RFUAV
A benchmark dataset and toolkit for RF-based drone detection and identification using raw IQ data and deep learning models.
What is RFUAV?
RFUAV is a benchmark dataset and research toolkit for detecting and identifying drones using Radio-Frequency (RF) signals. It provides raw IQ data from 35 drone types, along with deep learning models and signal processing tools to analyze the data directly, solving the problem of drone surveillance in RF domains where visual methods may fail.
Researchers and engineers working on RF signal analysis, drone surveillance, and machine learning applications in telecommunications or IoT security.
It offers a unique, publicly available RF dataset with high SNR recordings and a complete toolkit for end-to-end drone detection and identification, enabling reproducible research and practical deployment without proprietary dependencies.
Overview
This is official repository of our paper "RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification". Codes include a two stage model to achieve drone detection and classification using some FFT/STFT analytical method. The Raw data will be free to use after our paper is accept. Star us!!!!, if you think this is useful♥
Use Cases
Best For
- Training deep learning models for RF-based drone identification
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
