most citedSMCNet: Supervised Surface Material Classification Using mmWave Radar IQ Signals and Complex-valued CNNs

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eess.SP2026★ 2 cited

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.SP2025

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…