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

12 citations · 12 across the 2 of their papers we have counts for

collaborators

6 papers

eess.SP2025

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

Lucas Heublein, Thorsten Nowak, Tobias Feigl +2

Jamming devices disrupt signals from the global navigation satellite system (GNSS) and pose a significant threat by compromising the reliability of accurate positioning. Consequent…

eess.SP2025

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

Lucas Heublein, Christian Wielenberg, Thorsten Nowak +3

Jamming devices disrupt signals from the global navigation satellite system (GNSS) and pose a significant threat by compromising the reliability of accurate positioning. Consequent…

cs.CV2025

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization

Nisha Lakshmana Raichur, Lucas Heublein, Christopher Mutschler +1

Indoor positioning based on 5G data has achieved high accuracy through the adoption of recent machine learning (ML) techniques. However, the performance of learning-based methods d…

cs.LG2025

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

Lucas Heublein, Simon Kocher, Tobias Feigl +3

Distributed learning and Edge AI necessitate efficient data processing, low-latency communication, decentralized model training, and stringent data privacy to facilitate real-time…

cs.CV202512 cited

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…

cs.AI2025

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

Harshith Manjunath, Lucas Heublein, Tobias Feigl +1

Large language models (LLMs) are advanced AI systems applied across various domains, including NLP, information retrieval, and recommendation systems. Despite their adaptability an…