S-ICDF数据集：基于Sionna仿真的动态干扰表征与测向数据集
The S-ICDF Dataset: Sionna-Simulated Dynamic Interference Characterization and Direction Finding
AI总结：
针对干扰监测数据集稀缺问题，基于Sionna生成含102种干扰配置的大规模S-ICDF数据集，用经典测向方法与现代ML方法完成基准测试，支撑无线干扰相关算法研发。
摘要：
干扰与欺骗会扰乱或操控射频（RF）信号，威胁无线通信与卫星导航系统，损害服务可用性、完整性与可信度。因此，鲁棒的干扰监测（即检测、分类、表征与测向）是识别与定位异常信号的核心需求。机器学习（ML）有望在复杂环境下实现更优性能，但其研发与验证依赖可捕捉真实信号与信道变化特性的大规模数据集。由于有意干扰属于违法行为，且传播、硬件与环境效应会干扰真实值标定，在现实场景中采集这类数据难度极大。为填补这一空白，本文构建并发布S-ICDF数据集，这是一个基于GPU加速的物理层无线通信仿真库Sionna生成的大规模室内干扰数据集。S-ICDF覆盖102种干扰配置，包含多样化的天线阵列方向图、带宽以及噪声水平、反射深度等仿真设置。研究团队还采用经典估计与测向（DF）方法（MUSIC、ESPRIT、CAPON）以及现代机器学习方法对S-ICDF开展基准测试，提供了基线结果。该数据集可通过以下公开链接获取：this https URL
英文摘要：
Jamming and spoofing threaten wireless and satellite navigation by disrupting or manipulating radio frequency (RF) signals, undermining availability, integrity, and trust. Robust interference monitoring (i.e., detection, classification, characterization, and direction finding) is therefore essential to identify and localize anomalous signals. While machine learning (ML) promises improved performance in complex environments, its development and validation depend on large-scale datasets that capture realistic signal and channel variability. Collecting such data in the real world is difficult because intentional jamming is illegal and ground-truth attribution is confounded by propagation, hardware, and environmental effects. To address this gap, we create and publish S-ICDF, a large-scale indoor interference dataset generated with Sionna, a GPU-accelerated simulation library for physical-layer wireless communications. S-ICDF covers 102 interference configurations, including diverse antenna array patterns, bandwidths, and simulation settings such as noise level and reflection depth. We further provide baseline results by benchmarking S-ICDF with classical estimation and direction finding (DF) methods (MUSIC, ESPRIT, and CAPON) and with modern ML approaches. The dataset is publicly available at: https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/sicdf_dataset
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
