Indoor Multi-Floor Localization Based on Location Calibration Combined with Magnetic Maps and Deep Learning Models 


Vol. 49,  No. 10, pp. 1354-1357, Oct.  2024
10.7840/kics.2024.49.10.1354


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  Abstract

In this paper, we propose a method to address the issues in smartphone-based pedestrian dead reckoning (PDR) by combining magnetic maps and deep learning models to calibrate the current location using calibration nodes when floor transitions are detected, thereby improving localization accuracy. In addition, we verified the localization performance of the proposed method by connecting the server and smartphone via socket communication, allowing real-time localization tracking.

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[IEEE Style]

J. Kim and Y. Shin, "Indoor Multi-Floor Localization Based on Location Calibration Combined with Magnetic Maps and Deep Learning Models," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 10, pp. 1354-1357, 2024. DOI: 10.7840/kics.2024.49.10.1354.

[ACM Style]

Jin-Woo Kim and Yoan Shin. 2024. Indoor Multi-Floor Localization Based on Location Calibration Combined with Magnetic Maps and Deep Learning Models. The Journal of Korean Institute of Communications and Information Sciences, 49, 10, (2024), 1354-1357. DOI: 10.7840/kics.2024.49.10.1354.

[KICS Style]

Jin-Woo Kim and Yoan Shin, "Indoor Multi-Floor Localization Based on Location Calibration Combined with Magnetic Maps and Deep Learning Models," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 10, pp. 1354-1357, 10. 2024. (https://doi.org/10.7840/kics.2024.49.10.1354)
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