3 papers
eess.SP2026
SMCNet: Supervised Surface Material Classification Using mmWave Radar IQ Signals and Complex-valued CNNs
Stefan Hägele, Fabian Seguel, Driton Salihu +2
Understanding surface material properties is crucial for enhancing indoor robot perception and indoor digital twinning. However, not all sensor modalities typically employed for th…
eess.SP2026
ACCOR: Attention-Enhanced Complex-Valued Contrastive Learning for Occluded Object Classification Using mmWave Radar IQ Signals
Stefan Hägele, Adam Misik, Constantin Patsch +1
Millimeter-wave (mmWave) radar provides robust sensing under adverse conditions and can penetrate thin materials for non-visual perception in industrial and robotic settings. Recen…
eess.SP2026
RadarFuseNet: Complex-Valued Cross-Attention Fusion of Time-Frequency IQ Radar Features for Robust Classification
Stefan Hägele, Adam Misik, Eckehard Steinbach
Millimeter-wave (mmWave) radar has emerged as a compact and powerful sensing modality for advanced perception tasks that leverage machine learning. It is particularly effective in…