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Found 6 papers, 1 papers with code
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.
Quantized Neural Networks for Radar Interference Mitigation
Radar sensors are crucial for environment perception of driver assistance systems as well as autonomous vehicles.
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.
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.
On Efficient Uncertainty Estimation for Resource-Constrained Mobile Applications
Deep neural networks have shown great success in prediction quality while reliable and robust uncertainty estimation remains a challenge.
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.
