Semi-supervised learning for WLAN positioning

Authors:
Teemu Pulkkinen;Teemu Roos;Petri Myllymäki
Affiliations:
Helsinki Institute for Information Technology, University of Helsinki, Finland;Helsinki Institute for Information Technology, University of Helsinki, Finland;Helsinki Institute for Information Technology, University of Helsinki, Finland
Venue:
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Year:
2011

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Abstract

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.