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cs.LG2026
Constrained Reinforcement Learning Using Successor Representations
Michael Girstl, Alexander Mattick, Christopher Mutschler
Real-world Reinforcement Learning depends on the ability to formulate safety constraints into a policy. A common way to model such constraints is to introduce an additional cost si…
eess.SP2026
The S-ICDF Dataset: Sionna-Simulated Dynamic Interference Characterization and Direction Finding
Christian Wielenberg, Lucas Heublein, Jonathan Ott +8
Jamming and spoofing threaten wireless and satellite navigation by disrupting or manipulating radio frequency (RF) signals, undermining availability, integrity, and trust. Robust i…
eess.SP2026
Active Sensing with Meta-Reinforcement Learning for Emitter Localization from RF Observations
M. Shamail J. Khan, Nisha L. Raichur, Lucas Heublein +5
Global navigation satellite system (GNSS) interference poses a serious threat to reliable positioning, especially in indoor and multipath-rich environments where source localizatio…