Scattering Centers to Point Clouds: A Review of mmWave Radars for Non-Radar-Engineers

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F22%3A39919611" target="_blank" >RIV/00216275:25530/22:39919611 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9908570" target="_blank" >https://ieeexplore.ieee.org/document/9908570</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2022.3211673" target="_blank" >10.1109/ACCESS.2022.3211673</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Scattering Centers to Point Clouds: A Review of mmWave Radars for Non-Radar-Engineers

Popis výsledku v původním jazyce

Recently, mmWave radars have been gaining popularity, thanks to their low cost, ease of use and high-resolution sensing. In this paper, we provide a review of the mmWave radar data processing frameworks, starting from mathematical foundations to applications. Specifically, we focus on the mmWave radar point cloud as a robust data structure representing compressed signatures for target recognition and classification. We first focus on the generation of the radar point clouds, and the signal processing algorithms designed for their unique characteristics. Then, we illustrate how the radar point clouds are prepared for feature extraction and classification using machine learning and deep learning approaches. Finally, we summarize the state-of-the-art applications, open datasets, developments and future research directions in this field.

Název v anglickém jazyce

Scattering Centers to Point Clouds: A Review of mmWave Radars for Non-Radar-Engineers

Popis výsledku anglicky

Recently, mmWave radars have been gaining popularity, thanks to their low cost, ease of use and high-resolution sensing. In this paper, we provide a review of the mmWave radar data processing frameworks, starting from mathematical foundations to applications. Specifically, we focus on the mmWave radar point cloud as a robust data structure representing compressed signatures for target recognition and classification. We first focus on the generation of the radar point clouds, and the signal processing algorithms designed for their unique characteristics. Then, we illustrate how the radar point clouds are prepared for feature extraction and classification using machine learning and deep learning approaches. Finally, we summarize the state-of-the-art applications, open datasets, developments and future research directions in this field.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20202 - Communication engineering and systems

Projekt

<a href="/cs/project/LTAIN19100" target="_blank" >LTAIN19100: Vývoj bezkontaktní technologie pro inteligentní ochranu zájmových prostor</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE ACCESS

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

10

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

30

Strana od-do

110992-111021

Kód UT WoS článku

000873874700001

EID výsledku v databázi Scopus

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