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… See the full description on the dataset page: https://huggingface.co/datasets/kitofrank/RFUAV.

In this paper, we propose RFUAV as a new benchmark dataset for radio-frequency based (RF-based) unmanned aerial vehicle (UAV) identification and address the following challenges: Firstly, many existing datasets feature a restricted variety of drone types and insufficient volumes of raw data, which fail to meet the demands of practical applications. Secondly, existing datasets often lack raw data covering a broad range of signal-to-noise ratios (SNR), or do not provide tools for transforming raw data to different SNR levels. This limitation undermines the validity of model training and evaluation. Lastly, many existing datasets do not offer open-access evaluation tools, leading to a lack of unified evaluation standards in current research within this field. RFUAV comprises approximately 1.3 TB of raw frequency data collected from 37 distinct UAVs using the Universal Software Radio Peripheral (USRP) device in real-world environments. Through in-depth analysis of the RF data in RFUAV, we define a drone feature sequence called RF drone fingerprint, which aids in distinguishing drone signals. In addition to the dataset, RFUAV provides a baseline preprocessing method and model evaluation tools. Rigorous experiments demonstrate that these preprocessing methods achieve state-of-the-art (SOTA) performance using the provided evaluation tools.