Large Language Models Can Achieve Explainable and Training-Free One-shot HRRP ATR

Lingfeng Chen; Panhe Hu; Zhiliang Pan; Qi Liu; Zhen Liu

doi:10.1109/lsp.2025.3598220

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arXiv · 2506.02465

Large Language Models Can Achieve Explainable and Training-Free One-shot HRRP ATR

Lingfeng Chen·Panhe Hu·Zhiliang Pan·Qi Liu·Zhen Liu

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Abstract

This letter introduces a pioneering, training-free and explainable framework for High-Resolution Range Profile (HRRP) automatic target recognition (ATR) utilizing large-scale pre-trained Large Language Models (LLMs). Diverging from conventional methods requiring extensive task-specific training or fine-tuning, our approach converts one-dimensional HRRP signals into textual scattering center representations. Prompts are designed to align LLMs' semantic space for ATR via few-shot in-context learning, effectively leveraging its vast pre-existing knowledge without any parameter update. We make our codes publicly available to foster research into LLMs for HRRP ATR.

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Lingfeng Chen, Panhe Hu, Zhiliang Pan, Qi Liu, Zhen Liu. 2025-06-03. Large Language Models Can Achieve Explainable and Training-Free One-shot HRRP ATR. https://doi.org/10.1109/lsp.2025.3598220

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