The Indoor Localization System Based on Federated Learning and RSS Using UWB-OFDM
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-35251-5_14
Reference14 articles.
1. Khatab, Z.E., Hajihoseini, A., Ghorashi, S.A.: A finge print method for indoor localization using autoencoder based deep extreme learning machine. IEEE Sens. Lett. 2(1), 1–4 (2017)
2. Lim, S., Jung, J., Kim, S.-C., et al.: Deep neural network-based in-vehicle people localization using ultra-wideband radar. IEEE Access 8, 96606–96612 (2020)
3. Ciftler, B.S., Albaseer, A., Lasla, N., et al.: Federated learning for rss fingerprint-based localization: a privacy preserving crowdsourcing method. In: 2020 International Wireless Communications and Mobile Computing (IWCMC), pp. 2112–2117. IEEE (2020)
4. Dou, F., Lu, J., Xu, T., et al.: A bisection reinforcement learning approach to 3-D indoor localization. IEEE Internet Things J. 8(8), 6519–6535 (2020)
5. Xue, J., Liu, J., Sheng, M., et al.: A WiFi fingerprint based high-adaptability indoor localization via machine learning. China Commun. 17(7), 247–259 (2020)
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