58 citations · 237 across the 39 of their papers we have counts for
10 papers · 1 filter
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…
"UWBCarGraz" Dataset for Car Occupancy Detection using Ultra-Wideband Radar
Jakob Möderl, Stefan Posch, Franz Pernkopf +1
We present a data-driven car occupancy detection algorithm using ultra-wideband radar based on the ResNet architecture. The algorithm is trained on a dataset of channel impulse res…
Fast Variational Block-Sparse Bayesian Learning
Jakob Möderl, Erik Leitinger, Bernard H. Fleury +2
We propose a variational Bayesian (VB) implementation of block-sparse Bayesian learning (BSBL) to compute proxy probability density functions (PDFs) that approximate the posterior…
Variational Inference of Structured Line Spectra Exploiting Group-Sparsity
Jakob Möderl, Franz Pernkopf, Klaus Witrisal +1
In this paper, we present a variational inference algorithm that decomposes a signal into multiple groups of related spectral lines. The spectral lines in each group are associated…
Variational Message Passing-Based Respiratory Motion Estimation and Detection Using Radar Signals
Jakob Möderl, Erik Leitinger, Franz Pernkopf +1
We present a variational message passing (VMP) approach to detect the presence of a person based on their respiratory chest motion using multistatic ultra-wideband (UWB) radar. In…
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…