TY - GEN
T1 - Drone classification from RF fingerprints using deep residual nets
AU - Basak, Sanjoy
AU - Rajendran, Sreeraj
AU - Pollin, Sofie
AU - Scheers, Bart
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/1/5
Y1 - 2021/1/5
N2 - Detecting UAVs is becoming more crucial for various industries such as airports and nuclear power plants for improving surveillance and security measures. Exploiting radio frequency (RF) based drone control and communication enables a passive way of drone detection for a wide range of environments and even without favourable line of sight (LOS) conditions. In this paper, we evaluate RF based drone classification performance of various state-of-the-art (SoA) models on a new realistic drone RF dataset. With the help of a newly proposed residual Convolutional Neural Network (CNN) model, we show that the drone RF frequency signatures can be used for effective classification. The robustness of the classifier is evaluated in a multipath environment considering varying Doppler frequencies that may be introduced from a flying drone. We also show that the model achieves better generalization capabilities under different wireless channel and drone speed scenarios. Furthermore, the newly proposed model's classification performance is evaluated on a simultaneous multi-drone scenario. The classifier achieves close to 99% classification accuracy for signal-to-noise ratio (SNR) 0 dB and at -10 dB SNR it obtains 5% better classification accuracy compared to the existing framework.
AB - Detecting UAVs is becoming more crucial for various industries such as airports and nuclear power plants for improving surveillance and security measures. Exploiting radio frequency (RF) based drone control and communication enables a passive way of drone detection for a wide range of environments and even without favourable line of sight (LOS) conditions. In this paper, we evaluate RF based drone classification performance of various state-of-the-art (SoA) models on a new realistic drone RF dataset. With the help of a newly proposed residual Convolutional Neural Network (CNN) model, we show that the drone RF frequency signatures can be used for effective classification. The robustness of the classifier is evaluated in a multipath environment considering varying Doppler frequencies that may be introduced from a flying drone. We also show that the model achieves better generalization capabilities under different wireless channel and drone speed scenarios. Furthermore, the newly proposed model's classification performance is evaluated on a simultaneous multi-drone scenario. The classifier achieves close to 99% classification accuracy for signal-to-noise ratio (SNR) 0 dB and at -10 dB SNR it obtains 5% better classification accuracy compared to the existing framework.
KW - Convolutional neural network
KW - deep neural networks
KW - sensor systems and applications
UR - http://www.scopus.com/inward/record.url?scp=85102038429&partnerID=8YFLogxK
U2 - 10.1109/COMSNETS51098.2021.9352891
DO - 10.1109/COMSNETS51098.2021.9352891
M3 - Conference contribution
AN - SCOPUS:85102038429
T3 - 2021 International Conference on COMmunication Systems and NETworkS, COMSNETS 2021
SP - 548
EP - 555
BT - 2021 International Conference on COMmunication Systems and NETworkS, COMSNETS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Conference on COMmunication Systems and NETworkS, COMSNETS 2021
Y2 - 5 January 2021 through 9 January 2021
ER -