36 citations · 143 across the 25 of their papers we have counts for
5 papers · 1 filter
Achieving Generalization in Orchestrating GNSS Interference Monitoring Stations Through Pseudo-Labeling
Lucas Heublein, Tobias Feigl, Alexander Rügamer +1
The accuracy of global navigation satellite system (GNSS) receivers is significantly compromised by interference from jamming devices. Consequently, the detection of these jammers…
Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification
Nishant S. Gaikwad, Lucas Heublein, Nisha L. Raichur +3
Federated learning (FL) enables multiple devices to collaboratively train a global model while maintaining data on local servers. Each device trains the model on its local server a…
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…
Few-Shot Learning with Uncertainty-based Quadruplet Selection for Interference Classification in GNSS Data
Felix Ott, Lucas Heublein, Nisha Lakshmana Raichur +4
Jamming devices pose a significant threat by disrupting signals from the global navigation satellite system (GNSS), compromising the robustness of accurate positioning. Detecting a…