arXiv Machine Learning By Nisha L. Raichur, Lucas Heublein, Dominik Seu{\ss}, Frank Deinzer, Felix Ott

Physics-Informed Domain-Invariant Feature Learning with Autoencoder-Driven Gaussian Clustering for Robust Non-line-of-Sight Scenarios

Read the original on arXiv Machine Learning →

arXiv:2607. 02537v1 Announce Type: cross Abstract: Jamming and spoofing pose significant threats to wireless and satellite navigation by disrupting radio-frequency (RF) signals and compromising availability and integrity.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI
Jul 7

The S-ICDF Dataset: Sionna-Simulated Dynamic Interference Characterization and Direction Finding

arXiv:2607. 03411v1 Announce Type: cross Abstract: Jamming and spoofing threaten wireless and satellite navigation by disrupting or manipulating radio frequency (RF) signals, undermining availability, integrity, and trust.

By Christian Wielenberg, Lucas Heublein, Jonathan Ott, Alexander Mattick, Nisha L. Raichur, Jonas Pirkl, Lukas Schelenz, Tobias Feigl, George Yammine, Christopher Mutschler, Felix Ott
arXiv AI
Jul 7

Active Sensing with Meta-Reinforcement Learning for Emitter Localization from RF Observations

arXiv:2605. 12569v2 Announce Type: replace-cross Abstract: Global navigation satellite system (GNSS) interference poses a serious threat to reliable positioning, especially in indoor and multipath-rich environments where source localization is highly challenging.

By M. Shamail J. Khan, Nisha L. Raichur, Lucas Heublein, Christian Wielenberg, Alexander Mattick, Tobias Feigl, Christopher Mutschler, Felix Ott