基于条件的室内定位分类器集成

Dip Ghosh, Priya Roy, C. Chowdhury, S. Bandyopadhyay
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引用次数: 13

摘要

基于Wi-Fi信号的射频指纹识别是一种流行的室内定位方法。最近有一些作品探讨了机器学习技术在这个问题上的适用性。然而,准确找到位置的挑战性任务依赖于指纹识别的预先努力。另一个挑战是,信号强度的距离灵敏度取决于与接入点的接近程度。设备的异质性给挑战增加了新的维度。现有的解决方案主要针对在一组考虑的条件下进行指纹识别,以便在相似的实验设置下获得训练和测试数据。本文设计了一套基于条件的分类器,用于室内定位,处理设备异质性、时间异质性和上下文异质性(门窗打开/关闭、附近是否有其他用户)。我们已经创建了一个室内定位数据集,其中的数据收集在上述维度。当训练数据集和测试数据集在相似的环境条件下使用相同的设备时,平均分类准确率在72.2% ~ 92.6%之间。但对于包含每个条件的示例的验证集,当使用对应于特定条件的单个训练集时,分类器的平均最大准确率为74.8%。为了避免这些条件依赖,我们使用了特定条件的k近邻分类器集合,使我们能够以96%的准确率预测位置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An ensemble of condition based classifiers for indoor localization
Radio frequency fingerprinting, based on Wi-Fi signals is a popular approach for indoor localization. Recently a few works have explored applicability of machine learning techniques to this problem. However, the challenging task of accurately finding the position depends on prior efforts of fingerprinting. Another challenge is that, distance sensitivity of signal strength depends on proximity to the access point. Heterogeneity of devices adds new dimension to the challenge. Existing solutions mostly aim at fingerprinting under the set of conditions considered so that training and test data can be taken under similar experimental setup. In this paper, an ensemble of condition based classifiers are designed for indoor localization that handles device heterogeneity, temporal heterogeneity and context heterogeneity (door and window open/close, presence/absence of other users in vicinity). We have created an indoor localization data set where data is collected in above mentioned dimensions. When the training and test data sets are taken under similar environmental conditions with same devices, the average classification accuracy ranges between 72.2% to 92.6%. But for a validation set which contains examples for every conditions, the classifiers achieve an average maximum accuracy of 74.8% when individual training set corresponding to specific conditions are used. To avoid these conditional dependencies we have used an ensemble of condition specific K-nearest neighbour classifiers which enables us to predict the location with 96% accuracy.
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