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Radar sensors are gradually becoming a wide-spread equipment for roadvehicles, playing a crucial role in autonomous driving and road safety. Thebroad adoption of radar sensors increases the chance of interference amongsensors from different vehicles, generating corrupted range profiles andrange-Doppler maps. In order to extract distance and velocity of multipletargets from range-Doppler maps, the interference affecting each range profileneeds to be mitigated. In this paper, we propose a fully convolutional neuralnetwork for automotive radar interference mitigation. In order to train ournetwork in a real-world scenario, we introduce a new data set of realisticautomotive radar signals with multiple targets and multiple interferers. To ourknowledge, we are the first to apply weight pruning in the automotive radardomain, obtaining superior results compared to the widely-used dropout. Whilemost previous works successfully estimated the magnitude of automotive radarsignals, we propose a deep learning model that can accurately estimate thephase. For instance, our novel approach reduces the phase estimation error withrespect to the commonly-adopted zeroing technique by half, from 12.55 degreesto 6.58 degrees. Considering the lack of databases for automotive radarinterference mitigation, we release as open source our large-scale data setthat closely replicates the real-world automotive scenario for multipleinterference cases, allowing others to objectively compare their future work inthis domain. Our data set is available for download at:http://github.com/ristea/arim-v2.

Estimating the Magnitude and Phase of Automotive Radar Signals under Multiple Interference Sources with Fully Convolutional Networks