{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:19:48Z","timestamp":1778602788096,"version":"3.51.4"},"reference-count":30,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,5,11]],"date-time":"2020-05-11T00:00:00Z","timestamp":1589155200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["Grant 2019YFB2103000 and 2018YFB1003201"],"award-info":[{"award-number":["Grant 2019YFB2103000 and 2018YFB1003201"]}]},{"name":"National Natural Science Foundation of P. R. China","award":["No. 61672296, No. 61602261, No. 61872196, and No. 61872194"],"award-info":[{"award-number":["No. 61672296, No. 61602261, No. 61872196, and No. 61872194"]}]},{"name":"STITP of the Nanjing University of Posts and Telecommunications(NUPT)","award":["Grant SZDG2019020"],"award-info":[{"award-number":["Grant SZDG2019020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As the core supporting technology of the Internet of Things, Radio Frequency Identification (RFID) technology is rapidly popularized in the fields of intelligent transportation, logistics management, industrial automation, and the like, and has great development potential due to its fast and efficient data collection ability. RFID technology is widely used in the field of indoor localization, in which three-dimensional location can obtain more real and specific target location information. Aiming at the existing three-dimensional location scheme based on RFID, this paper proposes a new three-dimensional localization method based on deep learning: combining RFID absolute location with relative location, analyzing the variation characteristics of the received signal strength (RSSI) and Phase, further mining data characteristics by deep learning, and applying the method to the smart library scene. The experimental results show that the method has a higher location accuracy and better system stability.<\/jats:p>","DOI":"10.3390\/s20092731","type":"journal-article","created":{"date-parts":[[2020,5,11]],"date-time":"2020-05-11T12:26:30Z","timestamp":1589199990000},"page":"2731","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["3DLRA: An RFID 3D Indoor Localization Method Based on Deep Learning"],"prefix":"10.3390","volume":"20","author":[{"given":"Shuyan","family":"Cheng","sequence":"first","affiliation":[{"name":"School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shujun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbai","family":"Guan","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2809-2237","authenticated-orcid":false,"given":"He","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"},{"name":"Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5026-5347","authenticated-orcid":false,"given":"Peng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"},{"name":"Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,11]]},"reference":[{"key":"ref_1","first-page":"11","article-title":"Present situation and development trend of advanced indoor positioning technology","volume":"11","author":"Zhao","year":"2015","journal-title":"Telecom Netw. Technol."},{"key":"ref_2","first-page":"15","article-title":"Development and application of RFID technology","volume":"29","author":"Wang","year":"2011","journal-title":"Technol. Inf."},{"key":"ref_3","first-page":"320","article-title":"Research on Indoor Location Algorithm Based on RFID Technology","volume":"27","author":"Yan","year":"2010","journal-title":"Comput. Simul."},{"key":"ref_4","unstructured":"Lei, Y., Mi, W., and Wang, D. (2007). Navigation studies based on the ubiquitous positioning technologies. Proceedings of SPIE-The International Society for Optical Engineering, International Society for Optics and Photonics."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1109\/TNET.2016.2590996","article-title":"STPP: Spatial-Temporal Phase Profiling-Based Method for Relative RFID Tag Localization","volume":"25","author":"Shangguan","year":"2017","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"20249","DOI":"10.1109\/ACCESS.2019.2895129","article-title":"PRDL: Relative Localization Method of RFID Tags via Phase and RSSI Based on Deep Learning","volume":"7","author":"Shen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, G., Qian, C., Shangguan, L., Ding, H., Han, J., Yang, N., Xi, W., and Zhao, J. (2017, January 12\u201314). HMRL: Relative Localization of RFID Tags with Static Devices. Proceedings of the 2017 14th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), San Diego, CA, USA.","DOI":"10.1109\/SAHCN.2017.7964944"},{"key":"ref_8","unstructured":"Chintalapudi, K.K., Dhariwal, A., Govindan, R., and Sukhatme, G. (2004, January 7\u201311). Ad-hoc localization using ranging and sectoring. Proceedings of the IEEE INFOCOM 2004, Hong Kong, China."},{"key":"ref_9","unstructured":"Ni, L.M., Liu, Y., Lau, Y.C., and Patil, A.P. (2003, January 26\u201326). LANDMARC: Indoor location sensing using active RFID. Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, Fort Worth, TX, USA."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Liu, Y., and Ni, L.M. (2007, January 10\u201314). VIRE: Active RFID-based Localization Using Virtual Reference Elimination. Proceedings of the 2007 International Conference on Parallel Processing (ICPP 2007), Xi\u2019an, China.","DOI":"10.1109\/ICPP.2007.84"},{"key":"ref_11","unstructured":"Yi, Z., Qishan, H., and Yuan, L. (2013). Research on the Indoor Location Algorithm Based on RFID. Auckland, New Zealand, Trans Tech Publications Ltd."