Integrated WiFi/PDR/Smartphone using an unscented Kalman filter algorithm for 3D indoor localization

Chen, Guoliang, Meng, Xiaolin, Wang, Yunjia, Zhang, Yanzhe and Tian, Peng (2015) Integrated WiFi/PDR/Smartphone using an unscented Kalman filter algorithm for 3D indoor localization. Sensors, 15 (9). pp. 24595-24614. ISSN 1424-8220

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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.

Item Type: Article
Keywords: Indoor localization; WiFi/PDR; Clustering; Auto-correlation analysis; Unscented Kalman Filter; Unity 3D
Schools/Departments: University of Nottingham, UK > Faculty of Engineering > Department of Civil Engineering
Identification Number:
Depositing User: Meng, Xiaolin
Date Deposited: 02 Aug 2016 10:16
Last Modified: 04 May 2020 17:16

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