构建了用于无线干扰检测的仿真数据集S-ICDF，助力抗干扰系统研发。
The S-ICDF Dataset: Sionna-Simulated Dynamic Interference Characterization and Direction Finding
- 用Sionna仿真生成102种干扰配置，覆盖多种天线与信道条件。
- 在真实感强的数据上验证了经典与深度学习方向估计方法性能。
- 适合做抗干扰算法研究、卫星导航安全与无线系统测试的科研人员。
干扰和欺骗通过破坏或操纵射频信号威胁无线与卫星导航系统的可用性、完整性和可信度。因此，实现鲁棒的干扰监测（即检测、分类、表征和方向定位）至关重要。尽管机器学习有望在复杂环境中提升性能，但其发展依赖于涵盖真实信号与信道多样性的大规模数据集。然而，现实中获取此类数据极为困难：主动干扰属违法行为，且传播、硬件与环境因素使真实标签难以确认。为填补这一空白，本文利用Sionna——一个用于物理层无线通信的GPU加速仿真库——创建并发布了大规模室内干扰数据集S-ICDF。该数据集涵盖102种干扰配置，包括多样的天线阵列模式、带宽设置以及噪声水平与反射深度等仿真参数。我们进一步通过经典估计与方向查找方法（MUSIC、ESPRIT、CAPON）及现代机器学习方法对S-ICDF进行了基准测试，提供基线结果。数据集已公开：https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/sicdf_dataset
原文摘要 · Abstract (English)
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
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