User Localization using RF Sensing: A Performance comparison between LIS and mm Wave Radars

Cristian J. Vaca-Rubio; Dariush Salami; Petar Popovski; Elisabeth de Carvalho; Zheng Hua Tan; Stephan Sigg

doi:10.48550/arXiv.2205.10321

User Localization using RF Sensing: A Performance comparison between LIS and mm Wave Radars

Cristian J. Vaca-Rubio, Dariush Salami, Petar Popovski, Elisabeth de Carvalho, Zheng Hua Tan, Stephan Sigg

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

Abstract

Since electromagnetic signals are omnipresent. Radio Frequency (RF)-seiisting has the potential to become a universal sensing mechanism with applications in localization, smart-home. retail, gesture recognition, intrusion detection, etc. Two emerging technologies in RF-seusing, namely sensing through Large Intelligent Surfaces (LISs) and mm Wave Frequency-Modulated Continuous-Wave (FMCW) radars, have been successfully applied to a wide range of applications. In this work, we compare LIS and mmWave radars for localization in real-world and simulated environments. In our experiments, the mm Wave radar achieves 0.71 Intersection Over Union (IOU) and 3cm error for bounding hoxes. while MS has 0,56 IOU and tflcni distance error. Although the radar outperforms the LIS hi terms of accuracy. LIS features additional applications in communication in addition to sensing scenarios.

Original language	English
Title of host publication	30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings
Publisher	European Signal Processing Conference (EUSIPCO)
Pages	1916-1920
Number of pages	5
ISBN (Electronic)	9789082797091
DOIs	https://doi.org/10.48550/arXiv.2205.10321
Publication status	Published - 2022
MoE publication type	A4 Article in a conference publication
Event	European Signal Processing Conference - Belgrade, Serbia Duration: 29 Aug 2022 → 2 Sep 2022 Conference number: 30 https://2022.eusipco.org/

Publication series

Name	European Signal Processing Conference
Volume	2022-August
ISSN (Print)	2219-5491

Conference

Conference	European Signal Processing Conference
Abbreviated title	EUSIPCO
Country/Territory	Serbia
City	Belgrade
Period	29/08/2022 → 02/09/2022
Internet address	https://2022.eusipco.org/

Keywords

FMCW
LIS
machine learning
mmWave
radar
Sensing

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10.48550/arXiv.2205.10321

https://ieeexplore.ieee.org/document/9909583

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Vaca-Rubio, C. J., Salami, D., Popovski, P., de Carvalho, E., Tan, Z. H., & Sigg, S. (2022). User Localization using RF Sensing: A Performance comparison between LIS and mm Wave Radars. In 30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings (pp. 1916-1920). (European Signal Processing Conference; Vol. 2022-August). European Signal Processing Conference (EUSIPCO). https://doi.org/10.48550/arXiv.2205.10321

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abstract = "Since electromagnetic signals are omnipresent. Radio Frequency (RF)-seiisting has the potential to become a universal sensing mechanism with applications in localization, smart-home. retail, gesture recognition, intrusion detection, etc. Two emerging technologies in RF-seusing, namely sensing through Large Intelligent Surfaces (LISs) and mm Wave Frequency-Modulated Continuous-Wave (FMCW) radars, have been successfully applied to a wide range of applications. In this work, we compare LIS and mmWave radars for localization in real-world and simulated environments. In our experiments, the mm Wave radar achieves 0.71 Intersection Over Union (IOU) and 3cm error for bounding hoxes. while MS has 0,56 IOU and tflcni distance error. Although the radar outperforms the LIS hi terms of accuracy. LIS features additional applications in communication in addition to sensing scenarios.",

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author = "Vaca-Rubio, {Cristian J.} and Dariush Salami and Petar Popovski and {de Carvalho}, Elisabeth and Tan, {Zheng Hua} and Stephan Sigg",

note = "Funding Information: This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant agreement No, 813999. Publisher Copyright: {\textcopyright} 2022 European Signal Processing Conference, EUSIPCO. All rights reserved. | openaire: EC/H2020/813999/EU//WINDMILL; European Signal Processing Conference, EUSIPCO ; Conference date: 29-08-2022 Through 02-09-2022",

year = "2022",

doi = "10.48550/arXiv.2205.10321",

language = "English",

series = "European Signal Processing Conference",

publisher = "European Signal Processing Conference (EUSIPCO)",

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Vaca-Rubio, CJ, Salami, D, Popovski, P, de Carvalho, E, Tan, ZH & Sigg, S 2022, User Localization using RF Sensing: A Performance comparison between LIS and mm Wave Radars. in 30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings. European Signal Processing Conference, vol. 2022-August, European Signal Processing Conference (EUSIPCO), pp. 1916-1920, European Signal Processing Conference, Belgrade, Serbia, 29/08/2022. https://doi.org/10.48550/arXiv.2205.10321

User Localization using RF Sensing : A Performance comparison between LIS and mm Wave Radars. / Vaca-Rubio, Cristian J.; Salami, Dariush; Popovski, Petar et al.

30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings. European Signal Processing Conference (EUSIPCO), 2022. p. 1916-1920 (European Signal Processing Conference; Vol. 2022-August).

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

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ER -

Vaca-Rubio CJ, Salami D, Popovski P, de Carvalho E, Tan ZH, Sigg S. User Localization using RF Sensing: A Performance comparison between LIS and mm Wave Radars. In 30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings. European Signal Processing Conference (EUSIPCO). 2022. p. 1916-1920. (European Signal Processing Conference). https://doi.org/10.48550/arXiv.2205.10321

User Localization using RF Sensing: A Performance comparison between LIS and mm Wave Radars

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WINDMILL: Integrating Wireless Communication ENgineering and MachIne Learning

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User Localization using RF Sensing: A Performance comparison between LIS and mm Wave Radars

Abstract

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Conference

Keywords

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Projects

WINDMILL: Integrating Wireless Communication ENgineering and MachIne Learning

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Science-IT

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