Microwave Sensing, Signals and Systems
Department of Microelectronics

MSc R Guendel

PhD student
Microwave Sensing, Signals and Systems (MS3), Department of Microelectronics

Expertise: Monitoring of continuous human activities in range and time beyond micro-Doppler by using RF-sensing technology.

Biography

Ronny Gündel received his Diploma-Engineering (FH) (Dipl.-Ing. (FH)) degree at the University of Applied Sciences in Zwickau in January 2017. During his studies, he was employed for 2.5 years as a student researcher at the Fraunhofer Institute for Machine Tools and Forming Technology in Chemnitz (IWU). For the last quarter of employment, the Fraunhofer institute operated as the practical partner for the diploma-thesis. The thesis for a reliable image transmission for a multi-camera-system was selected as an outstanding thesis from the University. In 2017/18 he joined Villanova University (USA) as a Fulbright scholar for a graduate program in Electrical Engineering. Over summer 2018, he worked on wireless communications for vehicular connectivity as a visitor at the Vodafone Char Dresden under Prof. Gerhard Fettweis. In 2018/2019, the Center for Advanced Communications of Villanova University offered him a research assistantship under the guidance of Dr. Moeness G. Amin, working on radar classification of consecutive and contiguous human motions with an industry partner in Philadelphia. Besides the industrial work, he graduated with a Master of Science degree in Signal Processing and Communications in 2019. Ronny Gündel joined TU Delft in January 2020, in the MS3 (Microwave Sensing Signals & Systems) section led by Prof. Alexander Yarovoy within the Department of Microelectronics. Currently, he is working on monitoring of continuous human activities in range and time beyond micro-Doppler by using RF-sensing technology.

Distributed radar for human activity recognition

  1. A Survey on Radar-Based Continuous Human Activity Recognition
    Ullmann, Ingrid; Guendel, Ronny G.; Kruse, Nicolas Christian; Fioranelli, Francesco; Yarovoy, Alexander;
    IEEE Journal of Microwaves,
    pp. 1-13, 2023. DOI: 10.1109/JMW.2023.3264494

  2. Distributed radar fusion and recurrent networks for classification of continuous human activities
    Guendel, Ronny G.; Fioranelli, Francesco; Yarovoy, Alexander;
    IET Radar, Sonar and Navigation,
    pp. 12, 2022. DOI: https://doi.org/10.1049/rsn2.12249
    Keywords: ... distributed sensors, micro Doppler, radar target recognition, radar tracking, signal classification, ultra wideband radar.

    document

  3. Continuous Human Activity Recognition With Distributed Radar Sensor Networks and CNN–RNN Architectures
    Zhu, Simin; Guendel, Ronny Gerhard; Yarovoy, Alexander; Fioranelli, Francesco;
    IEEE Transactions on Geoscience and Remote Sensing,
    Volume 60, pp. 1-15, 2022. DOI: 10.1109/TGRS.2022.3189746

  4. Radar-based Human Activities Classification with Complex-valued Neural Networks
    Yang, Ximei; Guendel, Ronny G.; Yarovoy, Alexander; Fioranelli, Francesco;
    In 2022 IEEE Radar Conference (RadarConf22),
    pp. 1-6, 2022. DOI: 10.1109/RadarConf2248738.2022.9763903

  5. Evaluation Metrics for Continuous Human Activity Classification Using Distributed Radar Networks
    Guendel, Ronny G.; Fioranelli, Francesco; Yarovoy, Alexander;
    In 2022 IEEE Radar Conference (RadarConf22),
    pp. 1-6, 2022. DOI: 10.1109/RadarConf2248738.2022.9764181

  6. Distributed Radar-based Human Activity Recognition using Vision Transformer and CNNs
    Zhao, Yubin; Guendel, Ronny Gerhard; Yarovoy, Alexander; Fioranelli, Francesco;
    In 2021 18th European Radar Conference (EuRAD),
    pp. 301-304, 2022. DOI: 10.23919/EuRAD50154.2022.9784575

  7. Exploiting Radar Data Domains for Classification with Spatially Distributed Nodes
    R. G. Guendel; N.C. Kruse; F. Fioranelli; A. Yarovoy;
    In 2022. SET-312 Research Specialists' Meeting over Distributed Multi-Spectral/Statics Sensing ; Conference date: 23-05-2022 Through 24-05-2022.

  8. Continuous human activity recognition for arbitrary directions with distributed radars
    Guendel, Ronny Gerhard; Unterhorst, Matteo; Gambi, Ennio; Fioranelli, Francesco; Yarovoy, Alexander;
    In 2021 IEEE Radar Conference (RadarConf21),
    pp. 1-6, 2021. DOI: 10.1109/RadarConf2147009.2021.9454972

  9. Radar classifications of consecutive and contiguous human gross-motor activities
    Moeness G. Amin; Ronny G. Guendel;
    IET Radar, Sonar Navigation,
    Volume 14, Issue 9, pp. 1417-1429, 09 2020. DOI: 10.1049/iet-rsn.2019.0585

  10. Phase-based Classification for Arm Gesture and Gross-Motor Activities using Histogram of Oriented Gradients
    R. G. Guendel; F. Fioranelli; A. Yarovoy;
    IEEE Sensors Journal,
    pp. 10, 12 2020. DOI: 10.1109/JSEN.2020.3044675

  11. Radar Human Motion Recognition Using Motion States and Two-Way Classifications
    Moeness G. Amin; Ronny G. Guendel;
    In 2020 IEEE International Radar Conference (RADAR),
    pp. 1046-1051, 2020. DOI: 10.1109/RADAR42522.2020.9114613

  12. Derivative Target Line (DTL) for Continuous Human Activity Detection and Recognition
    R. G. Guendel; F. Fioranelli; A. Yarovoy;
    In 2020 IEEE Radar Conference (RadarConf20),
    pp. 1-6, 2020. DOI: 10.1109/RadarConf2043947.2020.9266383

  13. Radar Classification of Contiguous Activities of Daily Living
    Ronny Gerhard Guendel;
    MSc thesis, 2020.

  14. Automatic Arm Motion Recognition Using Radar for Smart Home Technologies
    Moeness G. Amin; Z. Zeng; T. Shan; Ronny G. Guendel;
    In 2019 International Radar Conference (RADAR),
    pp. 1-4, 2019. DOI: 10.1109/RADAR41533.2019.171318

  15. RF sensing for continuous monitoring of human activities for home consumer applications
    Moeness G. Amin; Arun Ravisankar; Ronny G. Guendel;
    In Fauzia Ahmad (Ed.), Big Data: Learning, Analytics, and Applications,
    SPIE, International Society for Optics and Photonics, pp. 33 -- 44, 2019. DOI: 10.1117/12.2519984
    Keywords: ... Radar, indoor monitoring, smart homes, motion classification.

    document

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Last updated: 8 Feb 2023

Ronny Guendel

MSc students