Improving the Precision of Wireless Localization Algorithms: ML Techniques for Indoor Positioning

Pavel Masek, Petr Sedlacek, Aleksandr Ometov, Jiri Mekyska, Petr Mlynek, Jiri Hosek, Mikhail Komarov

Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

1 Sitaatiot (Scopus)
25 Lataukset (Pure)

Abstrakti

Due to the tremendous increase in the number of wearable devices and proximity-based services, the need for improved indoor localization techniques becomes more significant. The evolution of the positioning from a hardware perspective is pacing its way along with various software-based approaches also powered by Machine Learning (ML). In this paper, we apply ML algorithms to the real-life collected signal parameters in an indoor localization system based on Ultra-Wideband (UWB) technology to make an analysis of the signal and classify it accordingly. The contribution aims to answer the question of whether an indoor positioning system could benefit from utilizing ML for signal parameter analysis in order to increase its location accuracy, reliability, and robustness across various environments. To this end, we compare different applications of ML approaches and detail the trade-off between computational speed and accuracy.
AlkuperäiskieliEnglanti
Otsikko43rd International Conference on Telecommunications and Signal Processing (TSP)
Sivut589-594
Sivumäärä6
DOI - pysyväislinkit
TilaJulkaistu - 7 heinäk. 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Conference on Telecommunications and Signal Processing - Milan, Italia
Kesto: 7 heinäk. 20209 heinäk. 2020

Conference

ConferenceInternational Conference on Telecommunications and Signal Processing
Maa/AlueItalia
KaupunkiMilan
Ajanjakso7/07/209/07/20

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