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Subway Stop/Departure Detection using a Magnetic Sensor of the Smartphone

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Published:27 August 2018Publication History

ABSTRACT

Wi-Fi-based indoor positioning has an advantage that it can be utilized without additional equipment, unlike other methods requiring additional infrastructure. Especially, Wi-Fi fingerprint-based positioning methods using signal specificity are used in various indoor location services because they provide a relatively high indoor location recognition accuracy. Although Wi-Fi-based indoor positioning is superior to other technologies in terms of ease of implementation and positioning accuracy, stable positioning is not guaranteed due to unstable Wi-Fi signal characteristics in the various real environment. Recently, as a part of efforts to supplement the Wi-Fi-based positioning system, researches on positioning method using a geomagnetic field are being spotlighted. This can be considered as a sign that users who demand location-based service has increased. Positioning method using a geomagnetic field can be applied to various areas. Geomagnetic field-based positioning can provide more convenient service to users through fusion with facilities, for instance, public transportation which is closely related to users' real life. In the case of a subway, if an accurate train time schedule is provided, many users will get better services. In this study, we developed a systematic and an efficient geomagnetic based positioning method. This method provides stop and running conditions of trains for setting up more accurate and reliable system. Through this system, we tried to offer more stable indoor positioning service and to make the location-based services widespread.

References

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  1. Subway Stop/Departure Detection using a Magnetic Sensor of the Smartphone

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    • Published in

      cover image ACM Other conferences
      ICVISP 2018: Proceedings of the 2nd International Conference on Vision, Image and Signal Processing
      August 2018
      402 pages
      ISBN:9781450365291
      DOI:10.1145/3271553

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      New York, NY, United States

      Publication History

      • Published: 27 August 2018

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      Overall Acceptance Rate186of424submissions,44%

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