activity
20242026
most citedEvaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies

12 citations · 20 across the 5 of their papers we have counts for

collaborators
Showing 2026Show all

2 papers · 1 filter

eess.SP2026

Analyzing and Characterizing Multi-Source Interference Effects at Jammertest Norway 2025

Lucas Heublein, Inigo Cortes Vidal, Tobias Feigl +2

Intentional radio-frequency interference from low-cost GNSS jammers increasingly threatens the accuracy and reliability of satellite-based positioning. Mitigating this threat requi…

cs.LG2026★ 1 cited

GenAI for Energy-Efficient and Interference-Aware Compressed Sensing of GNSS Signals on a Google Edge TPU

Thorben Wegner, Lucas Heublein, Tobias Feigl +3

Traditional methods for classifying global navigation satellite system (GNSS) jamming signals typically involve post-processing raw or spectral data streams, requiring complex and…