Data · dataset · 2026
DRFF-R2 | Multi-Scenario UAV Radio Frequency Signal Dataset
Listed in ScienceDB
This dataset is a large-scale dataset designed to support research on multiple unmanned aerial vehicle radio frequencies (RF).
Description
It relies on a unified standardized collection framework and aims to provide a reliable foundation for cross scenario modeling and model generalization ability evaluation. This dataset has the following four core advantages: Device and individual diversity: The dataset includes 26 drones of 8 different models, providing rich device categories and individual differences. Multi scenario and environmental complexity: The collected environment covers various complex scenarios such as outdoor flight, indoor interference, and mixed flight of multiple drones, which can truly and effectively depict the channel changes and signal coupling characteristics in actual wireless environments. The spatiotemporal dynamics of altitude: Data collection covers different flight altitudes, speeds, operating states, and cross time conditions.
This multidimensional dynamic change feature helps algorithm models better interpret and process non-stationary signals.Data consistency and comparability: With a unified hardware configuration and standardized collection process, the dataset strictly ensures consistency between data from different scenarios. Please cite the following references when using this dataset: @article{zheng2026multi,title={A Multi-Scenario UAV RF Dataset with Real-World Acquisition and Signal Processing Benchmarking},author={Zheng, Haolin and Gao, Ning and Zhu, Zhenghang and Huang, Zhijun and Jin, Shi and Matthaiou, Michail},journal={arXiv preprint arXiv:2603.00106},year={2026}}
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.36815 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
Provenance · 1 source records, 10 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.36815 | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/rights |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |