The R Journal: article published in 2019, volume 11:1

Indoor Positioning and Fingerprinting: The R Package ipft
Emilio Sansano, Raúl Montoliu, Óscar Belmonte and Joaquín Torres-Sospedra , The R Journal (2019) 11:1, pages 67-90.

Abstract 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.

Received: 2018-02-02; online 2019-08-15, supplementary material, (1.8 KiB)
CRAN packages: ipft, ggplot2
CRAN Task Views implied by cited CRAN packages: Graphics, Phylogenetics, TeachingStatistics

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2019-010,
  author = {Emilio Sansano and Raúl Montoliu and Óscar Belmonte and
          Joaquín Torres-Sospedra},
  title = {{Indoor Positioning and Fingerprinting: The R Package ipft}},
  year = {2019},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2019-010},
  url = {https://doi.org/10.32614/RJ-2019-010},
  pages = {67--90},
  volume = {11},
  number = {1}
}