Abstract
In addition to being a fundamental infrastructure for communication, cellular networks are employed for positioning through signal fingerprinting. In this respect, the choice of the specific strategy used to obtain a position estimation from fingerprints plays a major role in determining the overall accuracy. In this paper, a new machine learning approach, based on decision tree ensembles, is outlined and evaluated against a set of well-known, state-of-the-art fingerprint comparison functions from the literature. Tests are carried out with different tracking devices and environmental settings. It turns out that the proposed approach provides consistently better estimations than the other considered functions.
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Viel, A., Brunello, A., Montanari, A., Pittino, F. (2018). An Original Approach to Positioning with Cellular Fingerprints Based on Decision Tree Ensembles. In: Kiefer, P., Huang, H., Van de Weghe, N., Raubal, M. (eds) Progress in Location Based Services 2018. LBS 2018. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-71470-7_3
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DOI: https://doi.org/10.1007/978-3-319-71470-7_3
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