UAVs and birds classification using robust coordinate attention synergy residual split-attention network based on micro-Doppler signature measurement by using L-band staring radar

Abstract

Developing unmanned aerial vehicles (UAVs) and birds surveillance technologies to produce accurate descriptions and achieve high classification accuracy is critical in the field of radar automatic target recognition (RATR). This article proposes a grayscale spectrogram image-based UAVs and birds classification method using a robust coordinate attention synergy residual Split-Attention network (RCA-ResNeSt) under the holographic staring radar system. Specifically, the ResNet structure with Split-Attention is used as an m-D feature extractor. The CrossNorm and SelfNorm (CNSN) mechanism is then incorporated into the network to advance generalization robustness. After that, to consider the spatial direction of the m-D signature, a coordinated attention (CA) mechanism is introduced at the tail end of the network to enable fine-grained mining of potential m-D features. Experiments are carried out using a designed radar system. The results show the superiority of the proposed method over existing approaches in classification accuracy and noise robustness.