发表机构
University of Alabama（阿拉巴马大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种小型语言模型驱动的自主智能体框架，作为语言条件认知雷达的智能控制器，经实验验证可在多种雷达场景下完成算法选择，且雷达特定提示与基于物理的工具执行是可靠决策的必要条件。
AI 中文摘要
现代雷达系统需根据变化的干扰、杂波及数据可用性调整处理策略。本文提出一种小型语言模型（SLM）驱动的自主智能体框架，用于语言条件认知雷达，作为阵列信号处理工具套件的智能控制器。给定自然语言指令后，该智能体提取雷达操作相关线索，选择合适的信号处理方法序列，配置参数并调用可执行工具进行数值计算。针对合成均匀线性阵列（ULA）雷达的实验表明，给定自然语言指令时，该智能体可在多种场景下完成有意义的算法选择，涵盖旁瓣控制、干扰抑制、多零陷波束形成、相干源处理及快拍数低情况下的波达方向（DOA）估计。 ablation结果显示，雷达特定提示与基于物理的工具执行对可靠决策及无幻觉数值结果均为必要条件。
英文摘要
Modern radar systems require adapting their processing strategies in response to changing interference, clutter, and data availability. This paper introduces a framework for a small language model (SLM)-driven autonomous agent designed for language-conditioned cognitive radar, functioning as an intelligent controller for a suite of array signal processing tools. Given a natural-language command, the agent extracts radar-operation-related cues, selects an appropriate sequence of signal-processing methods, configures parameters, and invokes executable tools for numerical computation. Experiments with a synthetic uniform linear array (ULA) radar demonstrate that, given a natural-language command, the agent performs meaningful algorithm selection across diverse scenarios for sidelobe control, jammer suppression, multiple-null beamforming, coherent-source handling, and low-snapshot direction-of-arrival (DOA) estimation. Ablation results show that radar-specific prompting and physics-grounded tool execution are both required for reliable decisions and hallucination-free numerical results.
CommentsAccepted at MLSP 2026, ATL, USA
