HYMN provides synchronized UWB, BLE, WiFi, 5G, and GNSS data with ground truth in a hybrid indoor-outdoor industrial setting to support seamless localization research.
An empirical assessment of indoor-outdoor localization based on signals of opportunity from multiple systems
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A neural network fuses wheel and motor speed signals to cut wheel-speed estimation error by up to 85% versus the production sensor on real Volkswagen ID.7 data.
citing papers explorer
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Descriptor: A Hybrid Indoor and Indoor-Outdoor Positioning Multi-Technology Dataset (HYMN)
HYMN provides synchronized UWB, BLE, WiFi, 5G, and GNSS data with ground truth in a hybrid indoor-outdoor industrial setting to support seamless localization research.
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Neural Network-Based Virtual Wheel-Speed Sensor for Enhanced Low-Velocity State Estimation
A neural network fuses wheel and motor speed signals to cut wheel-speed estimation error by up to 85% versus the production sensor on real Volkswagen ID.7 data.