Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/s11036-008-0139-0 | MONET |
Keywords | Field | DocType |
localization systems,Wi-Fi network,unsupervised learning,Wi-Fi device variance | Computer science,Real-time computing,Unsupervised learning,Computer hardware,RSS,Positioning system,Scalability | Journal |
Volume | Issue | ISSN |
14 | 5 | 1383-469X |
Citations | PageRank | References |
78 | 3.35 | 20 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arvin Wen Tsui Tsui | 1 | 200 | 10.52 |
| Yu-Hsiang Chuang | 2 | 78 | 3.35 |
| Hao-Hua Chu | 3 | 1168 | 98.54 |