Paper Info
Title
Integrated WiFi/PDR/Smartphone Using an Unscented Kalman Filter Algorithm for 3D Indoor Localization
Abstract
Because of the high calculation cost and poor performance of a traditional planar map when dealing with complicated indoor geographic information, a WiFi fingerprint indoor positioning system cannot be widely employed on a smartphone platform. By making full use of the hardware sensors embedded in the smartphone, this study proposes an integrated approach to a three-dimensional (3D) indoor positioning system. First, an improved K-means clustering method is adopted to reduce the fingerprint database retrieval time and enhance positioning efficiency. Next, with the mobile phone's acceleration sensor, a new step counting method based on auto-correlation analysis is proposed to achieve cell phone inertial navigation positioning. Furthermore, the integration of WiFi positioning with Pedestrian Dead Reckoning (PDR) obtains higher positional accuracy with the help of the Unscented Kalman Filter algorithm. Finally, a hybrid 3D positioning system based on Unity 3D, which can carry out real-time positioning for targets in 3D scenes, is designed for the fluent operation of mobile terminals.
Year
DOI
Venue
2015
10.3390/s150924595
SENSORS
Keywords
Field
DocType
indoor localization,WiFi,PDR,clustering,auto-correlation analysis,Unscented Kalman Filter,Unity 3D
Inertial navigation system,Hybrid positioning system,Algorithm,Fingerprint,Kalman filter,Dead reckoning,Mobile phone,Engineering,Cluster analysis,Indoor positioning system
Journal
Volume
Issue
Citations 
15
9
22
PageRank 
References 
Authors
1.08
15
6
Name
Order
Citations
PageRank
Guoliang Chen130546.48
Xiaolin Meng24912.86
Yunjia Wang37115.63
yanzhe zhang4221.08
peng tian5221.08
huachao yang6221.08