Physics-Informed Semantic Prompt Learning for Few-Shot Low-Altitude Radar Target Recognition in Remote Sensing

Low-altitude radar target recognition is important for intelligent airspace monitoring, unmanned aerial vehicle (UAV) supervision, airport bird-strike prevention, and low-altitude remote sensing. Reliable recognition remains difficult because birds, balloons, and UAVs often produce weak radar responses, share similar trajectory-level signatures, and are difficult to annotate at scale. To address these challenges, this paper proposes a physics-informed semantic prompt learning framework for few-shot low-altitude radar target recognition. The framework converts radar point-track and track measurements into structured textual prompts that combine statistical descriptors, radar-domain physical knowledge, and task-specific instructions. A partially fine-tuned Generative Pre-trained Transformer 2 (GPT-2) encoder is then used to extract semantic representations that preserve motion and scattering-related information. An adaptive feature aggregation module further weights informative hidden states across temporal positions and semantic levels, and a relation-based meta-learning network models query-support similarity for few-shot classification. Experiments on a real low-altitude radar dataset with four target categories, namely birds, balloons, small rotary-wing UAVs, and light rotary-wing UAVs, show that the proposed method consistently outperforms conventional machine learning, deep learning, and representative few-shot baselines. Under the 20-shot setting, it achieves mean 90.85% precision, 90.47% recall, and 90.63% F1-score. The results indicate that embedding radar physical semantics into language-model-based representation learning can improve sample efficiency and recognition robustness for low-altitude radar remote sensing.