Semi-supervised learning for WLAN positioning
Teemu Pulkkinen, Teemu Roos and Petri Myllymäki
In: The 21st International Conference on Artificial Neural Networks (ICANN-2011), Helsinki, Finland(2011).
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.