Indoor Positioning and Fingerprinting:The R Package ipft
![Thumbnail](/xmlui/bitstream/handle/10234/185365/66799.pdf.jpg?sequence=5&isAllowed=y)
Visualitza/
Impacte
![Google Scholar](/xmlui/themes/Mirage2/images/uji/logo_google.png)
![Microsoft Academico](/xmlui/themes/Mirage2/images/uji/logo_microsoft.png)
Metadades
Mostra el registre complet de l'elementcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7034
comunitat-uji-handle3:10234/8619
comunitat-uji-handle4:
INVESTIGACIONMetadades
Títol
Indoor Positioning and Fingerprinting:The R Package ipftAutoria
Data de publicació
2019Editor
The R FoundationISSN
2073-4859Cita bibliogràfica
SANSANO, Emilio, et al. Indoor Positioning and Fingerprinting: The R Package ipft. The R Journal, 2019, vol. 11, núm. 1, p. 67-90Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
https://journal.r-project.org/archive/2019/RJ-2019-010/index.htmlVersió
info:eu-repo/semantics/publishedVersionResum
Methods based on Received Signal Strength Indicator (RSSI) fingerprinting are in theforefront among several techniques being proposed for indoor positioning. This paper introducesthe R packageipft, which ... [+]
Methods based on Received Signal Strength Indicator (RSSI) fingerprinting are in theforefront among several techniques being proposed for indoor positioning. This paper introducesthe R packageipft, which provides algorithms and utility functions for indoor positioning usingfingerprinting techniques. These functions are designed for manipulation of RSSI fingerprint datasets, estimation of positions, comparison of the performance of different positioning models, andgraphical visualization of data. Well-known machine learning algorithms are implemented in thispackage to perform analysis and estimations over RSSI data sets. The paper provides a descriptionof these algorithms and functions, as well as examples of its use with real data. Theipftpackageprovides a base that we hope to grow into a comprehensive library of fingerprinting-based indoorpositioning methodologies. [-]
Publicat a
The R Journal, 2019, vol. 11, núm. 1, p. 67-90Proyecto de investigación
This work has been partially funded by the Spanish Ministry of Economy and Competitivenessthrough the "Proyectos I + D Excelencia" programme (TIN2015-70202-P) and by Jaume I University"Research promotion plan 2017" programme (UJI-B2017-45).Drets d'accés
© The R Foundation
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
Apareix a les col.leccions
- INIT_Articles [727]
- LSI_Articles [345]
- ICC_Articles [407]
- ESID_Articles [453]
Els següents fitxers sobre la llicència estan associats a aquest element: