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

C. J. Vaca-Rubio; D. Salami; P. Popovski; E. De Carvalho; Z. -H. Tan; S. Sigg

doi:10.23919/EUSIPCO55093.2022.9909583

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

C. J. Vaca-Rubio, D. Salami, P. Popovski, E. De Carvalho, Z. -H. Tan, S. Sigg

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

Abstract

Since electromagnetic signals are omnipresent. Radio Frequency (RF)-sensing 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-sensing, namely sensing through Large Intelligent Surfaces (LISs) and mmWave 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 mmWave radar achieves 0.71 Intersection Over Union (IOU) and 3cm error for bounding boxes, while LIS has 0.56 IOU and 10cm distance error. Although the radar outperforms the LIS in terms of accuracy, LIS features additional applications in communication in addition to sensing scenarios.

Original language	English
Title of host publication	2022 30th European Signal Processing Conference (EUSIPCO)
Publisher	IEEE
Pages	1916-1920
Number of pages	5
ISBN (Electronic)	978-90-827970-9-1
ISBN (Print)	978-1-6654-6799-5
DOIs	https://doi.org/10.23919/EUSIPCO55093.2022.9909583
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
ISSN (Electronic)	2076-1465

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

Location awareness
Radio frequency
Performance evaluation
Radar detection
Intrusion detection
Massive MIMO
Sensors
Sensing
machine learning
US
mmWave
radar
FMCW

Access to Document

10.23919/EUSIPCO55093.2022.9909583

OpenUrl availability

Full text

WINDMILL: Integrating Wireless Communication ENgineering and MachIne Learning
Tirkkonen, O., Salami, D., Sigg, S. & Kazemi, P.
01/01/2019 → 30/06/2023
Project: EU: MC

Cite this

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title = "User Localization using RF Sensing: A Performance comparison between LIS and mmWave Radars",

abstract = "Since electromagnetic signals are omnipresent. Radio Frequency (RF)-sensing 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-sensing, namely sensing through Large Intelligent Surfaces (LISs) and mmWave 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 mmWave radar achieves 0.71 Intersection Over Union (IOU) and 3cm error for bounding boxes, while LIS has 0.56 IOU and 10cm distance error. Although the radar outperforms the LIS in terms of accuracy, LIS features additional applications in communication in addition to sensing scenarios.",

keywords = "Location awareness, Radio frequency, Performance evaluation, Radar detection, Intrusion detection, Massive MIMO, Sensors, Sensing, machine learning, US, mmWave, radar, FMCW",

author = "Vaca-Rubio, {C. J.} and D. Salami and P. Popovski and Carvalho, {E. De} and Tan, {Z. -H.} and S. Sigg",

note = "| openaire: EC/H2020/813999/EU//WINDMILL; European Signal Processing Conference, EUSIPCO ; Conference date: 29-08-2022 Through 02-09-2022",

year = "2022",

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language = "English",

isbn = "978-1-6654-6799-5",

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Vaca-Rubio, CJ, Salami, D, Popovski, P, Carvalho, ED, Tan, Z-H & Sigg, S 2022, User Localization using RF Sensing: A Performance comparison between LIS and mmWave Radars. in 2022 30th European Signal Processing Conference (EUSIPCO). European Signal Processing Conference, IEEE, pp. 1916-1920, European Signal Processing Conference, Belgrade, Serbia, 29/08/2022. https://doi.org/10.23919/EUSIPCO55093.2022.9909583

User Localization using RF Sensing: A Performance comparison between LIS and mmWave Radars. / Vaca-Rubio, C. J.; Salami, D.; Popovski, P. et al.

2022 30th European Signal Processing Conference (EUSIPCO). IEEE, 2022. p. 1916-1920 (European Signal Processing Conference).

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

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AU - Salami, D.

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AU - Tan, Z. -H.

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AB - Since electromagnetic signals are omnipresent. Radio Frequency (RF)-sensing 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-sensing, namely sensing through Large Intelligent Surfaces (LISs) and mmWave 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 mmWave radar achieves 0.71 Intersection Over Union (IOU) and 3cm error for bounding boxes, while LIS has 0.56 IOU and 10cm distance error. Although the radar outperforms the LIS in terms of accuracy, LIS features additional applications in communication in addition to sensing scenarios.

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ER -

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

Abstract

Publication series

Conference

Keywords

Access to Document

OpenUrl availability

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Projects

WINDMILL: Integrating Wireless Communication ENgineering and MachIne Learning

Cite this