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• W4296099006 abstract "Indoor localization is an important technology for providing various location-based services to smartphones. Among the various indoor localization technologies, pedestrian dead reckoning using inertial measurement units is a simple and highly practical solution for indoor localization. In this study, we propose a smartphone-based indoor localization system using pedestrian dead reckoning. To create a deep learning model for estimating the moving speed, accelerometer data and GPS values were used as input data and data labels, respectively. This is a practical solution compared with conventional indoor localization mechanisms using deep learning. We improved the positioning accuracy via data preprocessing, data augmentation, deep learning modeling, and correction of heading direction. In a horseshoe-shaped indoor building of 240 m in length, the experimental results show a distance error of approximately 3 to 5 m." @default.
• W4296099006 created "2022-09-17" @default.
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• W4296099006 date "2022-09-07" @default.
• W4296099006 modified "2023-10-14" @default.
• W4296099006 title "Practical and Accurate Indoor Localization System Using Deep Learning" @default.
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• W4296099006 doi "https://doi.org/10.3390/s22186764" @default.
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