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Sensors 2015, 15(9), 24595-24614; doi:10.3390/s150924595

Integrated WiFi/PDR/Smartphone Using an Unscented Kalman Filter Algorithm for 3D Indoor Localization

1
School of Environment Science and Spatial Informatics, China University of Mining and Technology, 1 Daxue Road, 221116 Xuzhou, China
2
Nottingham Geospatial Institute, University of Nottingham, Triumph Road, NG7 2TU Nottingham, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Kourosh Khoshelham
Received: 24 July 2015 / Accepted: 17 September 2015 / Published: 23 September 2015
(This article belongs to the Special Issue Sensors for Indoor Mapping and Navigation)
View Full-Text   |   Download PDF [2020 KB, uploaded 24 September 2015]   |  

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. View Full-Text
Keywords: indoor localization; WiFi/PDR; clustering; auto-correlation analysis; Unscented Kalman Filter; Unity 3D indoor localization; WiFi/PDR; clustering; auto-correlation analysis; Unscented Kalman Filter; Unity 3D
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Chen, G.; Meng, X.; Wang, Y.; Zhang, Y.; Tian, P.; Yang, H. Integrated WiFi/PDR/Smartphone Using an Unscented Kalman Filter Algorithm for 3D Indoor Localization. Sensors 2015, 15, 24595-24614.

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