UAV Identification via Multiscale Decision Fusion CNN Utilizing Micro-Doppler Features

With the rapid expansion of the low-altitude economy, the supervision of low-altitude unmanned aerial vehicles (UAVs) is encountering increasingly complex challenges, with the accurate identification of UAVs emerging as a critical issue. Radar systems, owing to their robustness against external interference, are frequently integrated with other technologies to enhance UAV identification capabilities. This study introduces a novel UAV radar signal identification approach utilizing a multi-scale residual convolutional neural network (CNN). By combining time-domain features, time-frequency domain features, and range-Doppler features from a frequency-modulated continuous-wave radar (FMCWR), and employing decision-level fusion techniques to extract multi-scale characteristics, the proposed method significantly enhances feature representation. Experimental results conclusively demonstrate that this fusion strategy achieves a identification accuracy of 94%.