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.

Migrating insects and birds are the primary biological targets in the aerial ecosystem. Radar is a powerful tool for monitoring and studying aerial animals. However, accurately identifying insects and birds based on radar observations has remained an unsolved problem. To address this research gap, this paper proposed an intelligent classification method based on a novel multi-scale time–frequency deep feature fusion network (MSTFF-Net). A comprehensive radar dataset of aerial biological targets was established. The analysis revealed that radar cross section (RCS) features are insufficient to support insect and bird classification tasks, as aerial biological targets may be detected in radar sidelobes, leading to uncertainty in RCS values. Additionally, the motion characteristics of insects and birds are complex, with diverse motion patterns observed during limited observation periods. Simple feature extraction and classification algorithms struggle to achieve accurate classification of insects and birds, making aerial biological target classification a challenging task. Based on the analysis of insect and bird features, the designed MSTFF-Net consists of the following three modules. The first module is the amplitude sequence extraction module, which abandons traditional RCS features and instead extracts the dynamic variation features of the echo amplitude. The second module is the time–frequency feature extraction module, which extracts multi-scale time–frequency features to address the complex motion characteristics of biological targets. The third module is the adaptive feature fusion attention module, which captures the correlation between features to adjust feature weights and achieve the fusion of different feature types with varying representations. The reliability of the classification algorithm was finally verified using a manually selected dataset, which includes typical bird, insect, and other unknown targets. The algorithm proposed in this paper achieved a classification accuracy of 94.0% for insect and bird targets.

Polarimetric radar offers strong potential for UAV detection, but time-varying polarization induced by rotor rotation leads to unstable echoes, degrading feature consistency and recognition accuracy. This paper proposes a unified framework that combines rotor phase compensation, adaptive polarization filtering, and a multi-branch polarization aware fusion network (MPAF-Net) to enhance micro-Doppler features. The compensation scheme improves harmonic visibility through rotation-angle-based phase alignment and polarization optimization, while MPAF-Net exploits complementary information across polarimetric channels for robust classification. The framework is validated on both simulated and measured UAV radar data under varying SNR conditions. Results show an average harmonic SNR gain of approximately 1.2 dB and substantial improvements in recognition accuracy: at 0 dB, the proposed method achieves 66.7% accuracy, about 10% higher than Pauli and Sinclair decompositions, and at 20 dB, it reaches 97.2%. These findings confirm the effectiveness of the proposed approach for UAV identification in challenging radar environments.