Unsupervised Learning for Solving RSS Hardware Variance Problem in WiFi Localization
Unsupervised Learning for Solving RSS Hardware Variance Problem in WiFi Localization
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DOI：
10.1007/s11036-008-0139-0
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发表时间：
2009-10
影响因子：
3.8
通讯作者：
A. W. Tsui;Yu-Hsiang Chuang;Hao-Hua Chu
中科院分区：
文献类型：
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作者：
A. W. Tsui;Yu-Hsiang Chuang;Hao-Hua Chu
硬件差异会显着降低基于 RSS 的 WiFi 定位系统的定位精度。虽然手动调整可以减少位置误差，但随着新 WiFi 设备数量的增加，该解决方案不可扩展。我们提出了一种无监督学习方法来自动解决 WiFi 定位中的硬件方差问题。该方法在工作 WiFi 定位系统中设计和实现，并使用具有不同 RSS 信号模式的不同 WiFi 设备进行评估。实验结果表明，所提出的学习方法在 100 秒的学习时间内提高了位置精度。
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.