Towards Accelerated Localization Performance Across Indoor Positioning Datasets

Lucie Klus, Darwin Quezada-Gaibor, Joaquín Torres-Sospedra, Elena Simona Lohan, Carlos Granell, Jari Nurmi

Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

13 Lataukset (Pure)


The localization speed and accuracy in the indoor scenario can greatly impact the Quality of Experience of the user. While many individual machine learning models can achieve comparable positioning performance, their prediction mechanisms offer different complexity to the system. In this work, we propose a fingerprinting positioning method for multi-building and multi-floor deployments, composed of a cascade of three models for building classification, floor classification, and 2D localization regression. We conduct an exhaustive search for the optimally performing one in each step of the cascade while validating on 14 different openly available datasets. As a result, we bring forward the best-performing combination of models in terms of overall positioning accuracy and processing speed and evaluate on independent sets of samples. We reduce the mean prediction time by 71% while achieving comparable positioning performance across all considered datasets. Moreover, in case of voluminous training dataset, the prediction time is reduced down to 1% of the benchmark's.

Otsikko2022 International Conference on Localization and GNSS, ICL-GNSS 2022 - Proceedings
ToimittajatJari Nurmi, Elena-Simona Lohan, Joaquin Torres Sospedra, Heidi Kuusniemi, Aleksandr Ometov
ISBN (elektroninen)9781665405751
ISBN (painettu)9781665405768
DOI - pysyväislinkit
TilaJulkaistu - 2022
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Conference on Localization and GNSS - Tampere, Suomi
Kesto: 7 kesäk. 20229 kesäk. 2022


NimiInternational Conference on Localization and GNSS
ISSN (painettu)2325-0747
ISSN (elektroninen)2325-0771


ConferenceInternational Conference on Localization and GNSS


  • Jufo-taso 1

!!ASJC Scopus subject areas

  • Computer Networks and Communications
  • Aerospace Engineering


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