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Data Fusion Approaches For WiFi Fingerprinting

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Standard

Data Fusion Approaches For WiFi Fingerprinting. / Lohan, Elena-Simona; Talvitie, Jukka; Granados, Gonzalo Seco.

International Conference on Localization and GNSS (ICL-GNSS 2016). IEEE, 2016.

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Harvard

Lohan, E-S, Talvitie, J & Granados, GS 2016, Data Fusion Approaches For WiFi Fingerprinting. julkaisussa International Conference on Localization and GNSS (ICL-GNSS 2016). IEEE, INTERNATIONAL CONFERENCE ON LOCALIZATION AND GNSS, 1/01/00. https://doi.org/10.1109/ICL-GNSS.2016.7533847

APA

Lohan, E-S., Talvitie, J., & Granados, G. S. (2016). Data Fusion Approaches For WiFi Fingerprinting. teoksessa International Conference on Localization and GNSS (ICL-GNSS 2016) IEEE. https://doi.org/10.1109/ICL-GNSS.2016.7533847

Vancouver

Lohan E-S, Talvitie J, Granados GS. Data Fusion Approaches For WiFi Fingerprinting. julkaisussa International Conference on Localization and GNSS (ICL-GNSS 2016). IEEE. 2016 https://doi.org/10.1109/ICL-GNSS.2016.7533847

Author

Lohan, Elena-Simona ; Talvitie, Jukka ; Granados, Gonzalo Seco. / Data Fusion Approaches For WiFi Fingerprinting. International Conference on Localization and GNSS (ICL-GNSS 2016). IEEE, 2016.

Bibtex - Lataa

@inproceedings{eed165d7bdfe4aac9060d6e45a3b51a2,
title = "Data Fusion Approaches For WiFi Fingerprinting",
abstract = "WiFi localization problem is basically a multi-sensor data fusion. This paper investigates the use of Bayesian and non-Bayesian Dempster Shafer (DS) data fusion in the context of WiFi-based indoor positioning via fingerprinting. Two novel DS mass choices are discussed. The positioning results are based on real-field measurement data from nine distinct multi-floor buildings in two countries. It is shown that a proper mass choice is crucial in DS processing and that, in spite of taking into account the data uncertainty, the DS data fusion is not offering significant advantage in terms of positioning performance over the Bayesian data fusion.",
author = "Elena-Simona Lohan and Jukka Talvitie and Granados, {Gonzalo Seco}",
note = "JUFOID=72237",
year = "2016",
doi = "10.1109/ICL-GNSS.2016.7533847",
language = "English",
publisher = "IEEE",
booktitle = "International Conference on Localization and GNSS (ICL-GNSS 2016)",

}

RIS (suitable for import to EndNote) - Lataa

TY - GEN

T1 - Data Fusion Approaches For WiFi Fingerprinting

AU - Lohan, Elena-Simona

AU - Talvitie, Jukka

AU - Granados, Gonzalo Seco

N1 - JUFOID=72237

PY - 2016

Y1 - 2016

N2 - WiFi localization problem is basically a multi-sensor data fusion. This paper investigates the use of Bayesian and non-Bayesian Dempster Shafer (DS) data fusion in the context of WiFi-based indoor positioning via fingerprinting. Two novel DS mass choices are discussed. The positioning results are based on real-field measurement data from nine distinct multi-floor buildings in two countries. It is shown that a proper mass choice is crucial in DS processing and that, in spite of taking into account the data uncertainty, the DS data fusion is not offering significant advantage in terms of positioning performance over the Bayesian data fusion.

AB - WiFi localization problem is basically a multi-sensor data fusion. This paper investigates the use of Bayesian and non-Bayesian Dempster Shafer (DS) data fusion in the context of WiFi-based indoor positioning via fingerprinting. Two novel DS mass choices are discussed. The positioning results are based on real-field measurement data from nine distinct multi-floor buildings in two countries. It is shown that a proper mass choice is crucial in DS processing and that, in spite of taking into account the data uncertainty, the DS data fusion is not offering significant advantage in terms of positioning performance over the Bayesian data fusion.

U2 - 10.1109/ICL-GNSS.2016.7533847

DO - 10.1109/ICL-GNSS.2016.7533847

M3 - Conference contribution

BT - International Conference on Localization and GNSS (ICL-GNSS 2016)

PB - IEEE

ER -