Data for Download

https://github.com/nyu-wireless/mmwchanmod

Related publications

• W. Xia et al., "Millimeter Wave Channel Modeling via Generative Neural Networks," 2020 IEEE Globecom Workshops (GC Wkshps, Taipei, Taiwan, 2020, pp. 1-6, doi: 10.1109/GCWkshps50303.2020.9367420

and

• W. Xia et al., "Generative Neural Network Channel Modeling for Millimeter-Wave UAV Communication," in IEEE Transactions on Wireless Communications, vol. 21, no. 11, pp. 9417-9431, Nov. 2022, doi: 10.1109/TWC.2022.3176480.

The measurement results of drone RCS signatures at 26-40 GHz are available for download. The results can be used to form a substantial database of commercially available drone models. Based on the measured RCS machine learning algorithms or drone detection techniques may be developed.

http://dx.doi.org/10.21227/m8xk-dr55

Suggested citation:

• V. Semkin, J. Haarla, T. Pairon, C. Slezak, S. Rangan, V. Viikari, and C. Oestges, "Analyzing Radar Cross Section Signatures of Diverse Drone Models at mmWave Frequencies," IEEE Access, vol. 8, 2020, pp. 48958-48969.

and

• V. Semkin, J. Haarla, T. Pairon, C. Slezak, S. Rangan, V. Viikari, C. Oestges, "Drone RCS measurements (26-40 GHz)", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/m8xk-dr55. Accessed: Nov. 21, 2019.

The board design was performed at New York University, while the antenna, transmission line, and balun design were developed at Aalto University. All hardware design schematics have been published on GitHub under the MIT license and is available by the following link:

http://bit.ly/FullyDigitalSDR60GHz