58 citations · 113 across the 8 of their papers we have counts for
8 papers
Variational Signal Separation for Automotive Radar Interference Mitigation
Mate Toth, Erik Leitinger, Klaus Witrisal
Algorithms for mutual interference mitigation and object parameter estimation are a key enabler for automotive applications of frequency-modulated continuous wave (FMCW) radar. In…
Angle-Equivariant Convolutional Neural Networks for Interference Mitigation in Automotive Radar
Christian Oswald, Mate Toth, Paul Meissner +1
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 o…
End-to-End Training of Neural Networks for Automotive Radar Interference Mitigation
Christian Oswald, Mate Toth, Paul Meissner +1
In this paper we propose a new method for training neural networks (NNs) for frequency modulated continuous wave (FMCW) radar mutual interference mitigation. Instead of training NN…
Resource-efficient Deep Neural Networks for Automotive Radar Interference Mitigation
Johanna Rock, Wolfgang Roth, Mate Toth +2
Radar sensors are crucial for environment perception of driver assistance systems as well as autonomous vehicles. With a rising number of radar sensors and the so far unregulated a…
Complex-valued Convolutional Neural Networks for Enhanced Radar Signal Denoising and Interference Mitigation
Alexander Fuchs, Johanna Rock, Mate Toth +2
Autonomous driving highly depends on capable sensors to perceive the environment and to deliver reliable information to the vehicles' control systems. To increase its robustness, a…
Deep Interference Mitigation and Denoising of Real-World FMCW Radar Signals
Johanna Rock, Mate Toth, Paul Meissner +1
Radar sensors are crucial for environment perception of driver assistance systems as well as autonomous cars. Key performance factors are a fine range resolution and the possibilit…
Quantized Neural Networks for Radar Interference Mitigation
Johanna Rock, Wolfgang Roth, Paul Meissner +1
Radar sensors are crucial for environment perception of driver assistance systems as well as autonomous vehicles. Key performance factors are weather resistance and the possibility…
Complex Signal Denoising and Interference Mitigation for Automotive Radar Using Convolutional Neural Networks
Johanna Rock, Mate Toth, Elmar Messner +2
Driver assistance systems as well as autonomous cars have to rely on sensors to perceive their environment. A heterogeneous set of sensors is used to perform this task robustly. Am…