{"ID":2831907,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.06617","arxiv_id":"2512.06617","title":"Teaching large language models to see in radar: aspect-distributed prototypes for few-shot HRRP ATR","abstract":"High-resolution range profiles (HRRPs) play a critical role in automatic target recognition (ATR) due to their richinformationregarding target scattering centers (SCs), which encapsulate the geometric and electromagnetic characteristics of thetarget.Under few-shot circumstances, traditional learning-based methods often suffer from overfitting and struggle togeneralizeeffectively. The recently proposed HRRPLLM, which leverages the in-context learning (ICL) capabilities of largelanguagemodels (LLMs) for one-shot HRRP ATR, is limited in few-shot scenarios. This limitation arises because it primarilyutilizesthe distribution of SCs for recognition while neglecting the variance of the samples caused by aspect sensitivity. Thispaperproposes a straightforward yet effective Aspect-Distributed Prototype (ADP) strategy for LLM-based ATRunder few-shotconditions to enhance aspect robustness. Experiments conducted on both simulated and measured aircraft electromagneticdatasets demonstrate that the proposed method significantly outperforms current benchmarks.","short_abstract":"High-resolution range profiles (HRRPs) play a critical role in automatic target recognition (ATR) due to their richinformationregarding target scattering centers (SCs), which encapsulate the geometric and electromagnetic characteristics of thetarget.Under few-shot circumstances, traditional learning-based methods often...","url_abs":"https://arxiv.org/abs/2512.06617","url_pdf":"https://arxiv.org/pdf/2512.06617v2","authors":"[\"De Bi\",\"Chengbai Xu\",\"Lingfeng Chen\",\"Panhe Hu\"]","published":"2025-12-07T01:34:55Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}