发表机构
M S Ramaiah University of Applied Sciences; Bapatla Engineering College（MS Ramaiah应用科学大学; 巴普拉工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用CLIP等视觉语言模型从辐射方向图图像提取特征，结合机器学习分类与回归模型推断天线参数，在RadPat 50K数据集上实现超过70%的准确率，展示了多模态AI在天线分析中的潜力。
AI 中文摘要
观测到的辐射方向图通常作为天线行为的主要证据，但将其转化为有意义的解读是一项非平凡且高度依赖专业知识的任务。这种需求促使了自动化方向图解读的发展，这是一个在射频（RF）监视、非合作辐射源表征和空中（OTA）测试等应用中遇到的诊断问题。本工作研究了对比语言-图像预训练（CLIP）及其他近期视觉语言模型在此类诊断中的应用。这些模型分析多模态数据，直接从辐射方向图图像中提取判别性特征，以训练机器学习（ML）方法对天线的几何和性能参数进行有意义的推断。使用RadPat 50K（一个为均匀线性阵列（ULAs）生成的辐射方向图图像合成数据集）展示了三种此类视觉语言模型的性能。辐射方向图图像由视觉语言模型处理以获得判别性特征。CLIP提取的特征由ML分类模型处理以推断天线参数，如单元数量、单元间距、加权方案、栅瓣存在性和转向角，或由ML回归模型处理以推断波束宽度、方向性和主瓣方向等参数。ML模型实现了超过70%的准确率，凸显了多模态人工智能（AI）在直观天线分析方面的潜力。
英文摘要
Observed radiation patterns often serve as a primary evidence of antenna's behavior, but translating them into meaningful interpretations is a nontrivial and expertise intensive task. This demand necessitates automated pattern interpretation, a diagnosis problem encountered in applications encompassing Radio Frequency (RF) surveillance, non cooperative emitter characterization and Over The Air (OTA) testing. This work addresses the incorporation of Contrastive Language Image Pre training (CLIP) and other recent vision language models to perform such a diagnosis. These models analyze the multimodal data and extract discriminative features directly from radiation pattern images, to train Machine Learning (ML) approaches for meaningful inferences on geometrical and performance parameters of the antenna. Performance of three such vision language models is demonstrated using RadPat 50K, a synthetic dataset of radiation pattern images generated for uniform linear arrays (ULAs). The radiation pattern images are processed by the vision language models to obtain discriminative features. The CLIP extracted features are processed by either ML classification models to infer antenna parameters number of elements, element spacing, weighting scheme, presence of grating lobe and steering angle, or ML regression models to infer parameters beam width, directivity and main lobe direction. ML models achieve accuracies exceeding 70 percent, highlighting the potential of multimodal Artificial Intelligence (AI) towards intuitive antenna analysis.
Comments10 pages, 3 Figures, 9 Tables
