A study on outdoor localization method by recurrent deep learning based on time series of received signal strength from low power wireless tag-Reference-Cited by-同舟云学术

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A study on outdoor localization method by recurrent deep learning based on time series of received signal strength from low power wireless tag

Published:2019 Issue:12 Volume:8 Page:572-577
ISSN:2187-0136
Container-title:IEICE Communications Express
language:en
Short-container-title:IEICE ComEX

Author:

Yamanishi Takahiro¹,Jikyo Takuto¹,Kamada Tomio²^ORCID,Nishide Ryo³^ORCID,Ohta Chikara¹^ORCID,Oyama Kenji⁴^ORCID,Ohkawa Takenao²

Affiliation:

1. Graduate School of Science, Technology and Innovation, Kobe University

2. Graduate School of System Informatics, Kobe University

3. The Center for Data Science Education and Research, Shiga University

4. Graduate School of Agricultural Science, Kobe University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

General Medicine

Link

https://www.jstage.jst.go.jp/article/comex/8/12/8_2019GCL0065/_pdf

Reference11 articles.

1. [1] Y. Yamauchi, R. Nishide, Y. Takaki, C. Ohta, K. Oyama, and T. Ohkawa, “Cattle community extraction using the interactions based on synchronous behavior,” Proc. the Ninth International Symposium on Information and Communication Technology, pp. 227–234, Dec. 2018. DOI:10.1145/3287921.3287941

2. [2] NTT TechnoCross Corporation, “Active RFID product mobicollet,” https://www.ntt-tx.co.jp/products/mobicollet/, accessed July 9, 2019.

3. [3] Q. D. Vo and P. De, “A survey of fingerprint-based outdoor localization,” IEEE Commun. Surveys Tuts., vol. 18, no. 1, pp. 491–506, First-quarter 2016. DOI:10.1109/COMST.2015.2448632

4. [4] W. Zhang, K. Liu, W. Zhang, Y. Zhang, and J. Gu, “Deep neural networks for wireless location in indoor and outdoor environments,” Neurocomputing, vol. 194, pp. 279–287, 2016. DOI:10.1016/j.neucom.2016.02.055

5. [5] H. Lu, G. Xingli, L. Shuang, Z. Heng, L. Yaning, and Z. Ruihui, “Indoor positioning technology based on deep neural networks,” Proc. Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS), Mar. 2018. DOI:10.1109/UPINLBS.2018.8559721

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