Accurate WiFi based indoor positioning with continuous location sampling
| Autor: | van Engelen, J.E., van Lier, J.J., Takes, F.W., Trautmann, H., Brefeld, U., Curry, E., Daly, E., MacNamee, B., Marascu, A., Pinelli, F., Berlingerio, M., Hurley, N. |
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| Přispěvatelé: | Political Economy and Transnational Governance (PETGOV, AISSR, FMG) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Accuracy and precision
Computer science business.industry 05 social sciences Real-time computing Sampling (statistics) Estimator 020206 networking & telecommunications Robotics 02 engineering and technology symbols.namesake 0502 economics and business 0202 electrical engineering electronic engineering information engineering symbols Artificial intelligence business Gaussian process Reflection mapping Smoothing 050205 econometrics Interpolation |
| Zdroj: | Machine Learning and Knowledge Discovery in Databases ISBN: 9783030109967 ECML/PKDD (3) Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018 : proceedings, 3, 524-540 |
| Popis: | The ubiquity of WiFi access points and the sharp increase in WiFi-enabled devices carried by humans have paved the way for WiFi-based indoor positioning and location analysis. Locating people in indoor environments has numerous applications in robotics, crowd control, indoor facility optimization, and automated environment mapping. However, existing WiFi-based positioning systems suffer from two major problems: (1) their accuracy and precision is limited due to inherent noise induced by indoor obstacles, and (2) they only occasionally provide location estimates, namely when a WiFi-equipped device emits a signal. To mitigate these two issues, we propose a novel Gaussian process (GP) model for WiFi signal strength measurements. It allows for simultaneous smoothing (increasing accuracy and precision of estimators) and interpolation (enabling continuous sampling of location estimates). Furthermore, simple and efficient smoothing methods for location estimates are introduced to improve localization performance in real-time settings. Experiments are conducted on two data sets from a large real-world commercial indoor retail environment. Results demonstrate that our approach provides significant improvements in terms of precision and accuracy with respect to unfiltered data. Ultimately, the GP model realizes continuous location sampling with consistently high quality location estimates. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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