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Data Fusion Approaches For WiFi Fingerprinting

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoInternational Conference on Localization and GNSS (ICL-GNSS 2016)
KustantajaIEEE
ISBN (elektroninen)978-1-5090-1757-7
DOI - pysyväislinkit
TilaJulkaistu - 2016
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaINTERNATIONAL CONFERENCE ON LOCALIZATION AND GNSS -
Kesto: 1 tammikuuta 1900 → …

Julkaisusarja

Nimi
ISSN (elektroninen)2325-0771

Conference

ConferenceINTERNATIONAL CONFERENCE ON LOCALIZATION AND GNSS
Ajanjakso1/01/00 → …

Tiivistelmä

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.

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