Sparsely Connected Low Complexity CNN for Unmanned Vehicles Detection - Sensing RF Signal

Unmanned aerial systems, namely drones, have greatly improved and expanded drastically over the years. Due to their efficiency and ease of use, drones have been utilized in a wide range of applications. Despite various potential uses, drones are also being utilized for illegal operations and exposing security threats to citizens. It is vital to install an effective anti-drone system to identify and defend against intruding malevolent drones to protect national security. Although there has been tremendous advancement in the development of machine learning to deploy lightweight architectures in the sensor industry, no such drone detection approach has yet been described in the literature. Therefore, this paper proposed a lightweight convolution neural network (CNN), namely RFDNet, to investigate the problem of 17 types of drone RF fingerprint classification problems in the low SNR regime. The network is configured with two principle modules, which are leveraged by the grouped and depth-wise convolution layers, incorporating accuracy improvement while keeping the complexity low. Notably, most existing networks fail to outstandingly detect drones at low SNR levels because the RF signal envelope is distorted and the transient information is lost in the noise. To solve this issue, we collected an open-source RF dataset that stores 17 types of drone RF signals at 30 dB SNR. To investigate the RF dataset at various SNR levels, we regenerate the dataset at different SNRs (i.e.,
$-10$
dB to 30 dB SNR with the 5 dB interval) and analyze the performance of the proposed network. The empirical results show that RFDNet performed outstandingly compared to the existing deep learning-based drone detection methods and achieved an overall 99.07% accuracy at 15 dB signal-to-noise ratio (SNR).