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.
The dataset contains ~1000 RF signals in .mat format from the remote controllers (RCs) of the following drones:
- DJI (5): Inspire 1 Pro, Matrice 100, Matrice 600*, Phantom 4 Pro*, Phantom 3
- Spektrum (4): DX5e, DX6e, DX6i, JR X9303
- Futaba (1): T8FG
- Graupner (1): MC32
- HobbyKing (1): HK-T6A
- FlySky (1): FS-T6
- Turnigy (1): 9X
- Jeti Duplex (1): DC-16.
In the dataset, there are two pairs of RCs for the drones indicated by an asterisk above, making a total of 17 drone RCs. Each RF signal contains 5 million samples and spans a time period of 0.25 ms.
The scripts provided with the dataset defines a class to create drone RC objects and creates a database of objects as well as a database in table format with all the available information, such as make, model, raw RF signal, sampling frequency, etc. The scripts also include functions to visualize data and extract a few example features from the raw RF signal (e.g., transient signal start point). Instructions for using the scripts are included at the top of each script and can also be viewed by typing help scriptName in MATLAB command window.
The drone RC RF dataset was used in the following papers:
- 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.
- 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.
- 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.
Other details regarding the dataset and data collection and processing can be found in the above papers and attached documentation.
- Experiment design: O. Ozdemir and M. Ezuma
- Data collection: M. Ezuma
- Scripts: F. Erden and C. K. Anjinappa
- Documentation: F. Erden
- Supervision, revision, and funding: I. Guvenc
This work was supported in part by NASA through the Federal Award under Grant NNX17AJ94A, and in part by NSF under CNS-1939334 (AERPAW, one of NSF's Platforms for Advanced Wireless Research (PAWR) projects).