AI-based Interference Detection

In an increasingly digitally connected world, technical systems across a wide range of industries rely on interference-free communication and precise signal processing. However, interference—whether intentional or unintentional—poses a growing challenge: it can disrupt processes, compromise safety, and cause economic damage.

Interference detection is therefore a key issue in areas such as mobility, aviation, defense, and critical infrastructure.

However, classic, rule-based methods reach their limits in real multipath environments: Reflections and signal mirroring distort measurement results and make reliable localization difficult.

Our solution takes a fundamentally different approach:

It is based on artificial intelligence (AI) that not only recognizes, but also learns, adapts, and improves with each implementation – even in complex environments with multipath effects. 

Pirkkl, J., Ott, J., Schelenz, L., Böhm, L., Eidloth, A., Feigl, T., Mutschler, C. (2026):

Democratizing Label-Free Localization: On-Device WiFi-RTT Channel charting

Schelenz, L., Ott, J., Pirkl, J., Pahl,  J., Feigl, T., Mutschler, C. (2026):

EchoNeRF. Time-Resolved RF Channel Synthesis with Neural Radiance Fields for Localization

Foroushani, M., Pirkl, J., Stahlke, M., Yammine, G., Feigl, T., Mutschler, C. (2026):

ChronoChart: Setup-Agnostic Channel Charting for Label-Free Indoor Localization

Heublein, L., Vidal, I., Feigl, T., Rügamer, A., Ott, F. (2026):

Analyzing and Characterizing Multi-Source Interference Effects at Jammertest Norway 2026 

in: arXiv: 2608.15819

Khan, S., Raichur, N., Heublein, L., Wielenberg, C., Mattick, A., Feigl, T., Mutschler, C., Ott, F. (2026):

Active Sensing with MEta-Reinforcement Learning for emitter Localization from RF Observations

in: arXiv: 2605.12569

Wielenberg, C., Heublein, L., Ott, J., Mattick, A., Raichur, N., Pirkl, J., Schelenz, L., Feigl, T., Yammine, G., Mutschler, C., Ott, F. (2026):

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

in: arXiv: 2607.03411

Raichur, N., Heublein, L., Seuß, D., Deinzer, F., Ott, F. (2026):

Physics-Informed Domain-Invariant Feature Learning with Autoencoder-Driven Gaussian Clustering for Robust Non-line-of-Sight Scenarios

in: arXiv: 2607.02537

Heublein, L., Deutel, M., Plinge, A., Ott, F. (2026): 

Efficient Network Inference via Hardware-Aware Architecture Search, Model Prunning & Quantization

in: arXiv: 2606.23210

Heublein, L., Benschuh, F., Rügamer, A., Ott, F. (2026):

Receiver-Aware Analysis and Verification of the Spectral Separation Coefficient Under Interference-Induced Degradation

in: arXiv: 26.06.18196

Wielenberg, C., Ott, F., Heublein, L., Pahl, J., Mutschler, C., Feigl, T., Yammine, G., Seitz, J., and Schwerna, P., (2026):

Simulationsgestützte Funkpeilung in Mehrwegeumgebungen

In: DWT Angewandte Forschung für Verteidigung und Sicherheit

Raichur, N. L., Heublein, L., Pahl, J., Seuß, D., Mutschler, C., & Ott, F. (2026):

Adaptive & Efficient Sample Selection for Domain Incremental 5G Indoor Localization in Dynamic Multipath Environments

In: IEEE Journal of Indoor and Seamless Positioning and Navigation  (J-ISPIN), doi: 10.1109/JISPIN.2025.3648680, January 2026

Heublein, L., Feigl, T., Rügamer, A., Mutschler, C., and Ott, F. (2026):

Variational and Generative Models with Quantization for Disentanglement and Compressed Sensing of GNSS Spectrograms

In: IEEE Journal of Indoor and Seamless Positioning and Navigation (J-ISPIN), doi:10.1109/JISPIN.2026.3655630, January 2026

Ott, F., Karim, R., Heublein, L., Pahl, J., and Feigl, T., (2025):

Digitale Resilienz im Gefechtsfeld der Zukunft: Föderale KI-Sicherheit durch Digitales Watermarking

In: DWT Cyber Defence Conference

Heublein, L., Nowak, T., Feigl, T., Pahl, J., and Ott, F. (2025):

GNSS Jammer Direction Finding in Dynamic Scenarios Using an Inertial-based Multi-Antenna System

In: DGON Inertial Sensors and Applications (ISA), October 2025

Heublein, L., Wielenberg, C., Nowak, T., Feigl, T., Mutschler, C., and Ott, F. (2025):

Attention-Based Fusion of IQ and FFT Spectrograms with AoA Features for GNSS Jammer Localization

In: IEEE Radar Conference, doi:10.1109/RadarConf2559087.2025.11204959, Krakow, Poland, October 2025

Heublein, L., Kocher, S., Feigl, T., Rügamer, A., Mutschler, C., and Ott, F. (2025):

VAE-based Feature Disentanglement for Data Augmentation and Compression in Generalized GNSS Interference Classification

In: IEEE Intl. Conf. on Localization and GNSS (ICL-GNSS), doi:10.1109/ICL-GNSS65520.2025.11046129, June 2025

Ott, F., Heublein, L., Feigl, T., Rügamer, A. & Mutschler, C.:

Metric-Based Few-Shot Learning With Triplet Selection for Adaptive GNSS Interference Classification

In: IEEE Journal of Indoor and Seamless Positioning and Navigation, vol. 3, pp. 81-104, 2025, doi: 10.1109/JISPIN.2025.3562140, April 2025

Raichur, N., Heublein, L., Feigl, T., Nowak, T., Rügamer, A., Mutschler, C., & Ott, F. (2025):

Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning

In: Trans. on Machine Learning Research (TMLR), April 2025

Manjunath, H., Heublein, L., Feigl, T., & Ott, F. (2025):

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization

In: IEEE Wireless Communications and Networking Conference (WCNC) March 2025

Heublein, L., Feigl, T., Nowak, T., Rügamer, A., Mutschler, C., & Ott, F. (2025):

Evaluating ML Robustness in GNSS Interference Classification, Characterization & Localization 

In: IEEE Intl. Conf. on Localization and GNSS (ICL-GNSS)

Gaikwad, N. S., Heublein, L., Raichur, N. L., Feigl, T., Mutschler, C., & Ott, F. (2024):

Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification

In: IEEE/IFIP Network Operations and Management Symposium (NOMS) Mai 2024

Heublein, L., Feigl, T., Rügamer, A., & Ott, F. (2024):

Achieving Generalization in Orchestrating GNSS Interference Monitoring Stations Through Pseudo-Labeling

In: DGON Positioning and Navigation for Intelligent Transport Systems (POSNAV)

Ott, F., Heublein, L., Raichur, N. L., Feigl, T., Hansen, J., Rügamer, A., & Mutschler, C. (2024):

Few-Shot Learning with Uncertainty-Based Quadruplet Selection for Interference Classification in GNSS Data

In: 2024 International Conference on Localization and GNSS (ICL-GNSS) (pp. 1-7)

Heublein, L., Raichur, N. L., Feigl, T., Brieger, T., Heuer, F., Asbach, L., ... & Ott, F. (2024):

Evaluation of (Un-) Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies

In: Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024) (pp. 1260-1293)

Transparency, reproducibility, and open exchange with the research community are central components of our work in the field of AI-based interference detection. That is why we publish selected data sets, models, and methods in our own GitLab repository.