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NTIS 바로가기Applied sciences, v.12 no.13, 2022년, pp.6484 -
Zhao, Yuxuan (The School of Mechanical Engineering, NanJing University of Science and Technology, Nanjing 210094, China) , Wang, Manyi (The School of Mechanical Engineering, NanJing University of Science and Technology, Nanjing 210094, China)
A localization system is one of the basic requirements for coal mines. Ultra-wideband (UWB), as a technology with broad application prospects, is considered to have great potential in the absence of satellite signals, especially in the underground mine environment, as it can provide rescue assistanc...
10.1109/IEMCON51383.2020.9284937 Cheng, L., Zhao, A., Wang, K., Li, H., and Chang, R. (2020, January 4-7). Activity Recognition and Localization based on UWB Indoor Positioning System and Machine Learning. Proceedings of the 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada.
10.17654/EC018060871 Li, B., Zhao, K., Saydam, S., Rizos, C., Wang, J., and Wang, Q. (2018). Third Generation Positioning System for Underground Mine Environments: An Update on Progress, Far East Journal of Electronics and Communications (FJEC).
Aca UWB-based sensor networks for localization in mining environments Ad Hoc Netw. 2009 10.1016/j.adhoc.2008.08.007 7 987
Capraro Human Real Time Localization System in Underground Mines using UWB IEEE Lat. Am. Trans. 2020 10.1109/TLA.2020.9085295 18 392
10.1371/journal.pone.0220471 Zheng, X., Wang, B., and Zhao, J. (2019). High-precision positioning of mine personnel based on wireless pulse technology. PLoS ONE, 14.
10.21203/rs.3.rs-127354/v1 Bao, J., Wang, H., and Research on Key Technologies of High-Precision Location Based Service System for Intelligent Mines (2022, May 29). ResearchGate.PrePrint. Version 1. Posted 17 December 2020., Available online: https://www.sciencegate.app/app/document/download#10.21203/rs.3.rs-127354/v1.
Ming A Novel Ultra-Wideband Hybrid Localization Scheme in Coal Mine J. Commun. 2015 10 889
Liu UWB LOS/NLOS identification in multiple indoor environments using deep learning methods Phys. Commun. 2022 10.1016/j.phycom.2022.101695 52 101695
10.1109/IPIN51156.2021.9662545 Altstidl, T., Kram, S., Herrmann, O., Stahlke, M., Feigl, T., and Mutschler, C. (December, January 29). Accuracy-Aware Compression of Channel Impulse Responses using Deep Learning. Proceedings of the 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Lloret de Mar, Spain.
Song A fast imbalanced binary classification approach to NLOS identification in UWB positioning Math. Probl. Eng. 2018 10.1155/2018/1580147 2018 1580147
Fan, R., and Du, X. (2022). NLOS Error Mitigation Using Weighted Least Squares and Kalman Filter in UWB Positioning. arXiv.
10.1109/ICRA.2017.7989660 Liu, R., Yuen, C., Do, T.N., Jiao, D., Liu, X., and Tan, U.X. (June, January 29). Cooperative Relative Positioning of Mobile Users by Fusing IMU Inertial and UWB Ranging Information. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.
Savic Measurement Analysis and Channel Modeling for TOA-Based Ranging in Tunnels IEEE Trans. Wirel. Commun. 2015 10.1109/TWC.2014.2350493 14 456
Jiang UWB NLOS/LOS Classification Using Deep Learning Method IEEE Commun. Lett. 2020 10.1109/LCOMM.2020.2999904 24 2226
10.3390/app10113980 Sang, C.L., Steinhagen, B., Homburg, J.D., Adams, M., and Rückert, U. (2020). Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods. Appl. Sci., 10.
10.1109/ICAIIC54071.2022.9722667 Tran, D.H., Chung, B., and Jang, Y.M. (2022, January 21-24). GAN-based Data Augmentation for UWB NLOS Identification Using Machine Learning. Proceedings of the 2022 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Jeju Island, Korea.
Kaemarungsi, K., and Krishnamurthy, P. (2004, January 7-11). Modeling of indoor positioning systems based on location fingerprinting. Proceedings of the IEEE INFOCOM, Hong Kong, China.
Trees, H.V., and Bell, K. (2002, January 19-22). On Geolocation Accuracy with Prior Information in Nonlineofsight Environment. Proceedings of the EEE 56th Vehicular Technology Conference, Helsinki, Finland.
10.1109/ICAIIC51459.2021.9415277 Poulose, A., and Han, D.S. (2021, January 20-23). Feature-Based Deep LSTM Network for Indoor Localization Using UWB Measurements. Proceedings of the 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Jeju Island, Korea.
Djosic Multi-algorithm UWB-based localization method for mixed LOS/NLOS environments Comput. Commun. 2022 10.1016/j.comcom.2021.10.031 181 365
Creswell Generative Adversarial Networks: An Overview IEEE Signal Process. Mag. 2018 10.1109/MSP.2017.2765202 35 53
Rosenblatt, F. (1957). The Perceptron-A Perceiving and Recognizing Automaton, Cornell Aeronautical Lab.
Alavi, B. (2006). Distance Measurement Error Modeling for Time-of-Arrival-Based Indoor Geolocation. [Ph.D. Thesis, Worcester Polytechnic Institute].
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