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.
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 language | English |
|---|---|
| Title of host publication | Artificial Neural Networks and Machine Learning : 21st International Conference on Artificial Neural Networks (ICANN 2011) |
| Editors | Timo Honkela, Wlodzislaw Duch, Mark A. Girolami, Samuel Kaski |
| Number of pages | 8 |
| Volume | 1 |
| Publisher | Springer-Verlag |
| Publication date | 2011 |
| Pages | 355-362 |
| ISBN (Print) | 978-3-642-21734-0 |
| DOIs | |
| Publication status | Published - 2011 |
| MoE publication type | A4 Article in conference proceedings |
| Event | International Conference on Artificial Neural Networks - Espoo, Finland Duration: 14 Jun 2011 → 17 Jun 2011 Conference number: 21st |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Publisher | Springer |
| Volume | 6791 |
Fields of Science
- 112 Statistics and probability
- 113 Computer and information sciences
- 213 Electronic, automation and communications engineering, electronics