Indoor-Location Classification Using RF Signatures

被引：0
|
作者：
Chawathe, Sudarshan S. [1 ]
机构：
[1] Univ Maine, Sch Comp & Informat Sci, Orono, ME 04469 USA
来源：
2019 IEEE 18TH INTERNATIONAL SYMPOSIUM ON NETWORK COMPUTING AND APPLICATIONS (NCA) | 2019年
基金：
美国国家科学基金会;
关键词：
Indoor Localization; Radio-Frequency Signals; Classification; Machine Learning;
D O I：
10.1109/nca.2019.8935028
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Indoor locations are classified using spatial signatures of RF signals that are obtained using a measurement grid with a spacing of approximately a wavelength. The classification method is evaluated using a publicly available dataset of detailed signal measurements in a real environment. The experimental results suggest not only that high accuracy is achievable using much simpler signatures than those in prior work but also that this accuracy is maintained as the grid is significantly coarsened.
引用
收藏
页码：331 / 334
页数：4
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