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Bayesian Fusion for Indoor Positioning Using Bluetooth Fingerprints

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Abstract

This paper studies the use of received signal strength indicators (RSSI) applied to fingerprinting method in a Bluetooth network for indoor positioning. A Bayesian fusion (BF) method is proposed to combine the statistical information from the RSSI measurements and the prior information from a motion model. Indoor field tests are carried out to verify the effectiveness of the method. Test results show that the proposed BF algorithm achieves a horizontal positioning accuracy of about 4.7 m on the average, which is about 6 and 7 % improvement when compared with Bayesian static estimation and a point Kalman filter method, respectively.

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Authors and Affiliations

  1. Department of Positioning and Navigation, Finnish Geodetic Institute, Kirkkonummi, Finland

    Liang Chen, Ling Pei, Heidi Kuusniemi, Yuwei Chen, Tuomo Kröger & Ruizhi Chen

  2. Geodeetinrinne 2, P.O. Box 15, 02431, Masala, Finland

    Liang Chen, Ling Pei, Heidi Kuusniemi, Yuwei Chen, Tuomo Kröger & Ruizhi Chen

Authors
  1. Liang Chen
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  2. Ling Pei
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  3. Heidi Kuusniemi
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  4. Yuwei Chen
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  5. Tuomo Kröger
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  6. Ruizhi Chen
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Correspondence to Liang Chen.

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Chen, L., Pei, L., Kuusniemi, H. et al. Bayesian Fusion for Indoor Positioning Using Bluetooth Fingerprints. Wireless Pers Commun 70, 1735–1745 (2013). https://doi.org/10.1007/s11277-012-0777-1

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