TSAR-Net: Transfer-Enhanced Local-to-Global Representation Learning for Micro-Doppler Classification of Small Aerial Targets

Radar remote sensing provides an effective means of analyzing target characteristics for low-altitude surveillance, and the rapid growth and misuse of drones highlight the need for efficient classification of small aerial targets such as drones and birds. Micro-motions, such as rotor rotation or wing flapping, induce characteristic frequency shifts in radar echoes known as micro-Doppler effects. These radar-derived features provide essential structural and kinematic information for deep-learning-based target classification. However, in practical applications, existing methods still suffer from inadequate micro-Doppler feature extraction capability and rarely account for small-sample scenarios, both of which limit their classification performance. To address these challenges, this article proposes a transfer learning and self-attention enhanced ResNet50 (TSAR-Net), which combines transferred hierarchical convolutional features with multi-head self-attention to jointly exploit local and global time–frequency characteristics. Furthermore, to alleviate the small-sample classification problem, we introduce a projection-based TSAR-Net (PTSAR-Net), which maps the 2048-dimensional pooled feature through a 512-dimensional layer to a compact 128-dimensional representation for limited-label classification. On the measured DIAT-μSAT dataset, TSAR-Net achieves 99.59% accuracy, compared with 96.71% for VGG19, an improvement of 2.88 percentage points. With five training samples per class, PTSAR-Net achieves 81.14% accuracy, compared with 70.72% for TSAR-Net, an improvement of 10.42 percentage points.