发表机构
Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SCORE-LM结合紧凑雷达编码器与语言模型，实现高精度故障诊断并生成可理解的维护指导，在八项记录上以88.39%召回率领先，参数减少119.7倍。
AI 中文摘要
雷达硬件故障威胁自动化感知，促使需要准确、紧凑的诊断以及可理解的维护指导。我们提出了SCORE-LM，它将一个小型的散射体条件算子响应编码器（SCORE）与一个适配的本地语言模型相结合。SCORE结合了自参考复轨迹、物理描述符以及一个选择性状态空间分支，并采用仅源域自监督和方向性故障推断。在八个排除捕获的Rad-R故障记录上，它在评估的九模型比较中达到了最先进的性能：在十帧下，平均捕获召回率为88.39%，四故障宏F1为88.20%。其39,520个雷达推理系数比RadrNet-DS-CI少119.7倍，而召回率比这个最强竞争对手高出15.56个百分点。在另一个低标签协议中，SCORE在每类仅一个带标签源窗口的情况下达到71.58%的召回率。一个非线性投影器将四个冻结的故障相似度转换为五个软令牌，将紧凑诊断与类别条件维护指导联系起来。在涵盖24个雷达窗口的75个开发问题上，相对于移除共同训练的适配器，语言适配将正确故障答案从45提高到62（从60.0%提高到82.7%），同时保留相同的投影器。因此，SCORE-LM将一个紧凑的雷达专家与一个语言接口相结合，用于传达特定于故障的检查指导。
英文摘要
Radar hardware faults threaten automated perception, motivating accurate, compact diagnosis and understandable maintenance guidance. We introduce SCORE-LM, which couples a small scatterer-conditioned operator-response encoder (SCORE) to an adapted local language model. SCORE combines self-referenced complex trajectories, physical descriptors, and a selective state-space branch, with source-only self-supervision and directional fault inference. On eight capture-excluded Rad-R fault recordings, it achieves state-of-the-art performance within the evaluated nine-model comparison: 88.39% mean capture recall and 88.20% four-fault macro-F1 at ten frames. Its 39,520 radar inference coefficients are 119.7 times fewer than RadrNet-DS-CI's, while recall is 15.56 percentage points higher than this strongest competitor. In a separate low-label protocol, SCORE reaches 71.58% recall with one labeled source window per class. A nonlinear projector converts four frozen fault similarities into five soft tokens, linking compact diagnosis to class-conditioned maintenance guidance. On 75 development questions covering 24 radar windows, language adaptation raises correct-fault answers from 45 to 62 (60.0% to 82.7%) relative to removing the co-trained adapters, while retaining the same projector. SCORE-LM thus combines a compact radar specialist with a language interface for communicating fault-specific inspection guidance.
