Selective mixture of Gaussians clustering for location fingerprinting

Khuong An Nguyen, Zhiyuan Luo

Research output: Contribution to conferencePaper

Abstract

One of the challenges of location fingerprinting to be deployed in the real offices is the training database handling process, which does not scale well with increasing amount of tracking space to be covered. However, little attention was paid to tackle such issue, where the majority of previous work rather focused on improving the tracking accuracy.

In this paper, we propose a novel idea to enhance fingerprinting's processing speed and positioning accuracy with mixture of Gaussians clustering. We realised the key difference between fingerprinting and other un-supervised problems, that is we do know the label (the Cartesian co-ordinate) of the signal data in advance. This key information was largely ignored in previous work, where the fingerprinting clustering was based solely on the signal data information. By exploiting this information, we tackle the indoor signal multipath and shadowing with two-level signal data clustering and Cartesian co-ordinate clustering.

We tested our approach in a real office environment with harsh indoor condition, and concluded that our clustering scheme does not only reduce the fingerprinting processing time, but also improves the positioning accuracy.

Conference

Conference11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2014)
CountryUnited Kingdom
Period2/12/145/12/14
Internet address

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  • Cite this

    Nguyen, K. A., & Luo, Z. (2014). Selective mixture of Gaussians clustering for location fingerprinting. Paper presented at 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2014), United Kingdom. https://doi.org/10.4108/icst.mobiquitous.2014.257983