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Sensors 2017, 17(5), 951; doi:10.3390/s17050951

An Improved BLE Indoor Localization with Kalman-Based Fusion: An Experimental Study

OFFIS—Institut für Informatik, 26121 Oldenburg, Germany
Department of Computer Science, Carl von Ossietzky University of Oldenburg, 26111 Oldenburg, Germany
Institute for Parallel and Distributed Systems, University of Stuttgart, 70569 Stuttgart, Germany
Author to whom correspondence should be addressed.
Academic Editors: Jesús Ureña, Álvaro Hernández Alonso and Juan Jesús García Domínguez
Received: 14 March 2017 / Revised: 20 April 2017 / Accepted: 20 April 2017 / Published: 26 April 2017
View Full-Text   |   Download PDF [1204 KB, uploaded 26 April 2017]   |  


Indoor positioning has grasped great attention in recent years. A number of efforts have been exerted to achieve high positioning accuracy. However, there exists no technology that proves its efficacy in various situations. In this paper, we propose a novel positioning method based on fusing trilateration and dead reckoning. We employ Kalman filtering as a position fusion algorithm. Moreover, we adopt an Android device with Bluetooth Low Energy modules as the communication platform to avoid excessive energy consumption and to improve the stability of the received signal strength. To further improve the positioning accuracy, we take the environmental context information into account while generating the position fixes. Extensive experiments in a testbed are conducted to examine the performance of three approaches: trilateration, dead reckoning and the fusion method. Additionally, the influence of the knowledge of the environmental context is also examined. Finally, our proposed fusion method outperforms both trilateration and dead reckoning in terms of accuracy: experimental results show that the Kalman-based fusion, for our settings, achieves a positioning accuracy of less than one meter. View Full-Text
Keywords: indoor localization; Bluetooth Low Energy; Kalman filter; dead reckoning; trilateration; data fusion indoor localization; Bluetooth Low Energy; Kalman filter; dead reckoning; trilateration; data 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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Röbesaat, J.; Zhang, P.; Abdelaal, M.; Theel, O. An Improved BLE Indoor Localization with Kalman-Based Fusion: An Experimental Study. Sensors 2017, 17, 951.

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