Unsupervised Learning for Solving RSS Hardware Variance Problem in WiFi Localization

Authors:
Arvin Wen Tsui;Yu-Hsiang Chuang;Hao-Hua Chu
Affiliations:
Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei, Taiwan 106 and Information and Communication Research Laboratories, Industrial Technology Research Institute, C ...;Information and Communication Research Laboratories, Industrial Technology Research Institute, Chutung, Taiwan 310;Graduate Institute of Networking and Multimedia, Dept. of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan 106
Venue:
Mobile Networks and Applications
Year:
2009

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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.