26 citations · 85 across the 28 of their papers we have counts for
5 papers · 1 filter
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…
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…