Radio Tomographic Imaging Localization Based on Transformer Model

Zhichao Lu; Heng Liu; Xueming Zhang

doi:10.1109/ITNEC56291.2023.10082228

Radio Tomographic Imaging Localization Based on Transformer Model

Zhichao Lu^*, Heng Liu, Xueming Zhang

^*此作品的通讯作者

信息与电子学院

Beijing Institute of Technology

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

2 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	ITNEC 2023 - IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference
编辑	Bing Xu
出版商	Institute of Electrical and Electronics Engineers Inc.
页	1134-1138
页数	5
ISBN（电子版）	9781665460033
DOI	https://doi.org/10.1109/ITNEC56291.2023.10082228
出版状态	已出版 - 2023
活动	6th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023 - Chongqing, 中国期限: 24 2月 2023 → 26 2月 2023

出版系列

姓名	ITNEC 2023 - IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference

会议

会议	6th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023
国家/地区	中国
市	Chongqing
时期	24/02/23 → 26/02/23

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

访问文件

10.1109/ITNEC56291.2023.10082228

其它文件与链接

链接到 Scopus 的出版物

引用此

Lu, Z., Liu, H., & Zhang, X. (2023). Radio Tomographic Imaging Localization Based on Transformer Model. 在 B. Xu (编辑), ITNEC 2023 - IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference (页码 1134-1138). (ITNEC 2023 - IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITNEC56291.2023.10082228

Lu, Zhichao ; Liu, Heng ; Zhang, Xueming. / Radio Tomographic Imaging Localization Based on Transformer Model. ITNEC 2023 - IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference. 编辑 / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2023. 页码 1134-1138 (ITNEC 2023 - IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference).

@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",

}

Lu, Z, Liu, H & Zhang, X 2023, Radio Tomographic Imaging Localization Based on Transformer Model. 在 B Xu (编辑), ITNEC 2023 - IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference. ITNEC 2023 - IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference, Institute of Electrical and Electronics Engineers Inc., 页码 1134-1138, 6th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023, Chongqing, 中国, 24/02/23. https://doi.org/10.1109/ITNEC56291.2023.10082228

Radio Tomographic Imaging Localization Based on Transformer Model. / Lu, Zhichao; Liu, Heng; Zhang, Xueming.
ITNEC 2023 - IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference. 编辑 / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2023. 页码 1134-1138 (ITNEC 2023 - IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Radio Tomographic Imaging Localization Based on Transformer Model

AU - Lu, Zhichao

AU - Liu, Heng

AU - Zhang, Xueming

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - deep learning

KW - Radio tomographic imaging

KW - Transformer

KW - wireless sensor network

UR - http://www.scopus.com/inward/record.url?scp=85152765131&partnerID=8YFLogxK

U2 - 10.1109/ITNEC56291.2023.10082228

DO - 10.1109/ITNEC56291.2023.10082228

M3 - Conference contribution

AN - SCOPUS:85152765131

T3 - ITNEC 2023 - IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference

SP - 1134

EP - 1138

BT - ITNEC 2023 - IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference

A2 - Xu, Bing

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023

Y2 - 24 February 2023 through 26 February 2023

ER -

Lu Z, Liu H, Zhang X. Radio Tomographic Imaging Localization Based on Transformer Model. 在 Xu B, 编辑, ITNEC 2023 - IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference. Institute of Electrical and Electronics Engineers Inc. 2023. 页码 1134-1138. (ITNEC 2023 - IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference). doi: 10.1109/ITNEC56291.2023.10082228

Radio Tomographic Imaging Localization Based on Transformer Model

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

指纹

引用此