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

^*Corresponding author for this work

School of Information and Electronics

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

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.

Original language	English
Title of host publication	ITNEC 2023 - IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference
Editors	Bing Xu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1134-1138
Number of pages	5
ISBN (Electronic)	9781665460033
DOIs	https://doi.org/10.1109/ITNEC56291.2023.10082228
Publication status	Published - 2023
Event	6th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023 - Chongqing, China Duration: 24 Feb 2023 → 26 Feb 2023

Publication series

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

Conference

Conference	6th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023
Country/Territory	China
City	Chongqing
Period	24/02/23 → 26/02/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Keywords

Radio tomographic imaging
Transformer
deep learning
wireless sensor network

Access to Document

10.1109/ITNEC56291.2023.10082228

Cite this

Lu, Z., Liu, H., & Zhang, X. (2023). Radio Tomographic Imaging Localization Based on Transformer Model. In B. Xu (Ed.), ITNEC 2023 - IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference (pp. 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. editor / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 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 = "Radio tomographic imaging, Transformer, deep learning, 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. in B Xu (ed.), 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., pp. 1134-1138, 6th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023, Chongqing, China, 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. ed. / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1134-1138 (ITNEC 2023 - IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 - Radio tomographic imaging

KW - Transformer

KW - deep learning

KW - wireless sensor network

UR - https://www.scopus.com/pages/publications/85152765131

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. In Xu B, editor, ITNEC 2023 - IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference. Institute of Electrical and Electronics Engineers Inc. 2023. p. 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

Abstract

Publication series

Conference

UN SDGs

Keywords

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