Micro‐Doppler Signal Recognition for UAV Weak Targets Based on Improved MMFFNet

ABSTRACT

Aiming at the problems that the original Multi‐band Multi‐angle Feature Fusion Network (MMFFNet) is easily disturbed by noise and adopts a fixed multi‐band feature fusion mode under low signal‐to‐noise ratio (SNR), a UAV weak target micro‐Doppler signal recognition method based on improved MMFFNet is proposed. This method constructs a noise‐aware attention mechanism by integrating Convolutional Block Attention Module (CBAM) and Squeeze‐and‐Excitation (SE) attention, which suppresses noise‐induced misfocus and enhances effective target features. Meanwhile, an SNR‐adaptive Attentional Feature Fusion (AFF) module is designed to dynamically adjust the weights of multi‐band features according to the noise level. Experimental results demonstrate that the proposed method achieves a validation accuracy of 91.65% at −3 dB SNR, which outperforms the original MMFFNet by 6.59%. This work can serve as an effective technical support for robust radar identification of low, slow, small (LSS) UAV targets.