Polarization Compensation and Multi-Branch Fusion Network for UAV Recognition with Radar Micro-Doppler Signatures

Highlights: What are the main findings? A rotor–polarization coupling model and corresponding compensation algorithm are developed to correct time-varying polarization rotation induced by UAV rotor motion, effectively enhancing harmonic visibility and micro-Doppler continuity under low-SNR conditions. A Multi-branch Polarization-Aware Fusion Network (MPAF-Net) integrating U-Net-based denoising, polarization-compensated feature extraction, and multi-head attention fusion achieves over 5% improvement in UAV recognition accuracy compared with conventional polarimetric representations. What is the implication of the main finding? The proposed framework significantly improves the stability and discriminability of UAV polarimetric micro-Doppler features, offering a practical solution for cluttered, low-SNR radar environments. The results contribute to the advancement of polarization-based recognition techniques for rotor-type UAVs, enhancing radar sensing capability for small aerial vehicles. 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.