  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.
