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Sensors 2015, 15(10), 24862-24885; doi:10.3390/s151024862

A Bluetooth/PDR Integration Algorithm for an Indoor Positioning System

1,2,* , 2
and
2
1
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
2
School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 31 July 2015 / Revised: 18 September 2015 / Accepted: 21 September 2015 / Published: 25 September 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1189 KB, uploaded 25 September 2015]   |  

Abstract

This paper proposes two schemes for indoor positioning by fusing Bluetooth beacons and a pedestrian dead reckoning (PDR) technique to provide meter-level positioning without additional infrastructure. As to the PDR approach, a more effective multi-threshold step detection algorithm is used to improve the positioning accuracy. According to pedestrians’ different walking patterns such as walking or running, this paper makes a comparative analysis of multiple step length calculation models to determine a linear computation model and the relevant parameters. In consideration of the deviation between the real heading and the value of the orientation sensor, a heading estimation method with real-time compensation is proposed, which is based on a Kalman filter with map geometry information. The corrected heading can inhibit the positioning error accumulation and improve the positioning accuracy of PDR. Moreover, this paper has implemented two positioning approaches integrated with Bluetooth and PDR. One is the PDR-based positioning method based on map matching and position correction through Bluetooth. There will not be too much calculation work or too high maintenance costs using this method. The other method is a fusion calculation method based on the pedestrians’ moving status (direct movement or making a turn) to determine adaptively the noise parameters in an Extended Kalman Filter (EKF) system. This method has worked very well in the elimination of various phenomena, including the “go and back” phenomenon caused by the instability of the Bluetooth-based positioning system and the “cross-wall” phenomenon due to the accumulative errors caused by the PDR algorithm. Experiments performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI) building in the China University of Mining and Technology (CUMT) campus showed that the proposed scheme can reliably achieve a 2-meter precision. View Full-Text
Keywords: bluetooth beacons; pedestrian dead reckoning; adaptive system noise; extended kalman filter; map matching bluetooth beacons; pedestrian dead reckoning; adaptive system noise; extended kalman filter; map matching
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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Li, X.; Wang, J.; Liu, C. A Bluetooth/PDR Integration Algorithm for an Indoor Positioning System. Sensors 2015, 15, 24862-24885.

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