4 citations · 4 across the 11 of their papers we have counts for
12 papers
From Least Squares to Deep Learning: Benchmarking Indoor Positioning on the HYMN Multi-Technology Dataset
Paul Schwarzbach, Muhammad Ammad
Indoor positioning benchmarks rarely co-locate multiple technologies with high-accuracy ground truth, limiting cross-method and cross-technology comparison. We evaluate four range-…
Bridging the Indoor-Outdoor Gap: Cross-Technology Ranging for Seamless Robot Navigation
Paul Schwarzbach
Mobile robots that move between outdoor and indoor environments still struggle with consistent positioning. Satellite-based and terrestrial ranging each work well in their home dom…
Descriptor: A Hybrid Indoor and Indoor-Outdoor Positioning Multi-Technology Dataset (HYMN)
Muhammad Ammad, Albrecht Michler, Paul Schwarzbach +3
This article introduces the HYMN (HYbrid Multi-technology Navigation) dataset: a multi-system, and time synchronized dataset for localization research based on opportunistic signal…
Evaluation of Grid-based Uncertainty Propagation for Collaborative Self-Calibration in Indoor Positioning Systems
Paul Schwarzbach, Andrea Jung
Radio-based localization systems conventionally require stationary reference points (e.g. anchors) with precisely surveyed positions, making deployment time-consuming and costly. T…
Range-Angle Likelihood Maps for Indoor Positioning Using Deep Neural Networks
Muhammad Ammad, Paul Schwarzbach, Michael Schultz +1
Accurate and high precision of the indoor positioning is as important as ensuring reliable navigation in outdoor environments. Using the state-of-the-art deep learning models provi…
Simulation-based Evaluation of Indoor Positioning Systems in Connected Aircraft Cabins
Paul Schwarzbach, Jonas Ninnemann, Hagen Ußler +2
The manuscript discusses the increasing use of location-aware radio communication systems to support operational processes for the demanding aircraft cabin environment. In this con…
Potentials of Deterministic Radio Propagation Simulation for AI-Enabled Localization and Sensing
Albrecht Michler, Jonas Ninnemann, Jakob Krauthäuser +2
Machine leaning (ML) and artificial intelligence (AI) enable new methods for localization and sensing in next-generation networks to fulfill a wide range of use cases. These approa…
Grid-based Hybrid 3DMA GNSS and Terrestrial Positioning
Paul Schwarzbach, Albrecht Michler, Oliver Michler
The paper discusses the increasing use of hybridized sensor information for GNSS-based localization and navigation, including the use of 3D map-aided GNSS positioning and terrestri…
Auto-Positioning in Radio-based Localization Systems: A Bayesian Approach
Andrea Jung, Paul Schwarzbach, Oliver Michler
The application of radio-based positioning systems is ever increasing. In light of the dissemination of the Internet of Things and location-aware communication systems, the demands…
Efficient coverage planning for full-area C-ITS communications based on radio propagation simulation and measurement tools
Hagen Ußler, Christian Setzefand, Daniel Kanis +2
Intelligent infrastructure, currently often consisting of C-ITS stations and prospectively supplemented by 5G, is a key-enabler for application-oriented and area-wide realization o…
Enabling Radio Sensing for Multimodal Intelligent Transportation Systems: From Virtual Testing to Immersive Testbeds
Paul Schwarzbach, Jonas Ninnemann, Oliver Michler
In this paper, the necessity for application-oriented development and evaluation of Joint Communication and Sensing (JC&S) applications, especially in transportation, is addressed.…
Multipath-assisted Radio Sensing and Occupancy Detection for Smart In-house Parking in ITS
Jonas Ninnemann, Paul Schwarzbach, Oliver Michler
Joint, radio-based communication, localization and sensing is a rapidly emerging research field with various application potentials. Greatly benefiting from these capabilities, sma…