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Drone Remote Controller RF Signal Dataset

2019
2.4 GHz, 2019, Drone, RF signature
Non-Canonical Experiments
RC, RF surveillance system

Lead Experimenter

Martins Ezuma, NC State University

Link to Dataset

Equipment and Software Used

Keysight High-Sampling Oscilloscope, drones and remote controllers from different vendors (this is a BYOD experiment)

Description

This dataset contains RF signals from drone remote controllers (RCs) of different makes and models. The RF signals transmitted by the drone RCs to communicate with the drones are intercepted and recorded by a passive RF surveillance system, which consists of a high- frequency oscilloscope, directional grid antenna, and low-noise power amplifier. The drones were idle during the data capture process. All the drone RCs transmit signals in the 2.4 GHz band. There are 17 drone RCs from eight different manufacturers and ~1000 RF signals per drone RC, each spanning a duration of 0.25 ms.

Publications

  1. M. Ezuma, F. Erden, C. Kumar, O. Ozdemir, and I. Guvenc, "Micro-UAV detection and classification from RF fingerprints using machine learning techniques," in Proc. IEEE Aerosp. Conf., Big Sky, MT, Mar. 2019, pp. 1-13.
  2. M. Ezuma, F. Erden, C. K. Anjinappa, O. Ozdemir, and I. Guvenc, "Detection and classification of UAVs using RF fingerprints in the presence of Wi-Fi and Bluetooth interference," IEEE Open J. Commun. Soc., vol. 1, no. 1, pp. 60-79, Nov. 2019.
  3. E. Ozturk, F. Erden, and I. Guvenc, "RF-based low-SNR classification of UAVs using convolutional neural networks." arXiv preprint arXiv:2009.05519, Sept. 2020.

Representative Results

  • Result figures
    The image below shows the data collection procedure and a representative results on UAV classification accuracy.
    Screenshot 2023-04-11 at 23 06 42

 

Potential use cases for this dataset include:

  • Counter UAS
  • RF Fingerprinting for UAV Identification/Classification
  • Interference Analysis
  • Machine Learning Model Training