作者:Xue, WX (Xue, Weixing)[ 1 ] ; Qiu, WN (Qiu, Weining)[ 1 ] ; Hua, XH (Hua, Xianghong)[ 1 ] ; Yu, KG (Yu, Kegen)[ 2 ]
IEEE SENSORS JOURNAL
卷: 17 期: 7页: 2224-2230
DOI: 10.1109/JSEN.2017.2660522
出版年: APR 1 2017
摘要
Indoor localization based on Wi-Fi received signal strength indication (RSSI) has the advantage of low cost and easy implementation compared with a range of other localization approaches. However, Wi-Fi RSSI suffers from multipath interference in indoor dynamic environments, resulting in significant errors in RSSI observations. To handle this issue, a number of different methods have been proposed in the literature, including the mean method, Kalman filter algorithm, and the particle filter algorithm. It is observed that these existing methods may not perform sufficiently well in ever-changing dynamic indoor environments. This paper presents an algorithm to improve RSSI observations by using the average of a number of selected maximum RSSI observations. Smoothness index is employed to evaluate the quality of RSSI so as to select an appropriate number of RSSI observations. Experiments were conducted in four rooms and a corridor within an office building and the results demonstrate that the proposed method considerably outperforms the existing algorithms in terms of positioning accuracy, which is defined as the cumulative distribution function of position error.
关键词
作者关键词:Indoor localization; Wi-Fi signal strength; average of maximum RSSI observations;smoothness index; dynamic environment
作者信息
通讯作者地址: Hua, XH (通讯作者)
| Wuhan Univ, Sch Geodesy & Geomat, Wuhan 430079, Peoples R China. |
地址:
| [ 1 ] Wuhan Univ, Sch Geodesy & Geomat, Wuhan 430079, Peoples R China |
| [ 2 ] Wuhan Univ, Sch Geodesy & Geomat, Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Peoples R China |
电子邮件地址:[email protected]; [email protected]; [email protected];[email protected]
基金资助致谢
基金资助机构 | 授权号 |
National Natural Science Foundation of China | 41374011 41174010 |
出版商
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
类别/分类
研究方向:Engineering; Instruments & Instrumentation; Physics
Web of Science类别:Engineering, Electrical & Electronic; Instruments & Instrumentation; Physics, Applied
文献信息
文献类型:Article
语种:English
入藏号: WOS:000397600000032
ISSN: 1530-437X
eISSN: 1558-1748
期刊信息
· Impact Factor (影响因子):1.889
