Scattering Aware Episodic Adaptation for Few-Shot HRRP ATR Using Large Language Models

High-resolution range profile (HRRP) provides compact one-dimensional electromagnetic scattering representations but faces challenges due to signal ambiguity and the scarcity of labeled data for unseen target classes. Recently, Large Language Models (LLMs) have demonstrated potential by leveraging pre-trained semantic knowledge via In-Context Learning (ICL). However, a fundamental modality gap persists: the semantic space of frozen LLMs, evolved from natural language, is inherently misaligned with the non-Euclidean manifolds of electromagnetic scattering, resulting in a lack of sensitivity to fine-grained signal discriminators. To bridge this gap via manifold alignment, we propose Scattering-aware Episodic Adaptation via Semantics (SEAS), a unified framework that enhances the reasoning capabilities of LLMs with domain-specific signal alignment. Specifically, we conceptualize a hybrid episodic adaptation mechanism that combines the implicit, attention-based reasoning of frozen LLMs with an explicit, gradient-based outer loop facilitated by Low-Rank Adaptation (LoRA). To bridge the gap between radar signals and textual semantics, we introduce the Scattering-Aware Episodic Construction (SEAC) pipeline, which transforms HRRP measurements into fidelity-validated instruction episodes. Extensive empirical validation demonstrates that SEAS effectively aligns internal semantic representations with radar signal manifolds. Notably, SEAS enables Qwen3-8B to outperform current LLM-based HRRP ATR baselines including DeepSeekV3.2-Exp and Qwen3-max, while our scaled-up variant achieves a 31.6% relative improvement over the strongest baseline.