Felix Ott's picture
Real name: 
Felix Ott
First Name: 
Felix
Last Name: 
Ott
Affiliation: 
Fraunhofer Institute for Integrated Circuits IIS
Job Title: 
Postdoctoral Researcher
External links: 
Google Scholar
Short Bio: 
Felix Ott received his M.Sc. degree in Computational Engineering from Friedrich-Alexander University (FAU) Erlangen-Nürnberg in 2019. He subsequently joined the Self-Learning Systems group within the Machine Intelligence department at the Fraunhofer Institute for Integrated Circuits IIS. In 2023, he was awarded his Ph.D. from Ludwig-Maximilians University (LMU) Munich, where he conducted research in the Probabilistic Machine and Deep Learning group. He is currently serving as a postdoctoral researcher and project leader. His research focuses on representation learning, domain adaptation, and few-shot learning for GNSS-based interference monitoring.

Datasets & Competitions

GNSS Interference Spectrum Controlled Low-Frequency

Jamming devices present a significant threat by disrupting signals from the global navigation satellite system (GNSS), compromising the robustness of accurate positioning. The detection of anomalies within frequency snapshots is crucial to counteract these interferences effectively. A critical preliminary measure involves the reliable classification of interferences and characterization and localization of jamming devices.

Categories:
Artificial Intelligence
Signal Processing
Machine Learning
Sensors
Standards Research Data
113 Views

GNSS Interference Spectrum Highway Dataset 2

Jamming devices pose a significant threat by disrupting signals from the global navigation satellite system (GNSS), compromising the robustness of accurate positioning. Detecting anomalies in frequency snapshots is crucial to counteract these interferences effectively. The ability to adapt to diverse, unseen interference characteristics is essential for ensuring the reliability of GNSS in real-world applications. We recorded a dataset with our own sensor station at a German highway with two interference classes and one non-interference class.

Categories:
Artificial Intelligence
Signal Processing
Machine Learning
Sensors
Standards Research Data
193 Views

GNSS Interference Spectrum & Low-Cost Controlled Indoor Dataset

Interference signals degrade and disrupt Global Navigation Satellite System (GNSS) receivers, impacting their localization accuracy. Therefore, they need to be detected, classified, and located to ensure GNSS operation. State-of-the-art techniques employ supervised deep learning to detect and classify potential interference signals. We fuse both modalities only from a single bandwidth-limited low-cost sensor, instead of a fine-grained high-resolution sensor and coarse-grained low-resolution low-cost sensor.

Categories:
Artificial Intelligence
Signal Processing
Machine Learning
Sensors
Standards Research Data
390 Views

GNSS Interference Spectrum Highway Dataset 1

Jamming devices pose a significant threat by disrupting signals from the global navigation satellite system (GNSS), compromising the robustness of accurate positioning. Detecting anomalies in frequency snapshots is crucial to counteract these interferences effectively. The ability to adapt to diverse, unseen interference characteristics is essential for ensuring the reliability of GNSS in real-world applications. We recorded a dataset with our own sensor station at a German highway with eight interference classes and three non-interference classes.

Categories:
Artificial Intelligence
Signal Processing
Machine Learning
Sensors
Standards Research Data
150 Views