Poster Abstract: Extreme Learning Machine for Wireless Indoor Localization

被引：0
|
作者：
Xiao, Wendong [1 ]
Liu, Peidong [1 ]
Soh, Wee-Seng [1 ]
Jin, Yunye [1 ]
机构：
[1] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing, Peoples R China
来源：
IPSN'12: PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING IN SENSOR NETWORKS | 2012年
关键词：
Indoor localization; fingerprinting; neural network; ELM;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Due to the widespread deployment and low cost, WLAN has drawn much attention for indoor localization. In this poster, an efficient indoor localization algorithm, which utilizes the WLAN received signal strength from each Access Point (AP), has been proposed. The algorithm is based on the Extreme Learning Machine (ELM), a Single layer Feed-forward neural Network (SLFN). It is competitive fast in offline learning and online localization. Also, compared with existing fingerprinting approach, it does not need the fingerprinting database in the online phase, which can substantially reduce the required storage space of the terminal devices.
引用
收藏
页码：101 / 102
页数：2
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