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Acknowledgement to Reviewers of ISPRS International Journal of Geo-Information in 2015
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ISPRS Int. J. Geo-Inf. 2016, 5(2), 8; doi:10.3390/ijgi5020008

Integrated WiFi/PDR/Smartphone Using an Adaptive System Noise Extended Kalman Filter Algorithm for Indoor Localization

1
Jiangsu Key Laboratory of Resources and Environment Information Engineering, Xuzhou 221116, China
2
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Academic Editors: Georg Gartner, Haosheng Huang and Wolfgang Kainz
Received: 19 October 2015 / Revised: 19 January 2016 / Accepted: 22 January 2016 / Published: 1 February 2016
(This article belongs to the Special Issue Location-Based Services)
View Full-Text   |   Download PDF [5610 KB, uploaded 1 February 2016]   |  

Abstract

Wireless signal strength is susceptible to the phenomena of interference, jumping, and instability, which often appear in the positioning results based on Wi-Fi field strength fingerprint database technology for indoor positioning. Therefore, a Wi-Fi and PDR (pedestrian dead reckoning) real-time fusion scheme is proposed in this paper to perform fusing calculation by adaptively determining the dynamic noise of a filtering system according to pedestrian movement (straight or turning), which can effectively restrain the jumping or accumulation phenomena of wireless positioning and the PDR error accumulation problem. Wi-Fi fingerprint matching typically requires a quite high computational burden: To reduce the computational complexity of this step, the affinity propagation clustering algorithm is adopted to cluster the fingerprint database and integrate the information of the position domain and signal domain of respective points. An experiment performed in a fourth-floor corridor at the School of Environment and Spatial Informatics, China University of Mining and Technology, shows that the traverse points of the clustered positioning system decrease by 65%–80%, which greatly improves the time efficiency. In terms of positioning accuracy, the average error is 4.09 m through the Wi-Fi positioning method. However, the positioning error can be reduced to 2.32 m after integration of the PDR algorithm with the adaptive noise extended Kalman filter (EKF). View Full-Text
Keywords: indoor positioning; affinity propagation clustering; feature fusion; pedestrian dead reckoning; multi-sensor fusion indoor positioning; affinity propagation clustering; feature fusion; pedestrian dead reckoning; multi-sensor fusion
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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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MDPI and ACS Style

Li, X.; Wang, J.; Liu, C.; Zhang, L.; Li, Z. Integrated WiFi/PDR/Smartphone Using an Adaptive System Noise Extended Kalman Filter Algorithm for Indoor Localization. ISPRS Int. J. Geo-Inf. 2016, 5, 8.

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