The RFUAV DATASET
This repository contains the RFUAV dataset, presented in the paper "RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification". RFUAV provides approximately 1.3 TB of raw frequency data collected from 37 distinct UAVs, offering a comprehensive benchmark for radio-frequency-based drone detection and identification. The dataset addresses limitations of existing datasets by providing a diverse range of drone types, sufficient data volume, coverage across various signal-to-noise ratios (SNR), and open-access evaluation tools.
For detailed usage instructions, preprocessing methods, model evaluation tools, and code examples, please refer to the GitHub repository.
Citation
@misc{shi2025rfuavbenchmarkdatasetunmanned,
title={RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification},
author={Rui Shi and Xiaodong Yu and Shengming Wang and Yijia Zhang and Lu Xu and Peng Pan and Chunlai Ma},
year={2025},
eprint={2503.09033},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2503.09033},
}
The RFUAV DATASET
This repository contains the RFUAV dataset, presented in the paper "RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification". RFUAV provides approximately 1.3 TB of raw frequency data collected from 37 distinct UAVs, offering a comprehensive benchmark for radio-frequency-based drone detection and identification. The dataset addresses limitations of existing datasets by providing a diverse range of drone types, sufficient data volume, coverage across various signal-to-noise ratios (SNR), and open-access evaluation tools.
For detailed usage instructions, preprocessing methods, model evaluation tools, and code examples, please refer to the GitHub repository.
Citation
@misc{shi2025rfuavbenchmarkdatasetunmanned,
title={RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification},
author={Rui Shi and Xiaodong Yu and Shengming Wang and Yijia Zhang and Lu Xu and Peng Pan and Chunlai Ma},
year={2025},
eprint={2503.09033},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2503.09033},
}