Bayesian Fusion for Indoor Positioning Using Bluetooth Fingerprints

被引：0
|
作者：
Liang Chen
Ling Pei
Heidi Kuusniemi
Yuwei Chen
Tuomo Kröger
Ruizhi Chen
机构：
[1] Finnish Geodetic Institute,Department of Positioning and Navigation
来源：
Wireless Personal Communications | 2013年 / 70卷
关键词：
Bayesian fusion; Indoor positioning; Fingerprints; Bluetooth; Motion model;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
引用
收藏
页码：1735 / 1745
页数：10
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