Abstract
This paper investigates the use of micro-Doppler signatures of drones and birds for their detection and classification. Assessments made from simulated results are verified by data collected using a 10-GHz continuous wave (CW) radar system. Time/Velocity spectrograms generated for micro-Doppler analysis of multiple drones and birds are used for target identification and movement classification within TensorFlow. Results using Support Vector Machine (SVM) indicate 96% accuracy for drones vs. birds and 85% accuracy among individual drone and bird distinction between 5 classes.
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