带有WiFi指纹的室内定位框架

Rajan Khullar, Z. Dong
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引用次数: 15

摘要

通过WiFi指纹进行室内定位,需要大量细粒度的数据样本。本研究提出了一种数据采集和室内定位框架,该框架收集了大都市高层建筑中众包WiFi接收信号强度数据,并通过WiFi指纹识别预测位置。该框架由一个服务器和一个Android应用程序组成,并于2016年12月在纽约理工学院进行了两周的数据收集测试。通过线性支持向量机对数据集进行预处理和分析,测试位置预测的精度。比较了各种特征选择方案的位置预测精度。我们表明,一小部分特征足以提供高的位置预测精度。与仅使用空间特征相比,考虑时间特征时,平均位置预测精度从83%提高到100%。
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Indoor localization framework with WiFi fingerprinting
Indoor localization through WiFi fingerprinting requires a large number of fine-grained data samples. This study presents a data acquisition and indoor localization framework that collects crowd-sourced WiFi received signal strength data in a metropolitan high-rise building and predicts location through WiFi fingerprinting. The framework consists of a server and an Android application and was tested at NYIT for data collection for two weeks in December 2016. The dataset was preprocessed and analyzed through linear support vector machine to test location prediction accuracy. Various feature selection schemes were compared for their location prediction accuracy. We show that a small subset of features suffices to provide high location prediction accuracy. The average location prediction accuracy increases from 83% to 100% when time features are considered comparing to using only spatial features.
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