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Creators/Authors contains: "Soltani, Nasim"

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  1. Free, publicly-accessible full text available November 1, 2024
  2. Free, publicly-accessible full text available September 1, 2024
  3. Free, publicly-accessible full text available May 17, 2024
  4. null (Ed.)
  5. We propose a framework called AirID that identifies friendly/authorized UAVs using RF signals emitted by radios mounted on them through a technique called as RF finger- printing. Our main contribution is a method of intentionally inserting ‘signatures’ in the transmitted I/Q samples from each UAV, which are detected through a deep convolutional neural network (CNN) at the physical layer, without affecting the ongoing UAV data communication process. Specifically, AirID addresses the challenge of how to overcome the channel-induced perturbations in the transmitted signal that lowers identification accuracy. AirID is implemented using Ettus B200mini Software Defined Radios (SDRs) that serve as both static ground UAV identifiers, as well as mounted on DJI Matrice M100 UAVs to perform the identification collaboratively as an aerial swarm. AirID tackles the well-known problem of low RF fingerprinting accuracy in ‘train on one day test on another day’ conditions as the aerial environment is constantly changing. Results reveal 98% identification accuracy for authorized UAVs, while maintaining a stable communication BER of 10^−4 for the evaluated cases. 
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