Intelligent Recognition of a Low-Altitude AAV Based on Micro-Doppler Feature Enhancement

The recognition of unmanned aerial vehicles (UAVs) has become a core aspect of low-altitude airspace management. Typically, the micro-Doppler characteristics of rotors are employed to accomplish the UAV recognition task. However, the performance of UAV recognition on the basis of micro-Doppler characteristics significantly degrades under low signal-to-noise ratio (SNR) conditions. This paper presents an intelligent small UAV recognition method based on micro-Doppler enhancement to separate UAVs from aerial targets under low SNR conditions. First, a dynamic echo signal model of a UAV rotating blade is established while considering the blade length and rotation frequency parameters. Second, a micro-Doppler characteristic enhancement transformation based on nonlinear body–micro coupling is proposed. In the enhancement algorithm, the phase difference is used to separate the Doppler component of the UAV body and rotor blade. Then, a twodimensional micro-Doppler feature map is created on the basis of the blade length and rotor frequency. Crucially, the microDoppler energy is increased when the values of the parameters match the true values. On the basis of a micro-Doppler feature map, a convolutional neural network (CNN) is constructed to achieve UAV recognition. The effectiveness of the proposed method is verified by experiments using both simulated data and real data. The radar echoes of 2 types of UAVs with S-band carrier frequencies are collected. Among them, DJI-Inspire UAV data with a high SNR of micro-Doppler characteristics are used to verify the correctness of the algorithm, and DJI-Phantom4 UAV data with a low SNR of micro-Doppler characteristics are used to test the performance of the algorithm. The proposed method significantly improves UAV recognition performance under low SNR conditions.