Data fusion approaches for WiFi fingerprinting

Lohan, Elena Simona; Talvitie, Jukka

doi:10.1109/icl-gnss.2016.7533847

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…Furthermore, the MATLAB/Octave files for creating the more advanced algorithms, such as path loss-based estimators [23], Dempster-Shaffer-based estimators [24] or image-based processing [25,26], among others, are not made available due again to IP issues (e.g., calling some functions, which are proprietary code).…”

Section: Restrictions Of the Availabilitymentioning

confidence: 99%

Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning

Lohan

Torres-Sospedra

Leppäkoski

et al. 2017

Data

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Benchmark open-source Wi-Fi fingerprinting datasets for indoor positioning studies are still hard to find in the current literature and existing public repositories. This is unlike other research fields, such as the image processing field, where benchmark test images such as the Lenna image or Face Recognition Technology (FERET) databases exist, or the machine learning field, where huge datasets are available for example at the University of California Irvine (UCI) Machine Learning Repository. It is the purpose of this paper to present a new openly available Wi-Fi fingerprint dataset, comprised of 4648 fingerprints collected with 21 devices in a university building in Tampere, Finland, and to present some benchmark indoor positioning results using these data. The datasets and the benchmarking software are distributed under the open-source MIT license and can be found on the EU Zenodo repository.

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