Abstract
The interest of the automotive industry has progressively focused on
subjects related to driver assistance systems as well as autonomous
cars. In order to achieve remarkable results, cars combine a variety of
sensors to perceive their surroundings robustly. Among them, radar
sensors are indispensable because of their independence of light
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. 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.