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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)

Abstract

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.

Keywords

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

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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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