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20192026
most citedDomain Adaptation for Time-Series Classification to Mitigate Covariate Shift

36 citations · 178 across the 30 of their papers we have counts for

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Showing 2026Show all

12 papers · 1 filter

eess.SP2026

Permutation-Invariant Time-Frequency Segmentation and Parameter Estimation of Multiple Overlapping Interference Signals

Lucas Heublein, Christian Wielenberg, Christopher Mutschler +1

Radio-frequency interference threatens satellite navigation, wireless communications, radar, and industrial sensing. Reliable operation therefore requires spectrum situational awar…

eess.SP2026

Analyzing and Characterizing Multi-Source Interference Effects at Jammertest Norway 2025

Lucas Heublein, Inigo Cortes Vidal, Tobias Feigl +2

Intentional radio-frequency interference from low-cost GNSS jammers increasingly threatens the accuracy and reliability of satellite-based positioning. Mitigating this threat requi…

cs.CR2026

ZK-Trace: Certified Collusion Tracing with Zero-Knowledge Credentials for Federated GNSS Interference Monitoring

Redwanul Karim, Nisha L. Raichur, Lucas Heublein +3

Federated global navigation satellite system (GNSS) monitoring distributes a proprietary classifier to partly trusted stations, any of which may leak its copy. ZK-Trace combines pu…

stat.ML2026

CPDA: Class-Conditional Path Distribution Alignment for Unsupervised Time-Series Domain Adaptation

Felix Ott, Christopher Mutschler

Unsupervised time-series domain adaptation (DA) addresses the challenge of transferring a classifier from a labeled source domain to an unlabeled target domain under distribution s…

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…

cs.LG2026

Efficient Network Inference via Hardware-Aware Architecture Search, Model Pruning & Quantization

Lucas Heublein, Mark Deutel, Axel Plinge +1

Embedded global navigation satellite system (GNSS) interference monitoring requires fast and memory-efficient inference to process large volumes of raw in-phase and quadrature (IQ)…

eess.SP2026

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

Nisha L. Raichur, Lucas Heublein, Dominik Seuß +2

Jamming and spoofing pose significant threats to wireless and satellite navigation by disrupting radio-frequency (RF) signals and compromising availability and integrity. Robust RF…

eess.SP2026

Receiver-Aware Analysis and Verification of the Spectral Separation Coefficient Under Interference-Induced Degradation

Lucas Heublein, Fabian Benschuh, Alexander Rügamer +1

Interference poses a significant challenge to satellite-based positioning systems, making it essential to accurately quantify the effects of specific interference types on receiver…

cs.LG2026

PDRNN: Modular Data-driven Pedestrian Dead Reckoning on Loosely Coupled Radio- and Inertial-Signalstreams

Peter Bauer, Andreas Porada, Felix Ott +2

Modern pedestrian dead reckoning (PDR) systems rely on fusing noisy and biased estimates of position, velocity, and calibrated orientation derived from loosely coupled sensors to d…

cs.LG2026

Exploitation of Hidden Context in Dynamic Movement Forecasting: A Neural Network Journey from Recurrent to Graph Neural Networks and General Purpose Transformers

Lukas Schelenz, Shobha Rajanna, Denis Gosalci +6

Forecasting within signal processing pipelines is crucial for mitigating delays, particularly in predicting the dynamic movements of objects such as NBA players. This task poses si…

cs.LG2026★ 1 cited

GenAI for Energy-Efficient and Interference-Aware Compressed Sensing of GNSS Signals on a Google Edge TPU

Thorben Wegner, Lucas Heublein, Tobias Feigl +3

Traditional methods for classifying global navigation satellite system (GNSS) jamming signals typically involve post-processing raw or spectral data streams, requiring complex and…

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…