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Sensor-Aided Learning for Wi-Fi Positioning with Beacon Channel State Information

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Document pages: 13 pages

Abstract: Because each indoor site has its own radio propagation characteristics, asite survey process is essential to optimize a Wi-Fi ranging strategy forrange-based positioning solutions. This paper studies an unsupervised learningtechnique that autonomously investigates the characteristics of the surroundingenvironment using sensor data accumulated while users use a positioningapplication. Using the collected sensor data, the device trajectory can beregenerated, and a Wi-Fi ranging module is trained to make the shape of theestimated trajectory using Wi-Fi similar to that obtained from sensors. In thisprocess, the ranging module learns the way to identify the channel conditionsfrom each Wi-Fi access point (AP) and produce ranging results accordingly.Furthermore, we collect the channel state information (CSI) from beacon framesand evaluate the benefit of using CSI in addition to received signal strength(RSS) measurements. When CSI is available, the ranging module can identify morediverse channel conditions from each AP, and thus more precise positioningresults can be achieved. The effectiveness of the proposed learning techniqueis verified using a real-time positioning application implemented on a PCplatform.

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