UAV Classification Based on Deep Learning Fusion of Multi-Dimensional UAV Micro-Doppler Image Features

In the realm of expanding unmanned aerial vehicle (UAV) applications and types, the precision of UAV target classification is of paramount importance. Deep learning has emerged as the linchpin of such endeavors. A new approach based on deep learning fusion technique is proposed by our team, which integrates frequency modulated continuous wave (FMCW) radar micro-Doppler signals, cadence-velocity diagram (CVD) signals and cepstrum (CEP) signals. This synthesis culminates in UAV classification with exceptional accuracy, surpassing 97%. In this paper, two deep learning fusion approaches leveraging the ResNet34 network were employed: data-level fusion and feature-level fusion. Empirical results unequivocally highlight the potency of deep learning information fusion—most notably, the fusion of the three spectrograms—exceeding 97% accuracy. This firmly underscores the pivotal role that deep learning fusion techniques play in amplifying precision in UAV target classification.