Radio frequency based distributed system for noncooperative UAV classification and positioning

With the increasing popularity of civilian unmanned aerial vehicles (UAVs), safety issues arising from unsafe operations and terrorist activities have received growing attention. To address this problem, an accurate classification and positioning system is needed. Considering that UAVs usually use radio frequency (RF) signals for video transmission, in this paper, we design a passive distributed monitoring system that can classify and locate UAVs according to their RF signals. Specifically, three passive receivers are arranged in different locations to receive RF signals. Due to the noncooperation between a UAV and receivers, it is necessary to detect whether there is a UAV signal from the received signals. Hence, convolutional neural network (CNN) is proposed to not only detect the presence of the UAV, but also classify its type. After the UAV signal is detected, the time difference of arrival (TDOA) of the UAV signal arriving at the receiver is estimated by the cross-correlation method to obtain the corresponding distance difference. Finally, the Chan algorithm is used to calculate the location of the UAV. We deploy a distributed system constructed by three software defined radio (SDR) receivers on the campus playground, and conduct extensive experiments in a real wireless environment. The experimental results have successfully validated the proposed system.