Bayesian Fusion for Indoor Positioning Using Bluetooth Fingerprints |
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Authors: | Liang Chen Ling Pei Heidi Kuusniemi Yuwei Chen Tuomo Kröger Ruizhi Chen |
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Affiliation: | 1. Department of Positioning and Navigation, Finnish Geodetic Institute, Kirkkonummi, Finland 2. Geodeetinrinne 2, P.O. Box 15, 02431, Masala, Finland
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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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