Applied sciences, 2019-10, Vol.9 (20), p.4379
2019
Details
- Autor(en) / Beteiligte
- Titel
- A Sensor Fusion Framework for Indoor Localization Using Smartphone Sensors and Wi-Fi RSSI Measurements
- Ist Teil von
- Applied sciences, 2019-10, Vol.9 (20), p.4379
- Ort / Verlag
- Basel: MDPI AG
- Erscheinungsjahr
- 2019
- Link zum Volltext
- Quelle
- EZB Electronic Journals Library
- Beschreibungen/Notizen
- Sensor fusion frameworks for indoor localization are developed with the specific goal of reducing positioning errors. Although many conventional localization frameworks without fusion have been improved to reduce positioning error, sensor fusion frameworks generally provide a further improvement in positioning accuracy. In this paper, we propose a sensor fusion framework for indoor localization using the smartphone inertial measurement unit (IMU) sensor data and Wi-Fi received signal strength indication (RSSI) measurements. The proposed sensor fusion framework uses location fingerprinting and trilateration for Wi-Fi positioning. Additionally, a pedestrian dead reckoning (PDR) algorithm is used for position estimation in indoor scenarios. The proposed framework achieves a maximum of 1.17 m localization error for the rectangular motion of a pedestrian and a maximum of 0.44 m localization error for linear motion.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 2076-3417eISSN: 2076-3417DOI: 10.3390/app9204379Titel-ID: cdi_doaj_primary_oai_doaj_org_article_e985b94a7d5b4fbe84d28bba28c30b34
- Format
- –
- Schlagworte
- Accuracy, Algorithms, Dead reckoning, Error reduction, Experiments, Fingerprinting, Global positioning systems, GPS, Indoor environments, indoor localization, Inertial platforms, Inertial sensing devices, kalman filter, Localization, location fingerprinting, pedestrian dead reckoning (pdr), Radio frequency identification, received signal strength indication (rssi), sensor fusion frameworks, Sensors, Signal strength, smartphone sensors, Smartphones, Trilateration, wi-fi indoor positioning