activity
20222026
most citedPosition Tracking using Likelihood Modeling of Channel Features with Gaussian Processes

4 citations · 4 across the 7 of their papers we have counts for

collaborators

7 papers

eess.SP2026

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…

eess.SP2025

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…

eess.SP2025

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…

eess.SP2024

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…

eess.SP2023

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…

eess.SP2022

Indoor Localization with Robust Global Channel Charting: A Time-Distance-Based Approach

Maximilian Stahlke, George Yammine, Tobias Feigl +2

Fingerprinting-based positioning significantly improves the indoor localization performance in non-line-of-sight-dominated areas. However, its deployment and maintenance is cost-in…

eess.SP2022★ 4 cited

Position Tracking using Likelihood Modeling of Channel Features with Gaussian Processes

Sebastian Kram, Christopher Kraus, Tobias Feigl +3

Recent localization frameworks exploit spatial information of complex channel measurements (CMs) to estimate accurate positions even in multipath propagation scenarios. State-of-th…