Print Email Facebook Twitter Self-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars Title Self-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars Author Roldan Montero, I. (TU Delft Microwave Sensing, Signals & Systems) Fioranelli, F. (TU Delft Microwave Sensing, Signals & Systems) Yarovoy, Alexander (TU Delft Microwave Sensing, Signals & Systems) Date 2023 Abstract A novel framework to enhance the angular resolution of automotive radars is proposed. An approach to enlarge the antenna aperture using artificial neural networks is developed using a self-supervised learning scheme. Data from a high angular resolution radar, i.e., a radar with a large antenna aperture, is used to train a deep neural network to extrapolate the antenna element's response. Afterward, the trained network is used to enhance the angular resolution of compact, low-cost radars. One million scenarios are simulated in a Monte-Carlo fashion, varying the number of targets, their Radar Cross Section (RCS), and location to evaluate the method's performance. Finally, the method is tested in real automotive data collected outdoors with a commercial radar system. A significant increase in the ability to resolve targets is demonstrated, which can translate to more accurate and faster responses from the planning and decision-making system of the vehicle. Subject angular resolutionAntenna arraysAutomotive engineeringautomotive radarmachine learningMIMOMIMO radarneural networksRadarRadar antennasRadar cross-sectionsradar signal processingReceiving antennas To reference this document use: http://resolver.tudelft.nl/uuid:aabf36b1-ad0b-4c6f-af69-90331fddc6a2 DOI https://doi.org/10.1109/TVT.2023.3269199 Embargo date 2024-03-19 ISSN 0018-9545 Source IEEE Transactions on Vehicular Technology, 72 (9), 11505-11514 Bibliographical note Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type journal article Rights © 2023 I. Roldan Montero, F. Fioranelli, Alexander Yarovoy Files PDF Self_Supervised_Learning_ ... Radars.pdf 1.96 MB Close viewer /islandora/object/uuid:aabf36b1-ad0b-4c6f-af69-90331fddc6a2/datastream/OBJ/view