UAV Detection and Localization Based on Multi-Dimensional Signal Features

In recent years, unmanned aerial vehicles (UAVs) have received growing attention due to security threats issues. Though UAV detection and positioning systems are commonly used in various scenarios, most of the available systems are still suffering from low accuracy and susceptibility to the environment. Therefore, it is still necessary to design a highly accurate, versatile UAV detection and positioning system. In this paper, a UAV detection and positioning system based on multi-dimensional signal features is proposed. The first step of the system is to monitor the communication signal and channel state information (CSI) between the UAV and the controller. Subsequently the signal frequency spectrum (SFS), the wavelet energy entropy (WEE), and the power spectral entropy (PSE) are extracted as features. At the same time, machine learning algorithms combining the above features are applied to detect UAVs. After the UAV is successfully detected, the spatial features such as angle of azimuth (AOA) and angle of elevation (AOE) were extracted for UAV localization based on a super-resolution estimation algorithm. In conclusion, the Wireless Insite (WI) software was employed for long-distance positioning verification while the software-defined radio (SDR) was used for small-scale testing and verification. The experimental results show that the average detection rate of combining multiple features in the test environment is 95.58%, the median accuracy of 2D positioning is 0.76 m, and the median accuracy of 3D positioning is 1.2 m. In the WI simulation environment, the median accuracy of 2D positioning is 1.1 m, and the median accuracy of 3D positioning is 2.35 m.