Bayesian Fusion for Indoor Positioning Using Bluetooth Fingerprints

Authors:
Liang Chen;Ling Pei;Heidi Kuusniemi;Yuwei Chen;Tuomo Kröger;Ruizhi Chen
Affiliations:
Department of Positioning and Navigation, Finnish Geodetic Institute, Kirkkonummi, Finland and , Masala, Finland 02431;Department of Positioning and Navigation, Finnish Geodetic Institute, Kirkkonummi, Finland and , Masala, Finland 02431;Department of Positioning and Navigation, Finnish Geodetic Institute, Kirkkonummi, Finland and , Masala, Finland 02431;Department of Positioning and Navigation, Finnish Geodetic Institute, Kirkkonummi, Finland and , Masala, Finland 02431;Department of Positioning and Navigation, Finnish Geodetic Institute, Kirkkonummi, Finland and , Masala, Finland 02431;Department of Positioning and Navigation, Finnish Geodetic Institute, Kirkkonummi, Finland and , Masala, Finland 02431
Venue:
Wireless Personal Communications: An International Journal
Year:
2013

Citing 7
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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.