The S-ICDF Dataset: Sionna-Simulated Dynamic Interference Characterization and Direction Finding
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, ha...
| Authors | Christian Wielenberg, Lucas Heublein, Jonathan Ott et al. |
|---|---|
| Primary category | eess.SP |
| Published | 3 July 2026 |
| Last updated at source | 3 July 2026 |
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