12 citations · 20 across the 5 of their papers we have counts for
5 papers
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…
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…
Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies
Lucas Heublein, Nisha L. Raichur, Tobias Feigl +5
The accuracy and reliability of vehicle localization on roads are crucial for applications such as self-driving cars, toll systems, and digital tachographs. To achieve accurate pos…
Evaluating ML Robustness in GNSS Interference Classification, Characterization & Localization
Lucas Heublein, Tobias Feigl, Thorsten Nowak +3
Jamming devices disrupt signals from the global navigation satellite system (GNSS) and pose a significant threat, as they compromise the robustness of accurate positioning. The det…
Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning
Nisha L. Raichur, Lucas Heublein, Tobias Feigl +3
The primary objective of methods in continual learning is to learn tasks in a sequential manner over time (sometimes from a stream of data), while mitigating the detrimental phenom…