基于IEEE 802.11信号的室内符号用户定位的多观察者实例学习方法

T. Mantoro, A. Azizan, Salahudin Khairuzzaman, M. A. Ayu
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引用次数: 6

摘要

在室内环境中,Wi-Fi的信号强度(SS)和信号质量(SQ)在确定符号用户位置时波动很大。本文探讨了几种不同的训练数据集对确定用户符号位置的影响。实现和实验是使用离线的基于实例的机器学习方法来过滤所有的训练数据集。训练数据集采用“多观察者”k-最近邻方法进行优化。采用该方法,比较了4种不同的观测值,分别为8M SQ和SS观测值、8M SS观测值、1M SQ和SS观测值以及1M SS观测值。然后,通过找到最近的10个(k=10)用户位置中的大多数来继续确定用户位置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-observers instance-based learning approach for indoor symbolic user location determination using IEEE 802.11 signals
Wi-Fi's signals strength (SS), and signal quality (SQ) are found to greatly fluctuate in determination of symbolic user location in an indoor environment. This paper explores the influence of several different training data-sets in determining user's symbolic location. The implementation and experimentation were done using off-line instance-based machine learning methods to filter all of the training data-sets. The training data-sets were optimized using “multiple observers” k-Nearest Neighbor approach. Using this method, four different observations were compared, which were 8M observations of SQ and SS , 8M SS observers, 1M SQ and SS and the last was 1M SS observers. Then, a continuing determination of the user location was performed by finding the majority of the nearest ten (k=10) user locations.
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