Radio Tomographic Imaging Localization Based on Transformer Model

Zhichao Lu*, Heng Liu, Xueming Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 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 languageEnglish
Title of host publicationITNEC 2023 - IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference
EditorsBing Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1134-1138
Number of pages5
ISBN (Electronic)9781665460033
DOIs
Publication statusPublished - 2023
Event6th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023 - Chongqing, China
Duration: 24 Feb 202326 Feb 2023

Publication series

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

Conference

Conference6th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023
Country/TerritoryChina
CityChongqing
Period24/02/2326/02/23

Keywords

  • deep learning
  • Radio tomographic imaging
  • Transformer
  • wireless sensor network

Fingerprint

Dive into the research topics of 'Radio Tomographic Imaging Localization Based on Transformer Model'. Together they form a unique fingerprint.

Cite this