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The ubiquity of unmanned aerial vehicles (UAVs) or drones is posing bothsecurity and safety risks to the public as UAVs are now used for cybercrimes.To mitigate these risks, it is important to have a system that can detect oridentify the presence of an intruding UAV in a restricted environment. In thiswork, we propose a radio frequency (RF) based UAV detection and identificationsystem by exploiting signals emanating from both the UAV and its flightcontroller, respectively. While several RF devices (i.e., Bluetooth and WiFidevices) operate in the same frequency band as UAVs, the proposed frameworkutilizes a semi-supervised learning approach for the detection of UAV or UAV'scontrol signals in the presence of other wireless signals such as Bluetooth andWiFi. The semi-supervised learning approach uses stacked denoising autoencoderand local outlier factor algorithms. After the detection of UAV or UAV'scontrol signals, the signal is decomposed by using Hilbert-Huang transform andwavelet packet transform to extract features from the time-frequency-energydomain of the signal. The extracted feature sets are used to train athree-level hierarchical classifier for identifying the type of signals (i.e.,UAV or UAV control signal), UAV models, and flight mode of UAV. To demonstratethe feasibility of the proposed framework, we carried out an outdoor experimentfor data collection using six UAVs, five Bluetooth devices, and two WiFidevices. The acquired data is called Cardinal RF (CardRF) dataset, and it isavailable for public use to foster UAV detection and identification research.

Hierarchical Learning Framework for UAV Detection and Identification