Dataset-2: CARDINAL RF (CARDRF): An Outdoor UAV/UAS/DRONE RF Signals with Bluetooth and WiFi Signals Dataset
Lead Experimenter
Olusiji Medaiyese and Adrian Lauf, University of Louisville
Link to Dataset
- Dataset Link:
[IEEE Dataport]
Publications Using This Dataset
- J. Liu, Z. Xia, Y. Chen, H. Sun, S. Huang, “WUSTCA: enhanced UAV RF signal classification with wavelet transform and STCA attention mechanisms,” Scientific Reports, July 2026.
- S. S. Alam, A. Chakma, M. H. Rahman, R. Bin Mofidul, M. M. Alam, I. B. K. Y. Utama, Y. M. Jang, “RF-Enabled Deep-Learning-Assisted Drone Detection and Identification: An End-to-End Approach,” MDPI Sensors, March 2023.
- O. O. Medaiyese, M. Ezuma, A. P. Lauf, I. Guvenc, “Wavelet Transform Analytics for RF-Based UAV Detection and Identification System Using Machine Learning,” Elsevier Pervasive and Mobile Computing, March 2022.
- R. Wen, G. Lu, Y. Mi, B. Wu, Y. Zhang, L. Yang, “Autoencoder-Based Deep Learning for UAV RF Signal Recognition and Classification,” Proc. International Conference on Artificial Intelligence and Pattern Recognition (AIPR), June 2025.
- C. Kumari, N. L. Prasad, U. Satija, B. Ramkumar, “Hierarchical Deep Learning Framework for Enhanced UAV Classification Mitigating Bluetooth and WiFi Interference,” Proc. IEEE Veh. Technol. Conf. (VTC), June 2024.
- O. O. Medaiyese, A. Lauf, M. Ezuma, I. Guvenc, “Semi-Supervised Learning Framework for UAV Detection,” Proc. IEEE Personal, September 2021.
- O. O. Medaiyese, “Signal Fingerprinting and Machine Learning Framework for UAV Detection and Identification,” PhD Dissertation, January 2021.
Equipment and Software Used
Keysight High-Sampling Oscilloscope, drones, remote controllers from different vendors, WiFi and Bluetooth interferers (this is a BYOD experiment)
Description
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.
Representative Results
- Result figures
The image below shows the data collection procedure and representative results on UAV/WiFi/Bluetooth classification accuracy.
Potential use cases for this dataset include:
- Interference Analysis and Mitigation
- UAV Identification and Classification