Unsupervised Learning for Solving RSS Hardware Variance Problem in WiFi Localization
Resource
Springer/ACM Mobile Networks and Applications, In press
Journal
Mobile Networks and Applications
Pages
677-691
Date Issued
2009
Date
2009
Author(s)
Tsui, Arvin
Chuang, Yu-Hsiang
Chu, Hao-hua
Abstract
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. © 2008 Springer Science+Business Media, LLC.
Type
journal article
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