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- W2187691105 abstract "The widespread availability of wireless networks has created an increased interest in harnessing them for other purposes, such as localizing mobile devices. Our particular goal was to be able to use these techniques as well as others in order to better predict the location of a smart phone inside a building. The current state of the art techniques use Gaussian Process Latent Variable Models to solve this problem. However, existing implementations of this technique require ground traces (wi data collected sequentially) and are limited to signalrich environments. Due to the constraints of using phones for this, we were restricted to non sequential data. Since this eleminates the possbility of using traditional GP-LVM techniques, we created a locally weighted regression based machine learning algorithm for geographical localization of a mobile device in an indoor environment based on WiFi signal strength data. Our contribution is a novel approach to generating adequate likelihood models based on locally weighted sums of Gaussian probability densities. The results show that our algorithm is eectively deduces the original oor plan of the environment and presents a more accurate estimate of the device’s location." @default.
- W2187691105 created "2016-06-24" @default.
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- W2187691105 date "2011-01-01" @default.
- W2187691105 modified "2023-09-23" @default.
- W2187691105 title "Increasing Indoor Localization Accuracy Using Wi Signals" @default.
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