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Found 7 papers, 1 papers with code
Angle-Equivariant Convolutional Neural Networks for Interference Mitigation in Automotive Radar
In automotive applications, frequency modulated continuous wave (FMCW) radar is an established technology to determine the distance, velocity and angle of objects in the vicinity of the vehicle.
End-to-End Training of Neural Networks for Automotive Radar Interference Mitigation
In this paper we propose a new method for training neural networks (NNs) for frequency modulated continuous wave (FMCW) radar mutual interference mitigation.
Resource-efficient Deep Neural Networks for Automotive Radar Interference Mitigation
We analyze the quantization of (i) weights and (ii) activations of different CNN-based model architectures.
Complex-valued Convolutional Neural Networks for Enhanced Radar Signal Denoising and Interference Mitigation
Our experiments show, that the use of CVCNNs increases data efficiency, speeds up network training and substantially improves the conservation of phase information during interference removal.
Deep Interference Mitigation and Denoising of Real-World FMCW Radar Signals
We combine real measurements with simulated interference in order to create input-output data suitable for training the model.
Quantized Neural Networks for Radar Interference Mitigation
Radar sensors are crucial for environment perception of driver assistance systems as well as autonomous vehicles.
Complex Signal Denoising and Interference Mitigation for Automotive Radar Using Convolutional Neural Networks
Driver assistance systems as well as autonomous cars have to rely on sensors to perceive their environment.
