Teaching large language models to see in radar: aspect-distributed prototypes for few-shot HRRP ATR

High-resolution range profiles (HRRPs) play a critical role in automatic target recognition (ATR) due to their rich information regarding target scattering centers (SCs), which encapsulate the geometric and electromagnetic characteristics of the target. Under few-shot circumstances, traditional learning-based methods often suffer from overfitting and struggle to generalize effectively. The recently proposed HRRPLLM, which leverages the in-context learning (ICL) capabilities of large language models (LLMs) for one-shot HRRP ATR, is limited in few-shot scenarios. This limitation arises because it primarily utilizes the distribution of SCs for recognition while neglecting the variance of the samples caused by aspect sensitivity. This paper proposes a straightforward yet effective Aspect-Distributed Prototype (ADP) strategy for LLM-based ATR under few-shot conditions to enhance aspect robustness. Experiments conducted on both simulated and measured aircraft electromagnetic datasets demonstrate that the proposed method significantly outperforms current benchmarks.