Radio Frequency Fingerprinting Driven Drone Identification Based on Complex-valued CNN

Drone detection and identification technique is of great significance both in the military and civilian fields. Radio frequency (RF) fingerprinting of drone is considered as one of promising techniques due to its uniqueness. Deep learning based RF fingerprinting identification technique can extract hidden features in RF data and then achieve excellent performance. Motivated by this idea, this paper proposes a drone identification method using complex-valued convolutional neural network (CNN) algorithm with higher classification accuracy and faster equipment running time. The complex-valued CNN method convolves the complex convolutional kernel and the real and imaginary parts of the data features separately. In order to verify the proposed method, five state-of-the-art recognition algorithms are adopted to compare their recognition performance and equipment efficiency. Simulation results show that our proposed drone identification method can efficiently recognize the signal of various drones within less computation time.