Cardinal RF (CardRF): An Outdoor UAV/UAS/Drone RF Signals with Bluetooth and WiFi Signals Dataset
drone detection, classification, identification, remote controllers, RF fingerprinting, RF signals, unmanned aerial vehicles (UAVs), wireless communications, Hilbert transforms, autonomous aerial vehicles, uas, Feature extraction, learning (artificial intelligence), remotely operated vehicles, wavelet transforms, Bluetooth, WiFi, 2.4GHz
This article presents the details of the Cardinal RF (CardRF) dataset. CardRF is acquired to foster research in RF- based UAV detection and identification or RF fingerprinting. RF signals were collected from UAV controllers, UAV, Bluetooth, and Wi-Fi devices. Signals are collected at both visual line-of-sight and beyond-line-of-sight. The assumptions and procedure for the data acquisition are presented. A detailed explanation of how the data can be utilized is discussed. CardRF is over 65 GB in storage memory.
The dataset contains UAV (telemetry and control), Bluetooth, and WiFi RF signals in .mat format. It was captured in an outdoor setting. Each RF signal has 5 million sampling points and spans a time period of 0.25ms. The script for plotting the signal is called SIGNAL_PLOT.mlx (a Matlab script) in the code zip file.
In the supplement zip file, the processed zip has resampled signals with 1024 sampling points (signal length). We used these signals for our paper titled "Hierarchical Learning Framework for UAV Detection and Identification". The scripts used for resampling are in the code zip file.
The CardRF dataset has been used in the following papers:
- O. O. Medaiyese, M. Ezuma, A. P. Lauf and A. A. Adeniran, "Hierarchical Learning Framework for UAV Detection and Identification," in IEEE Journal of Radio Frequency Identification, vol. 6, pp. 176-188, 2022, doi: 10.1109/JRFID.2022.3157653.
- O. O. Medaiyese, M. Ezuma, A. P. Lauf and I. Guvenc, "Semi-supervised Learning Framework for UAV Detection," 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2021, pp. 1185-1190, doi: 10.1109/PIMRC50174.2021.9569452.
- O.O. Medaiyese, M. Ezuma, A.P. Lauf, and I. Guvenc, 2022. Wavelet transform analytics for RF-based UAV detection and identification system using machine learning. Pervasive and Mobile Computing, 82, p.101569.