Learning on Multistatic Simulation Data for Radar-Based Automotive Gesture Recognition-Reference-Cited by-同舟云学术

Learning on Multistatic Simulation Data for Radar-Based Automotive Gesture Recognition

Published:2022-11 Issue:11 Volume:70 Page:5039-5050
ISSN:0018-9480
Container-title:IEEE Transactions on Microwave Theory and Techniques
language:
Short-container-title:IEEE Trans. Microwave Theory Techn.

Author:

Kern Nicolai¹^ORCID,Aguilar Julian¹,Grebner Timo¹^ORCID,Meinecke Benedikt¹^ORCID,Waldschmidt Christian¹^ORCID

Affiliation:

1. Institute of Microwave Engineering, Ulm University, Ulm, Germany

Funder

Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg

Ministerium für Verkehr Baden-Württemberg as part of the Project INTUITIVER

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Condensed Matter Physics,Radiation

Link

http://xplorestaging.ieee.org/ielx7/22/9939118/09875369.pdf?arnumber=9875369

Reference32 articles.

1. Robust Doppler-Based Gesture Recognition With Incoherent Automotive Radar Sensor Networks

2. A method for registration of 3-D shapes

3. RadHAR: Human activity recognition from point clouds generated through a millimeter-wave radar;singh;Proc 3rd ACM Workshop Sec Ad Hoc Sensor Netw,2019

4. Radar Data Cube Processing for Human Activity Recognition Using Multisubspace Learning

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