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

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