Non-line-of-Sight Recognition Algorithm Based on Deep Learning
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-0416-7_66
Reference8 articles.
1. Wu, S., Zhang, S., Huang, D.: A TOA-based localization algorithm with simultaneous NLOS mitigation and synchronization error elimination. IEEE Sens. Lett. 3(3), 1–4 (2019)
2. Joung, J., Jung, S., Chung, S., Jeong, E-R.: CNN-based Tx-Rx distance estimation for UWB system localisation. IET 55(17), 938–940 (2019)
3. Zafari, F., Gkelias, A., Leung, K.K.: A survey of indoor localization systems and technologies. IEEE Commun. Surv. Tutorials 21(3), 2568–2599 (2019)
4. Lovón-Melgarejo, J., Castillo-Cara, M., Huarcaya-Canal, O., Orozco-Barbosa, L., García-Varea, I.: Comparative study of supervised learning and metaheuristic algorithms for the development of bluetooth based indoor localization mechanisms. IEEE Access 7, pp. 26123–26135 (2019)
5. Liu, Y-M., Sheu, J-P., Kuo, Y-C.: Deep learning for ultra-wideband indoor positioning. In: 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), pp. 1260–1266 (2021). https://doi.org/10.1109/PIMRC50174.2021.9569615
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