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      3DLRA: An RFID 3D Indoor Localization Method Based on Deep Learning

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          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.

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          Most cited references 28

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          LANDMARC: indoor location sensing using active RFID

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            STPP: Spatial-Temporal Phase Profiling-Based Method for Relative RFID Tag Localization

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              Ad-hoc localization using ranging and sectoring

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                11 May 2020
                May 2020
                : 20
                : 9
                Affiliations
                [1 ]School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; b17121207@ 123456njupt.edu.cn (S.C.); b17040101@ 123456njupt.edu.cn (S.W.); b17090126@ 123456njupt.edu.cn (W.G.); lipeng@ 123456njupt.edu.cn (P.L.)
                [2 ]Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China
                Author notes
                [* ]Correspondence: xuhe@ 123456njupt.edu.cn ; Tel.: +86-25-8586-6354
                Article
                sensors-20-02731
                10.3390/s20092731
                7249055
                32403286
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                Categories
                Article

                Biomedical engineering

                internet of things, rfid, three-dimensional localization, deep learning

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