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shen, L., Zhang, Q., Pang, J., Xu, H., Li, P., and Xue, D. (2019). ANTspin: Efficient Absolute Localization Method of RFID Tags via Spinning Antenna. Sensor, 19.","DOI":"10.3390\/s19092194"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"825","DOI":"10.3390\/s18030825","article-title":"Moving Object Localization Based on UHF RFID Phase and Laser Clustering","volume":"18","author":"Yulu","year":"2018","journal-title":"Sensor"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gharat, V., Colin, E., Baudoin, G., and Richard, D. (2017, January 18\u201321). Indoor performance analysis of LF-RFID based positioning system: Comparison with UHF-RFID and UWB. Proceedings of the 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sapporo, Japan.","DOI":"10.1109\/IPIN.2017.8115901"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Almaaitah, A., Ali, K., Hassanein, H.S., and Ibnkahla, M. (2010, January 23\u201327). 3D Passive Tag Localization Schemes for Indoor RFID Applications. Proceedings of the 2010 IEEE International Conference on Communications, Cape Town, South Africa.","DOI":"10.1109\/ICC.2010.5501946"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Qiu, L., Huang, Z., Wirstr\u00f6m, N., and Voigt, T. (2016, January 3\u20135). 3DinSAR: Object 3D localization for indoor RFID applications. Proceedings of the 2016 IEEE International Conference on RFID (RFID), Orlando, FL, USA.","DOI":"10.1109\/RFID.2016.7488026"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bouet, M., and Pujolle, G. (2008, January 15\u201318). A range-free 3-D localization method for RFID tags based on virtual landmarks. Proceedings of the 2008 IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications, Cannes, France.","DOI":"10.1109\/PIMRC.2008.4699497"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2438","DOI":"10.1109\/TMC.2018.2796092","article-title":"Tagspin: High Accuracy Spatial Calibration of RFID Antennas via Spinning Tags","volume":"17","author":"Duan","year":"2018","journal-title":"IEEE T Mobile Comput"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tlili, F., Hamdi, N., and Belghith, A. (2012, January 5\u20137). Accurate 3D localization scheme based on active RFID tags for indoor environment. Proceedings of the 2012 IEEE International Conference on RFID-Technologies and Applications (RFID-TA), Nice, France.","DOI":"10.1109\/RFID-TA.2012.6404550"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, C., Wu, H., and Tzeng, N. (2007, January 6\u201312). RFID-Based 3-D Positioning Schemes. Proceedings of the IEEE INFOCOM 2007-26th IEEE International Conference on Computer Communications, Barcelona, Spain.","DOI":"10.1109\/INFCOM.2007.147"},{"key":"ref_21","first-page":"360","article-title":"Analysis of RSSI-Based Distance Measurement at Different Antenna Heights","volume":"18","author":"Jin","year":"2012","journal-title":"J. Shanghai Univ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Houssaini, D.E., Mohamed, Z., Khriji, S., Besbes, K., and Kanoun, O. (2018, January 16\u201318). A Filtered RSSI Model Based on Hardware Characteristic for Localization Algorithm in Wireless Sensor Networks. Proceedings of the 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA)), Krakow, Poland.","DOI":"10.1109\/WAINA.2018.00073"},{"key":"ref_23","unstructured":"Mota, H.D.O., and Vasconcelos, F.H. (2005, January 20). Data Processing System for Denoising of Signals in Real-Time Using the Wavelet Transform. Proceedings of the Third International Workshop on Intelligent Solutions in Embedded Systems, Hamburg, Germany."},{"key":"ref_24","unstructured":"Tao, J., Huang, Y., and Wang, Y. (2012). Study on Improved LANDMARC Node Localization Algorithm, Anhui University of Science and Technology."},{"key":"ref_25","first-page":"209","article-title":"Research on indoor positioning simulation based on LANDMARC system","volume":"27","author":"Li","year":"2008","journal-title":"Comput. Eng. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Baptista, D., Morgado-Dias, F., and Sousa, L. (2018, January 9\u201312). Configurable N-fold Hardware Architecture for Convolutional Neural Networks. Proceedings of the 2018 International Conference on Biomedical Engineering and Applications (ICBEA), Funchal, Portugal.","DOI":"10.1109\/ICBEA.2018.8471739"},{"key":"ref_27","unstructured":"Wang, Y. (2019). Research on Missing Data Filling and Prediction Method based on Generative Adversarial Network, South China University of Technology."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, K., Shi, H., and Hu, Q. (2016, January 13\u201315). Complex convolution Kernel for deep networks. Proceedings of the 8th International Conference on Wireless Communications & Signal Processing (WCSP), Yangzhou, China.","DOI":"10.1109\/WCSP.2016.7752631"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ni, R., Cheng, H.D., Zhao, Y., and Hou, Y. (2013, January 17\u201320). High Capacity Reversible Watermarking for Images Based on Classified Neural Network. Proceedings of the 18th Scandinavian Conference on Image Analysis, Espoo, Finland.","DOI":"10.1007\/978-3-642-38886-6_65"},{"key":"ref_30","unstructured":"Zunino, R., and Gastaldo, P. (2002, January 26\u201329). Analog implementation of the SoftMax function. Proceedings of the 2002 IEEE International Symposium on Circuits and Systems, Phoenix-Scottsdale, AZ, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/9\/2731\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:27:39Z","timestamp":1760174859000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/9\/2731"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,11]]},"references-count":30,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["s20092731"],"URL":"https:\/\/doi.org\/10.3390\/s20092731","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,11]]}}}