no code implementations • 23 May 2024 • Mate Toth, Erik Leitinger, Klaus Witrisal
Algorithms for joint mutual interference mitigation and object parameter estimation are a key enabler for automotive applications of frequency-modulated continuous wave (FMCW) radar.
no code implementations • 18 Dec 2023 • Christian Oswald, Mate Toth, Paul Meissner, Franz Pernkopf
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.
no code implementations • 15 Dec 2023 • Christian Oswald, Mate Toth, Paul Meissner, Franz Pernkopf
In this paper we propose a new method for training neural networks (NNs) for frequency modulated continuous wave (FMCW) radar mutual interference mitigation.
no code implementations • 25 Jan 2022 • Johanna Rock, Wolfgang Roth, Mate Toth, Paul Meissner, Franz Pernkopf
We analyze the quantization of (i) weights and (ii) activations of different CNN-based model architectures.
no code implementations • 29 Apr 2021 • Alexander Fuchs, Johanna Rock, Mate Toth, Paul Meissner, Franz Pernkopf
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.
no code implementations • 4 Dec 2020 • Johanna Rock, Mate Toth, Paul Meissner, Franz Pernkopf
We combine real measurements with simulated interference in order to create input-output data suitable for training the model.
1 code implementation • 24 Jun 2019 • Johanna Rock, Mate Toth, Elmar Messner, Paul Meissner, Franz Pernkopf
Driver assistance systems as well as autonomous cars have to rely on sensors to perceive their environment.