3D Localization of RFID Antenna Tags using Convolutional Neural Networks


With the Internet of Things becoming widespread, there has been a rising demand for indoor location-based services. In recent trends, radio frequency identification has become an integral part of the production of IoT. Conventional methods use prior knowledge of antenna and tag positioning along with high-precision equipment capable of collecting phase or time-of-arrival data for robust estimation of three-dimensional location. In this work, we propose a three-dimensional localization method based on deep learning that relies on the phase and received signal strength indicator (RSSI) captured by steering beams to various locations using a phased array antenna. We evaluate the efficiency of this system by estimating three-dimensional location of 7 RFID tags mounted on metallic surfaces placed in a naturalistic environment. To evaluate the generalization of the proposed approach we crossvalidate the localization performance in different environments. The localization performance of the proposed approach is also tested on different formfactor of the RFID tag. With no prior information of either the tags or environment, the proposed system was able to achieve an average localization error as low as 1.33 cm with better system stability.


Electrical and Computer Engineering

Publication Status

Early Access


Research reported in this publication was partially supported by Navy SBIR program under award numbers: N122-126 and N182-100.

Keywords and Phrases

3D Localization; Antenna Measurements; Antennas; Convolutional Neural Network (CNN); Deep Neural Network (DNN); Direction-Of-Arrival Estimation; Estimation; Internet-Of-Things (IoT); Location Awareness; Machine Learning (ML); Phased Array Antennas; Phased Arrays; Three-Dimensional Displays; UHF RFID

International Standard Serial Number (ISSN)

1557-9662; 0018-9456

Document Type

Article - Journal

Document Version


File Type





© 2022 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

08 Feb 2022