Indoor Localization Using Neural Networks with Location Fingerprints

  • Christos Laoudias
  • Demetrios G. Eliades
  • Paul Kemppi
  • Christos G. Panayiotou
  • Marios M. Polycarpou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5769)

Abstract

Reliable localization techniques applicable to indoor environments are essential for the development of advanced location aware applications. We rely on WLAN infrastructure and exploit location related information, such as the Received Signal Strength (RSS) measurements, to estimate the unknown terminal location. We adopt Artificial Neural Networks (ANN) as a function approximation approach to map vectors of RSS samples, known as location fingerprints, to coordinates on the plane. We present an efficient algorithm based on Radial Basis Function (RBF) networks and describe a data clustering method to reduce the network size. The proposed algorithm is practical and scalable, while the experimental results indicate that it outperforms existing techniques in terms of the positioning error.

Keywords

Localization WLAN Fingerprinting Received Signal Strength Radial Basis Function Networks 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Christos Laoudias
    • 1
  • Demetrios G. Eliades
    • 1
  • Paul Kemppi
    • 2
  • Christos G. Panayiotou
    • 1
  • Marios M. Polycarpou
    • 1
  1. 1.KIOS Research Center for Intelligent Systems and Networks Department of Electrical and Computer EngineeringUniversity of CyprusNicosiaCyprus
  2. 2.VTT Technical Research Centre of FinlandEspooFinland

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