2024/10/03 by Lucas Heublein, Tobias Feigl, Heublein, Lucas +5 · 2 citations
Engineering · #82-11 #94-05 #Artificial Intelligence (cs.AI) #E.0 #FOS: Computer and information sciences #FOS: Electrical engineering #GNSS positioning and interference #I.2.0 #I.5.1 #I.5.4 #Indoor and Outdoor Localization Technologies #Inertial Sensor and Navigation #Machine Learning (cs.LG) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2410.14686
openalex publication_date 2024/10/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The accuracy of global navigation satellite system (GNSS) receivers is significantly compromised by interference from jamming devices. Consequently, the detection of these jammers are crucial to mitigating such interference signals. However, robust classification of interference using machine learning (ML) models is challenging due to the lack of labeled data in real-world environments. In this paper, we propose an ML approach that achieves high generalization in classifying interference through orchestrated monitoring stations deployed along highways. We present a semi-supervised approach coupled with an uncertainty-based voting mechanism by combining Monte Carlo and Deep Ensembles that effectively minimizes the requirement for labeled training samples to less than 5% of the dataset while improving adaptability across varying environments. Our method demonstrates strong performance when adapted from indoor environments to real-world scenarios.