Indoor positioning algorithm based on improved convolutional neural network-Reference-Cited by-同舟云学术

Indoor positioning algorithm based on improved convolutional neural network

Published:2021-09-12 Issue: Volume: Page:
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Zhou Taoyun,Ku Junhua,Lian Baowang,Zhang Yi

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-021-06112-5.pdf

Reference27 articles.

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2. Zhao Y, Wong WC, Feng T et al (2020) Efficient and scalable calibration-free indoor positioning using crowdsourced data. IEEE Intern Th J 7(1):160–175

3. Ardiansyah A, Nugraha GD, Han H (2019) A decision tree-based NLOS detection method for the UWB indoor location tracking accuracy improvement. Int J Commun Syst 32(4):e3997

4. Niu L, Saiki S, Matsumoto S (2016) WIF4InL: Web-based integration framework for Indoor location. Int J Pervasive Comput Commun 12(1):49–65

5. Zhu L, Shen Y (2017) Iterative solution of WiFi indoor distance intersection location. J Geomat 42(4):58–60

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