A Sensor Fusion Framework for Indoor Localization Using Smartphone Sensors and Wi-Fi RSSI Measurements

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...

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Bibliographic Details
Published in:Applied sciences 2019-10, Vol.9 (20), p.4379
Main Authors: Poulose, Alwin, Kim, Jihun, Han, Dong Seog
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Dead reckoning
Error reduction
Experiments
Fingerprinting
Global positioning systems
GPS
Indoor environments
indoor localization
Inertial platforms
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
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9204379