Year: 2025
Type: article
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.
Source: IEEE Signal Processing Letters
Authors: Lingfeng Chen, Panhe Hu, Zhiliang Pan, Qi Liu, Shuanghui Zhang, Zhen Liu
Institution: National University of Defense Technology
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Award: G5851501073
OpenAlex ID: W4413155532