@inproceedings{8e9516c71c7a422b9e3f9052e07fa05d,
title = "Radio Tomographic Imaging Localization Based on Transformer Model",
abstract = "Device-free localization (DFL) is an indispensable part of disaster relief and anti-terrorism operations. Radio tomographic imaging (RTI) emerges for locating targets in the area by using received signal strength (RSS) measurements from a wireless sensor network. In this paper, we briefly analyze the forward model of RTI and proposes a deep learning based RTI method to achieve multi-target location with high precision. Compared with the traditional RTI algorithm, this method has advantages in distinguishing multiple targets and computing efficiency. Simulation and experimental results verify the effectiveness of the proposed method.",
keywords = "deep learning, Radio tomographic imaging, Transformer, wireless sensor network",
author = "Zhichao Lu and Heng Liu and Xueming Zhang",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 6th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023 ; Conference date: 24-02-2023 Through 26-02-2023",
year = "2023",
doi = "10.1109/ITNEC56291.2023.10082228",
language = "English",
series = "ITNEC 2023 - IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1134--1138",
editor = "Bing Xu",
booktitle = "ITNEC 2023 - IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference",
address = "United States",
}
