36 citations · 202 across the 35 of their papers we have counts for
36 papers
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…
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…
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…
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…
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…
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…
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…
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…
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…
Resilient Channel Charting Under Varying Radio Link Availability
Jonas Pirkl, Jonathan Ott, Maximilian Stahlke +3
Channel charting (CC) has become a key technology for RF-based localization, enabling unsupervised radio fingerprinting, even in non line of sight scenarios, with a minimum of refe…
Attention-Based Fusion of IQ and FFT Spectrograms with AoA Features for GNSS Jammer Localization
Lucas Heublein, Christian Wielenberg, Thorsten Nowak +3
Jamming devices disrupt signals from the global navigation satellite system (GNSS) and pose a significant threat by compromising the reliability of accurate positioning. Consequent…
5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization
Nisha Lakshmana Raichur, Lucas Heublein, Christopher Mutschler +1
Indoor positioning based on 5G data has achieved high accuracy through the adoption of recent machine learning (ML) techniques. However, the performance of learning-based methods d…
Passive Channel Charting: Locating Passive Targets using a UWB Mesh
Raffael Poeggel, Maximilian Stahlke, Jonas Pirkl +4
Fingerprint-based passive localization enables high localization accuracy using low-cost UWB IoT radio sensors. However, fingerprinting demands extensive effort for data acquisitio…
Simplicity is Key: An Unsupervised Pretraining Approach for Sparse Radio Channels
Jonathan Ott, Maximilian Stahlke, Tobias Feigl +2
Unsupervised representation learning for wireless channel state information (CSI)reduces reliance on labeled data, thereby lowering annotation costs, and often improves performance…
VAE-based Feature Disentanglement for Data Augmentation and Compression in Generalized GNSS Interference Classification
Lucas Heublein, Simon Kocher, Tobias Feigl +3
Distributed learning and Edge AI necessitate efficient data processing, low-latency communication, decentralized model training, and stringent data privacy to facilitate real-time…
Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification
Nishant S. Gaikwad, Lucas Heublein, Nisha L. Raichur +3
Federated learning (FL) enables multiple devices to collaboratively train a global model while maintaining data on local servers. Each device trains the model on its local server a…
Radio Foundation Models: Pre-training Transformers for 5G-based Indoor Localization
Jonathan Ott, Jonas Pirkl, Maximilian Stahlke +2
Artificial Intelligence (AI)-based radio fingerprinting (FP) outperforms classic localization methods in propagation environments with strong multipath effects. However, the model…
Evaluating ML Robustness in GNSS Interference Classification, Characterization & Localization
Lucas Heublein, Tobias Feigl, Thorsten Nowak +3
Jamming devices disrupt signals from the global navigation satellite system (GNSS) and pose a significant threat, as they compromise the robustness of accurate positioning. The det…
On-Device Training of Fully Quantized Deep Neural Networks on Cortex-M Microcontrollers
Mark Deutel, Frank Hannig, Christopher Mutschler +1
On-device training of DNNs allows models to adapt and fine-tune to newly collected data or changing domains while deployed on microcontroller units (MCUs). However, DNN training is…
Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning
Nisha L. Raichur, Lucas Heublein, Tobias Feigl +3
The primary objective of methods in continual learning is to learn tasks in a sequential manner over time (sometimes from a stream of data), while mitigating the detrimental phenom…
Few-Shot Learning with Uncertainty-based Quadruplet Selection for Interference Classification in GNSS Data
Felix Ott, Lucas Heublein, Nisha Lakshmana Raichur +4
Jamming devices pose a significant threat by disrupting signals from the global navigation satellite system (GNSS), compromising the robustness of accurate positioning. Detecting a…
Optimal joint cutting of two-qubit rotation gates
Christian Ufrecht, Laura S. Herzog, Daniel D. Scherer +4
Circuit cutting, the partitioning of quantum circuits into smaller independent fragments, has become a promising avenue for scaling up current quantum-computing experiments. Here,…
Velocity-Based Channel Charting with Spatial Distribution Map Matching
Maximilian Stahlke, George Yammine, Tobias Feigl +2
Fingerprint-based localization improves the positioning performance in challenging, non-line-of-sight (NLoS) dominated indoor environments. However, fingerprinting models require a…
Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML
Mark Deutel, Georgios Kontes, Christopher Mutschler +1
Deploying deep neural networks (DNNs) on microcontrollers (TinyML) is a common trend to process the increasing amount of sensor data generated at the edge, but in practice, resourc…