The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. Cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of lighting conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. We propose two FCNs that take spectrograms of the beat signals as input, and provide the corresponding clean range profiles as output. We propose two architectures for interference mitigation which outperform the classical zeroing technique. Moreover, considering the lack of databases for this task, we release as open source a large scale data set that closely replicates real world automotive scenarios for single-interference cases, allowing others to objectively compare their future work in this domain. The data set is available for download at: http://github.com/ristea/arim.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation


    Contributors:


    Publication date :

    2020-11-01


    Size :

    1760261 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    RadChat: Spectrum Sharing for Automotive Radar Interference Mitigation

    Aydogdu, Canan / Keskin, Musa Furkan / Garcia, Nil et al. | IEEE | 2021



    Interference Mitigation for Automotive FMCW Radar With Tensor Decomposition

    Wang, Yunxuan / Huang, Yan / Liu, Jiang et al. | IEEE | 2024



    A Deep Learning Approach for Automotive Radar Interference Mitigation

    Mun, Jiwoo / Kim, Heasung / Lee, Jungwoo | IEEE | 2018