Microwave Sensing, Signals and Systems
Department of Microelectronics

MSc I Roldan

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

Expertise: Digital signal processing focused on radar signals; Machine Learning applied to detection and classification.

Biography

Ignacio Roldan received his B.Sc. and M.Sc. in Telecommunication Engineering at the Universidad Politecnica de Madrid, Spain in 2014 and 2016. In 2018 he complemented his education with an M.Sc. in Signal Processing and Machine Learning at the same university. Ignacio has worked more than 5 years for Advanced Radar Technologies, a Spanish tech company focused on the design and manufacture of radar systems. In this period, he has been involved in several international projects developing state-of-the-art signal processing techniques for radars. In his last stage, he was focused on applying Machine Learning techniques to UAV detection and classification. In Sep 2020, he joined the Microwave Sensing, Signals, and Systems group at Delft University of Technology as a Ph.D. candidate.

Doppler-free classification for automotive radar

  1. Self-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars
    Roldan, Ignacio; Fioranelli, Francesco; Yarovoy, Alexander;
    IEEE Transactions on Vehicular Technology,
    pp. 1-10, 2023. DOI: 10.1109/TVT.2023.3269199

  2. Efficient Embedded Element Pattern Prediction via Machine Learning: A Case Study with Planar Non-Uniform Sub-Arrays
    Onat, Nehir Berk; Roldan, Ignacio; Fioranelli, Francesco; Yarovoy, Alexander; Aslan, Yanki;
    In 2023 17th European Conference on Antennas and Propagation (EuCAP),
    pp. 1-5, 2023. DOI: 10.23919/EuCAP57121.2023.10133770

  3. Low Complexity Single-Snapshot DoA Estimation via Bayesian Compressive Sensing
    Roldan, Ignacio; Lamberti, Lucas; Fioranelli, Francesco; Yarovoy, Alexander;
    In 2023 IEEE Radar Conference (RadarConf23),
    pp. 1-6, 2023. DOI: 10.1109/RadarConf2351548.2023.10149589

  4. Benchmarking classification algorithms for radar-based human activity recognition.
    Fioranelli, Francesco; Zhu, Simin; Roldan, Ignacio;
    IEEE Aerospace and Electronic Systems Magazine,
    pp. 1-4, 2022. DOI: 10.1109/MAES.2022.3216262

  5. Enhancing Angular Resolution Using Neural Networks in Automotive Radars
    Roldan, Ignacio; Fioranelli, Francesco; Yarovoy, Alexander;
    In 2021 18th European Radar Conference (EuRAD),
    pp. 58-61, 2022. DOI: 10.23919/EuRAD50154.2022.9784559

  6. Total Variation Compressive Sensing for Extended Targets in MIMO Radar
    Roldan, Ignacio; Fioranelli, Francesco; Yarovoy, Alexander;
    In 2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM),
    pp. 61-65, 2022. DOI: 10.1109/SAM53842.2022.9827822

  7. DopplerNet: a convolutional neural network for recognising targets in real scenarios using a persistent range–Doppler radar
    I. Roldan; C. R. del-Blanco; Á. Duque de Quevedo; F. Ibañez Urzaiz; J. Gismero Menoyo; A. Asensio López; D. Berjón; F. Jaureguizar; N. García;
    IET Radar, Sonar Navigation,
    Volume 14, Issue 4, pp. 593-600, 2020. DOI: 10.1049/iet-rsn.2019.0307

BibTeX support

Last updated: 8 Feb 2023

Ignacio Roldan

MSc students