用小语言模型让雷达听懂人话，自动选处理算法
Small Language Model enabled Autonomous agent for Language-Conditioned Cognitive Radar
- 用小语言模型解析自然语言指令，驱动雷达信号处理
- 在多种场景下准确完成波束成形、干扰抑制等任务
- 适合雷达系统智能化研发人员和跨领域研究者
现代雷达需根据干扰、杂波和数据可用性动态调整处理策略。本文提出一种基于小型语言模型（SLM）的自主代理框架，用于语言控制的认知雷达，作为一系列阵列信号处理工具的智能控制器。给定自然语言指令后，该代理可提取与雷达操作相关的线索，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。在合成均匀线阵（ULA）雷达上的实验表明，面对自然语言指令，该代理能在侧瓣控制、干扰抑制、多零点波束成形、相干源处理及低快拍方向到达（DOA）估计等多种场景中实现有意义的算法选择。消融实验证明，雷达专用提示工程与物理约束的工具执行是确保可靠决策和无幻觉数值结果的必要条件。
原文摘要 · Abstract (English)
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.
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