp. 1–5, September 2008Google Scholar
  13. 13.
    Neskovic, A., Neskovic, N., Paunovic, G.: Modern approaches in modeling of mobile radio systems propagation environment. IEEE Commun. Surv. Tutorials 3(3), 2–12 (2000)CrossRefGoogle Scholar
  14. 14.
    Nuño-Barrau, G., Páz-Borrallo, J.M.: A new location estimation system for wireless networks based on linear discriminant functions and hidden Markov models. EURASIP J. Appl. Signal Process. 2006, 159–159 (2006)Google Scholar
  15. 15.
    Rose, D.M., Jansen, T., Werthmann, T., Türke, U., Kürner, T.: The IC 1004 urban hannover scenario - 3D pathloss predictions and realistic traffic and mobility patterns (2013)Google Scholar
  16. 16.
    Takenga, C., Xi, C., Kyamakya, K.: A hybrid neural network-data base correlation positioning in GSM network. In: 2006 10th IEEE Singapore International Conference on Communication Systems, pp. 1–5, October 2006Google Scholar
  17. 17.
    Takenga, C.M., Kyamakya, K.: Location fingerprinting in GSM network and impact of data pre-processing (2006)Google Scholar
  18. 18.
    Wu, C.L., Fu, L.C., Lian, F.L.: WLAN location determination in e-home via support vector classification. In: 2004 IEEE International Conference on Networking, Sensing and Control, vol. 2, pp. 1026–1031. IEEE (2004)Google Scholar
  19. 19.
    Yu, L., Laaraiedh, M., Avrillon, S., Uguen, B.: Fingerprinting localization based on neural networks and ultra-wideband signals. In: 2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 184–189, December 2011Google Scholar
  20. 20.
    Zekavat, R., Buehrer, R.M.: Handbook of Position Location: Theory, Practice and Advances, 1st edn. Wiley/IEEE Press, Hoboken/Piscataway (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Andrey Shestakov
    • 1
  • Danila Doroshin
    • 2
  • Dmitri Shmelkin
    • 2
  • Attila Kertész-Farkas
    • 1
    Email author
  1. 1.School of Data Analysis and Artificial Intelligence, Faculty of Computer ScienceNational Research University Higher School of Economics (HSE)MoscowRussia
  2. 2.Huawei Technologies Co. Ltd.MoscowRussia

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