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Session B3: AI and Machine Learning for GNSS Signal Processing, Error Modeling, and Threat Detection

Analyzing and Characterizing Multi-Source Interference Effects at Jammertest Norway 2025
Lucas Heublein, Inigo Cortes Vidal, Tobias Feigl, Alexander Rügamer, Felix Ott, Fraunhofer Institute for Integrated Circuits IIS
Location: Windsong 7-8
Date/Time: Thursday, Sep. 17, 8:57 a.m.

Intentional radio-frequency interference from low-cost GNSS jammers increasingly threatens the accuracy and reliability of satellite-based positioning. Mitigating this threat requires not only detection but also robust waveform classification and characterization, direction-of-arrival inference for localization, and impact estimation on receiver performance under realistic operating conditions; all of which are challenged by strong distribution shifts across devices, sensors, environments, and satellite geometries. We address these challenges by compiling a dedicated real-world dataset recorded during Jammertest 2025 (Andoya, Norway), covering two outdoor test areas with parallel measurements from a single-antenna E1/E5 receiver module and a 2x2 CRPA-array. The dataset spans diverse jamming, spoofing, and meaconing scenarios, including CW, PRN, sweep/chirp, and multi-emitter configurations, and is complemented by per-recording metadata enabling time-aligned ground truth. Methodologically, we benchmark 17 machine learning (ML) architectures for interference-modulation recognition and multi-task characterization (type, occupied bandwidth, and signal strength), and we quantify navigation-relevant degradation via a receiver-aware spectral separation coefficient (SSC) that maps measured spectra to effective (C_s/N_0)_eff loss. To enable multi-source analysis, we transfer a YOLOv8s and RF-DETR detector pretrained on a labeled spectrogram dataset to localize multiple simultaneous interferers in GNSS spectrograms and subsequently characterize each detected component. Results show near-99\% within-area classification accuracy but substantial cross-area performance drops, highlighting the need for robustness to real-world domain shifts. Overall, the proposed dataset and end-to-end, impact-aware pipeline provide a practical foundation for scalable multi-source interference monitoring and robust GNSS navigation in the field.

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