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Sensors 2015, 15(12), 31464-31481; doi:10.3390/s151229867

Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device

Department of Electrical and Computer Engineering, Oakland University, 2200 N Squirrel Road, Rochester, MI 48309, USA
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Academic Editor: Leonhard M. Reindl
Received: 16 September 2015 / Revised: 8 December 2015 / Accepted: 9 December 2015 / Published: 14 December 2015
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Abstract

Nowadays, smart mobile devices include more and more sensors on board, such as motion sensors (accelerometer, gyroscope, magnetometer), wireless signal strength indicators (WiFi, Bluetooth, Zigbee), and visual sensors (LiDAR, camera). People have developed various indoor positioning techniques based on these sensors. In this paper, the probabilistic fusion of multiple sensors is investigated in a hidden Markov model (HMM) framework for mobile-device user-positioning. We propose a graph structure to store the model constructed by multiple sensors during the offline training phase, and a multimodal particle filter to seamlessly fuse the information during the online tracking phase. Based on our algorithm, we develop an indoor positioning system on the iOS platform. The experiments carried out in a typical indoor environment have shown promising results for our proposed algorithm and system design. View Full-Text
Keywords: indoor positioning; HMM framework; graph structure; multimodal particle filter; sensor fusion; iOS platform indoor positioning; HMM framework; graph structure; multimodal particle filter; sensor fusion; iOS platform
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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

He, X.; Aloi, D.N.; Li, J. Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device. Sensors 2015, 15, 31464-31481.

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