User data selection scheme to reduce database update errors for fingerprint localization
Author:
Affiliation:
1. Graduate School of Engineering, University of Hyogo
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Subject
General Medicine
Link
https://www.jstage.jst.go.jp/article/comex/10/6/10_2021XBL0051/_pdf
Reference8 articles.
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2. [2] S. Aikawa, Y. Shigematsu, R. Baba, and T. Kominami, “Localization estimation system using wireless LAN,” General Assembly and Scientific Symposium of the International Union of Radio Science, Beijing, Aug. 2014. DOI: 10.1109/URSIGASS.2014.6929349
3. [3] C. Song and J. Wang, “WLAN fingerprint indoor positioning strategy based on implicit crowdsourcing and semi-supervised learning,” ISPRS Int. J. Geo-Inf., vol. 6, no. 11, 356, 2017. DOI: 10.3390/ijgi6110356
4. [4] S. He, W. Lin, and S.-H.G. Chan, “Indoor localization and automatic fingerprint update with altered AP signals,” IEEE Trans. Mobile Comput., vol. 16, no. 7, pp. 1897-1910, July 2017. DOI: 10.1109/TMC.2016.2608946
5. [5] X. Liu, J. Cen, Y. Zhan, and C. Tang, “An adaptive fingerprint database updating method for room localization,” IEEE Access, vol. 7, pp. 42626-42638, 2019. DOI: 10.1109/access.2019.2908000
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2. User data selection method using received signal strength indicator for semi-supervised learning in fingerprint localization;IEICE Communications Express;2023-03-01
3. Enhanced Indoor Localization Technique Based on Point Cloud Conversion Image Matching;Sensors and Materials;2023-01-31
4. User data selection method using received signal strength indicator for semi- supervised learning in fingerprint localization;IEICE COMMUN EXPRESS;2022
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