User data selection method using received signal strength indicator for semi-supervised learning in 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/12/3/12_2022XBL0168/_pdf
Reference8 articles.
1. [1] S. Aikawa, S. Yamamoto, and M. Morimoto, “WLAN finger print localization using deep learning,” IEEE APCAP, Aug. 2018. DOI: 10.1109/APCAP.2018.8538306
2. [2] L. Xiao, A. Behboodi, and R. Mathar, “A deep learning approach to fingerprinting indoor localization solutions,” International Telecommunication Networks and Applications Conference (ITNAC), Nov. 2017. DOI: 10.1109/ATNAC.2017.8215428
3. [3] M. Ibrahim, M. Torki, and M. El Nainay, “CNN based indoor localization using RSS time-series,” 2018 IEEE Symposium on Computers and Communications (ISCC 2018), pp. 01044-01049, 2018. DOI: 10.1109/ISCC.2018.8538530
4. [4] A. Mittal, S. Tiku, and S. Pasricha, “Adapting convolutional neural networks for indoor localization with smart mobile devices,” GLSVLSI’18, pp. 117-122, Chicago, IL, USA, May 23-25, 2018. DOI: 10.1145/3194554.3194594
5. [5] S. Aikawa, S. Yamamoto, and T. Muramatsu, “CNN localization using AP inverse position estimation,” 2019 IEEE Conference on Antenna Measurements and Applications (CAMA2019), Oct. 23-25, 2019. DOI: 10.1109/CAMA47423.2019.8959663
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