Abstract
Indoor localization has gained considerable attention over the past decade because of the emergence of numerous location-aware services. Research works have been proposed on solving this problem by using wireless networks. Nevertheless, there is still much room for improvement in the quality of the proposed classification models. In the last years, the emergence of Visible Light Communication (VLC) brings a brand new approach to high quality indoor positioning. Among its advantages, this new technology is immune to electromagnetic interference and has the advantage of having a smaller variance of received signal power compared to RF based technologies. In this paper, a performance analysis of seventeen machine leaning classifiers for indoor localization in VLC networks is carried out. The analysis is accomplished in terms of accuracy, average distance error, computational cost, training size, precision and recall measurements. Results show that most of classifiers harvest an accuracy above 90 %. The best tested classifier yielded a 99.0 % accuracy, with an average error distance of 0.3 centimetres.
Original language | English |
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Title of host publication | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Publisher | ISPRS |
Pages | 385-391 |
Volume | IV-2/W4 |
DOIs | |
Publication status | Published - 2017 |
Event | ISPRS Geospatial Week 2017 - Wuhan, China Duration: 18 Sep 2017 → 22 Sep 2017 http://www.isprs.org/documents/geoweek.aspx |
Conference
Conference | ISPRS Geospatial Week 2017 |
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Country | China |
City | Wuhan |
Period | 18/09/17 → 22/09/17 |
Internet address |
Keywords
- Fingerprinting Techniques
- Indoor Localization
- Machine Learning Classifiers
- Visible Light Communication