Semi-supervised learning for WLAN positioning

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


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.
Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning : 21st International Conference on Artificial Neural Networks (ICANN 2011)
EditorsTimo Honkela, Wlodzislaw Duch, Mark A. Girolami, Samuel Kaski
Number of pages8
Publication date2011
ISBN (Print)978-3-642-21734-0
Publication statusPublished - 2011
MoE publication typeA4 Article in conference proceedings
EventInternational Conference on Artificial Neural Networks - Espoo, Finland
Duration: 14 Jun 201117 Jun 2011
Conference number: 21st

Publication series

NameLecture Notes in Computer Science

Fields of Science

  • 112 Statistics and probability
  • 113 Computer and information sciences
  • 213 Electronic, automation and communications engineering, electronics

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