An adapted thin-wire model extended for bistatic OFDM sensing and multiple propellers produces micro-Doppler signatures that match drone measurements.
Multi-rotor Drone Micro-Doppler Simula- tion Incorporating Genuine Motor Speeds and Validation with L-band Staring Radar,
2 Pith papers cite this work. Polarity classification is still indexing.
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RadarCNN classifies indoor objects from radar IQ data at 97-99% accuracy, holding at ~50% under noise and occlusion.
citing papers explorer
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Modeling Micro-Doppler Signature of Multi-Propeller Drones in Distributed ISAC
An adapted thin-wire model extended for bistatic OFDM sensing and multiple propellers produces micro-Doppler signatures that match drone measurements.
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RadarCNN: Learning-based Indoor Object Classification from IQ Imaging Radar Data
RadarCNN classifies indoor objects from radar IQ data at 97-99% accuracy, holding at ~50% under noise and occlusion.