Unsupervised Learning for Solving RSS Hardware Variance Problem in WiFi Localization

被引：125

作者：

Tsui, Arvin Wen ^{[2
]}

Chuang, Yu-Hsiang ^{[2
]}

Chu, Hao-Hua ^{[1
]}

机构：

[1] Natl Taiwan Univ, Grad Inst Networking & Multimedia, Dept Comp Sci & Informat Engn, Taipei 106, Taiwan

[2] Ind Technol Res Inst, Informat & Commun Res Labs, Hsinchu 310, Taiwan

来源：

MOBILE NETWORKS & APPLICATIONS | 2009年 / 14卷 / 05期

关键词：

localization systems; Wi-Fi network; unsupervised learning; Wi-Fi device variance;

D O I：

10.1007/s11036-008-0139-0

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Hardware variance can significantly degrade the positional accuracy of RSS-based WiFi localization systems. Although manual adjustment can reduce positional error, this solution is not scalable as the number of new WiFi devices increases. We propose an unsupervised learning method to automatically solve the hardware variance problem in WiFi localization. This method was designed and implemented in a working WiFi positioning system and evaluated using different WiFi devices with diverse RSS signal patterns. Experimental results demonstrate that the proposed learning method improves positional accuracy within 100 s of learning time.

引用

页码：677 / 691

页数：15