Deep Complex-valued Convolutional Neural Network for Drone Recognition
Based on RF Fingerprinting
Abstract
Drones-aided ubiquitous applications play more and more important roles
in our daily life. Accurate recognition of drones is required in
aviation management due to their potential risks and even disasters.
Radio frequency (RF) fingerprinting-based recognition technology based
on deep learning is considered as one of the effective approaches to
extract hidden abstract features from RF data of drones. Existing deep
learning-based methods are either a high computational burden or low
accuracy.
In this paper, we propose a deep complex-valued convolutional neural
network (DC-CNN) method based on RF fingerprinting for recognizing
different drones.
Compared with existing recognition methods, the DC-CNN method has the
advantages of high recognition accuracy, fast running time and small
network complexity.
Nine algorithm models and two datasets are used to represent the
superior performance of our system.
Experimental results show that our proposed DC-CNN can achieve
recognition accuracy of 99.5\% and
74.1\% respectively on 4 and 8 classes of RF drone
datasets.