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

Sensors

Peilin Zhang¹  Oliver Theel¹  Mohamed Abdelaal²  Jenny Röbesaat³ 

[1] 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;OFFIS—Institut für Informatik, 26121 Oldenburg, Germany;

关键词: indoor localization;    Bluetooth Low Energy;    Kalman filter;    dead reckoning;    trilateration;    data fusion;   

DOI  :  10.3390/s17050951

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown