Methods based on Received Signal Strength Indicator (RSSI) fingerprinting are in the forefront among several techniques being proposed for indoor positioning. This paper introduces the R package ipft, which provides algorithms and utility functions for indoor positioning using fingerprinting techniques. These functions are designed for manipulation of RSSI fingerprint data sets, estimation of positions, comparison of the performance of different positioning models, and graphical visualization of data. Well-known machine learning algorithms are implemented in this package to perform analysis and estimations over RSSI data sets. The paper provides a description of these algorithms and functions, as well as examples of its use with real data. The ipft package provides a base that we hope to grow into a comprehensive library of fingerprinting-based indoor positioning methodologies.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2019-010.zip
Graphics, Phylogenetics, TeachingStatistics
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For attribution, please cite this work as
Sansano, et al., "The R Journal: Indoor Positioning and Fingerprinting: The R Package ipft", The R Journal, 2019
BibTeX citation
@article{RJ-2019-010,
author = {Sansano, Emilio and Montoliu, Raúl and Belmonte, Óscar and Torres-Sospedra, Joaquín},
title = {The R Journal: Indoor Positioning and Fingerprinting: The R Package ipft},
journal = {The R Journal},
year = {2019},
note = {https://doi.org/10.32614/RJ-2019-010},
doi = {10.32614/RJ-2019-010},
volume = {11},
issue = {1},
issn = {2073-4859},
pages = {67-90}
}