Acta Geodaetica et Cartographica Sinica >

2015 , Vol. 44 >Issue 12: 1314 - 1321

DOI: https://doi.org/10.11947/j.AGCS.2015.20140691

Unscented Kalman Filter Algorithm for WiFi-PDR Integrated Indoor Positioning

  • CHEN GuoLiang ,
  • ZHANG Yanzhe ,
  • WANG Yunjia ,
  • MENG Xiaolin
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  • 1. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China;
    2. Key Laboratory for Land Environment and Disaster Monitoring of SBSM, China University of Mining and Technology, Xuzhou 221116, China;
    3. The University of Nottingham, Nottingham NG7 2TU, UK

Received date: 2015-01-01

  Revised date: 2015-05-11

  Online published: 2016-01-04

Supported by

The National High-tech Research and Development Program of China (863 Program) (No.2013AA12A201);The National Natural Science Foundation of China (No.41371423);Engineering Construction of Jiangsu Universities (No.SZBF2011-6-B35)

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Abstract

Indoor positioning still faces lots of fundamental technical problems although it has been widely applied. A novel indoor positioning technology by using the smart phone with the assisting of the widely available and economically signals of WiFi is proposed. It also includes the principles and characteristics in indoor positioning. Firstly, improve the system's accuracy by fusing the WiFi fingerprinting positioning and PDR (ped estrian dead reckoning) positioning with UKF (unscented Kalman filter). Secondly, improve the real-time performance by clustering the WiFi fingerprinting with k-means clustering algorithm. An investigation test was conducted at the indoor environment to learn about its performance on a HUAWEI P6-U06 smart phone. The result shows that compared to the pattern-matching system without clustering, an average reduction of 51% in the time cost can be obtained without degrading the positioning accuracy. When the state of personnel is walking, the average positioning error of WiFi is 7.76 m, the average positioning error of PDR is 4.57 m. After UKF fusing, the system's average positioning error is down to 1.24 m. It shows that the algorithm greatly improves the system's real-time and positioning accuracy.

Key words： Indoor positioning; smart phone sensors; WiFi; ped estrian dead reckoning; k-means; UKF

Cite this article

CHEN GuoLiang , ZHANG Yanzhe , WANG Yunjia , MENG Xiaolin . Unscented Kalman Filter Algorithm for WiFi-PDR Integrated Indoor Positioning[J]. Acta Geodaetica et Cartographica Sinica, 2015 , 44(12) : 1314 -1321 . DOI: 10.11947/j.AGCS.2015.20140691

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