Semi-supervised Learning for WLAN Positioning

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 ...

• Type: Journal
• Year: 2011
• Where: ICANN
• Authors: Teemu Pulkkinen, Teemu Roos, Petri Myllymäki
• Keywords: ICANN 2011, Linear Projection, Neural Networks, Radio Maps, Signal Strength Measurements