Journal
MOBILE NETWORKS & APPLICATIONS
Volume 14, Issue 5, Pages 677-691Publisher
SPRINGER
DOI: 10.1007/s11036-008-0139-0
Keywords
localization systems; Wi-Fi network; unsupervised learning; Wi-Fi device variance
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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.
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