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

Sep 21, 2026

Uploaded by Oliver

Jamming and spoofing disrupt wireless and satellite navigation by corrupting radio-frequency (RF) signals, threatening availability, integrity, and trust. Robust interference monitoring—covering detection, classification, characterization, and direction finding—is crucial for identifying and localizing anomalous transmissions. Because real-world data collection is constrained and ground truth is confounded, the work introduces S-ICDF, a large-scale indoor dataset generated with Sionna to capture realistic channel and signal variability. It includes 102 interference configurations with diverse antennas, bandwidths, noise, and reflection depth, plus baselines using classical and machine-learning methods.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

AccessFree to download

Abstract

Index Terms

I. Introduction

Contributions

Overview of GNSS interference datasets

The S-ICDF Dataset: Sionna-Simulated Dynamic Interference Characterization and Direction Finding Christian Wielenberg, Lucas Heublein, Jonathan Ott, Alexander Mattick, Nisha L. Raichur, Jonas Pirkl, Lukas Schelenz, Tobias Feigl, George Yammine, Christopher Mutschler, Felix Ott Fraunhofer Institute for Integrated Circuits IIS, 90411 N¨urnberg, Germany {christian.wielenberg, tobias.feigl, george.yammine, christopher.mutschler, [felix.ott](felix.ott}@iis.fraunhofer.de)[}](felix.ott}@iis.fraunhofer.de)[@iis.fraunhofer.de](felix.ott}@iis.fraunhofer.de) arXiv :2607 .03411v1 [ ee ss . SP] 3 Jul 2026 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](https://gitlab.cc-asp.fraunhofer.de/darcy) gnss/sicdf dataset Index Terms—Sionna, Simulation, Interference Monitoring, Characterization, Direction Finding, Localization, Dataset, IQ I. INTRODUCTION Jamming devices interfere with global navigation satellite system (GNSS) signals emitted by RF sources and represent a serious threat, undermining the robustness and reliability of both precise positioning and wireless communications [1]–[3] . This problem has grown severe in recent years as readily obtainable jammers have become increasingly common [4],[5] . As a result, interference detection, classification [6], characterization [7], localization [8], [9], and mitigation [10] have emerged as key research areas. Numerous solutions have been explored, spanning traditional signal processing techniques [11]–[13] as well as ML-based methods [14], [15] . However, training such ML-based methods (particularly with the emergence of large foundation models) demands large-scale datasets that not only match the target task, but also comprehensively cover and remain well-balanced across all parameters the model is intended to learn, ensuring that relevant conditions and variations are adequately represented rather than dominated by a small subset of frequent cases [16] . In the context of interference monitoring, this entails broad and uniform coverage of jammer characteristics, including waveform/type (e.g., continuous wave, noise, swept, pulsed), center frequency and bandwidth, temporal behavior (duty cycle, burst/sweep dynamics), transmit power or interferenceto-noise-ratio (INR), and spatial/propagation conditions (e.g., location and mobility, (non-)line-of-sight (LoS/NLoS), and multipath [7] . Collecting real-world recordings involving jamming is often legally restricted [17], and typically necessitates extensive measurement campaigns, such as the Jammertest in Norway [18] . Hence, our objective is to generate a simulated dataset and to benchmark ML models on this dataset to enable rapid m

FAQs

What problem does the S-ICDF dataset address?

It addresses the difficulty of developing and validating interference monitoring methods for jamming and spoofing by providing large-scale simulated data with realistic variability.

How is S-ICDF generated and what does it include?

S-ICDF is generated with Sionna using GPU-accelerated physical-layer simulation, covering 102 interference configurations with varied antenna patterns, bandwidths, noise levels, and reflection depth.

What baselines are provided for evaluation?

The document benchmarks S-ICDF with classical direction finding and estimation methods including MUSIC, ESPRIT, and CAPON, and also with modern machine-learning approaches.

Sep 21, 2026

Uploaded by Oliver

The S-ICDF Dataset - Sionna-Simulated Dynamic Interference Characterization and Direction Finding_Page 1

Open in WPS to view all 6 pages