The S-ICDF Dataset: Sionna-Simulated Dynamic Interference Characterization and Direction Finding

2026-07-03

AI Summary

This work addresses the scarcity of real-world interference data and the challenges in annotating such data for robust evaluation of anti-jamming techniques in wireless and satellite navigation systems. To this end, the authors present S-ICDF, the first large-scale indoor interference dataset generated using the GPU-accelerated Sionna physical-layer simulation framework. S-ICDF encompasses 102 distinct interference configurations combined with diverse channel models and antenna array setups, enabling research on interference detection, classification, feature extraction, and direction-of-arrival estimation. The study establishes comprehensive benchmarks by integrating classical direction-finding algorithms—MUSIC, ESPRIT, and CAPON—with modern machine learning approaches. Both the S-ICDF dataset and baseline performance results are publicly released to foster standardized, reproducible research in interference monitoring.

Abstract

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

Problem

• interference monitoring
• direction finding
• dataset
• jamming
• spoofing

Innovation

• S-ICDF dataset
• Sionna simulation
• interference direction finding
• GPU-accelerated RF simulation
• machine learning for GNSS