Semi-supervised Learning for WLAN Positioning

  • Teemu Pulkkinen
  • Teemu Roos
  • Petri Myllymäki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6791)


Currently the most accurate WLAN positioning systems are based on the fingerprinting approach, where a “radio map” is constructed by modeling how the signal strength measurements vary according to the location. However, collecting a sufficient amount of location-tagged training data is a rather tedious and time consuming task, especially in indoor scenarios — the main application area of WLAN positioning — where GPS coverage is unavailable. To alleviate this problem, we present a semi-supervised manifold learning technique for building accurate radio maps from partially labeled data, where only a small portion of the signal strength measurements need to be tagged with the corresponding coordinates. The basic idea is to construct a non-linear projection that maps high-dimensional signal fingerprints onto a two-dimensional manifold, thereby dramatically reducing the need of location-tagged data. Our results from a deployment in a real-world experiment demonstrate the practical utility of the method.


non-linear projection manifold learning wlan positioning Isomap 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Teemu Pulkkinen
    • 1
  • Teemu Roos
    • 1
  • Petri Myllymäki
    • 1
  1. 1.Helsinki Institute for Information Technology HIITUniversity of HelsinkiFinland

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