$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

The LOS/NLOS Classification Method Based on Deep Learning for the UWB Localization System in Coal Mines 원문보기

Applied 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)

Abstract AI-Helper 아이콘AI-Helper

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...

참고문헌 (28)

  1. 10.1109/VETECS.2011.5956174 Choliz, J., Eguizabal, M., Hernandezsolana, A., and Valdovinos, A. (2011, January 15-18). Comparison of Algorithms for UWB Indoor Location and Tracking Systems. Proceedings of the 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring), Budapest, Hungary. 

  2. 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. 

  3. 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). 

  4. Aca UWB-based sensor networks for localization in mining environments Ad Hoc Netw. 2009 10.1016/j.adhoc.2008.08.007 7 987 

  5. Capraro Human Real Time Localization System in Underground Mines using UWB IEEE Lat. Am. Trans. 2020 10.1109/TLA.2020.9085295 18 392 

  6. 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. 

  7. 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. 

  8. Ming A Novel Ultra-Wideband Hybrid Localization Scheme in Coal Mine J. Commun. 2015 10 889 

  9. 10.1109/ICTC52510.2021.9620795 Yoon, J., Kim, H., Seo, D., and Nam, H. (2021, January 19-21). Performance Comparison of NLOS Detection Methods in UWB. Proceedings of the 2021 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea. 

  10. Liu UWB LOS/NLOS identification in multiple indoor environments using deep learning methods Phys. Commun. 2022 10.1016/j.phycom.2022.101695 52 101695 

  11. 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. 

  12. Song A fast imbalanced binary classification approach to NLOS identification in UWB positioning Math. Probl. Eng. 2018 10.1155/2018/1580147 2018 1580147 

  13. Fan, R., and Du, X. (2022). NLOS Error Mitigation Using Weighted Least Squares and Kalman Filter in UWB Positioning. arXiv. 

  14. 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. 

  15. Savic Measurement Analysis and Channel Modeling for TOA-Based Ranging in Tunnels IEEE Trans. Wirel. Commun. 2015 10.1109/TWC.2014.2350493 14 456 

  16. Jiang UWB NLOS/LOS Classification Using Deep Learning Method IEEE Commun. Lett. 2020 10.1109/LCOMM.2020.2999904 24 2226 

  17. 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. 

  18. 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. 

  19. 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. 

  20. 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. 

  21. 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. 

  22. Djosic Multi-algorithm UWB-based localization method for mixed LOS/NLOS environments Comput. Commun. 2022 10.1016/j.comcom.2021.10.031 181 365 

  23. Creswell Generative Adversarial Networks: An Overview IEEE Signal Process. Mag. 2018 10.1109/MSP.2017.2765202 35 53 

  24. Rosenblatt, F. (1957). The Perceptron-A Perceiving and Recognizing Automaton, Cornell Aeronautical Lab. 

  25. 10.1109/UEMCON47517.2019.8993100 Shirinabadi, P.A., and Abbasi, A. (2019, January 10-12). UWB Channel Classification Using Convolutional Neural Networks. Proceedings of the 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA. 

  26. Alavi, B. (2006). Distance Measurement Error Modeling for Time-of-Arrival-Based Indoor Geolocation. [Ph.D. Thesis, Worcester Polytechnic Institute]. 

  27. 10.1109/ICINIS.2015.35 Li, Q., Li, R., Ji, K., and Dai, W. (2015, January 1-3). Kalman Filter and Its Application. Proceedings of the 2015 8th International Conference on Intelligent Networks and Intelligent Systems (ICINIS), Tianjin, China. 

  28. 10.1109/IPIN.2016.7743686 Jimenez, A.R., and Seco, F. (2016, January 4-7). Comparing Decawave and Bespoon UWB location systems: Indoor/outdoor performance analysis. Proceedings of the 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Alcala de Henares, Spain. 

관련 콘텐츠

오픈액세스(OA) 유형

GOLD

오픈액세스 학술지에 출판된 논문

저작권 관리 안내
섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로