Crowdsensing-based WiFi Indoor Localization using Feed-forward Multilayer Perceptron Regressor

Simran Barnwal, Wei-Jan Peng
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引用次数: 5

Abstract

Most RSS based indoor localization algorithms require the a priori knowledge of location of Access Points, timewise variation of location of user, and use of multiple sensor data. The paper proposes an innovative approach combining the Crowdsensing based wireless indoor localization technology with Artificial Neural Networks, to automatically predict new users location and analyze the effect of device heterogeneity on the RSS localization accuracy, by using cell phone user data. The performance evaluation demonstrates that the trained MLP Regression model can obtain the highest localization accuracy than the probabilistic localization algorithms, without individual model for each device in the fingerprinting database. In contrast with existing systems proposed in the literature, the result shows that our proposed approach efficiently handles very large number of Access Points in 10 times larger indoor spaces.
基于人群感知的WiFi室内定位前馈多层感知器回归
大多数基于RSS的室内定位算法需要先验地了解接入点的位置、用户位置的时间变化以及使用多个传感器数据。本文提出了一种将基于Crowdsensing的无线室内定位技术与人工神经网络相结合的创新方法,利用手机用户数据,自动预测新用户的位置,分析设备异构性对RSS定位精度的影响。性能评估表明,训练后的MLP回归模型在不使用指纹数据库中每个设备的单独模型的情况下,可以获得比概率定位算法更高的定位精度。与文献中提出的现有系统相比,结果表明,我们提出的方法有效地处理了10倍大的室内空间中的大量接入点。
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