基于时频多通道特征的无人机信号智能识别方法OA

To address the need for precise perception of rapidly maneuvering and diverse unmanned aerial vehicle(UAV)models in complex airspace environments,a UAV signal recognition method based on time-frequency multi-channel features is proposed.In the signal processing stage,the UAV signals undergo detrending and denoising to eliminate direct current components and low-frequency drift,thereby reducing frequency-domain interference.The discrete Fourier transform is then applied to extract time-frequency information,followed by spectrum splicing and amplitude normalization to construct energy spec-trum data.To address spatial heterogeneity,multi-channel processing is performed along the frequency domain.For the model architecture,a multi-feature channel signal tensorization strategy is proposed based on an MC-2DCNN,precisely aligning with the model's multi-channel convolutional characteristics.Through spatial decoupling and structured reorganization of frequency-domain features,hierarchical extrac-tion and differentiated modeling of cross-band features are achieved while preserving feature topological adjacency,leveraging channel separation mechanisms to enhance model robustness and generalization.Experiments demonstrate that the proposed method achieves recognition accuracies of 98.15％and 96.32％for UAV models and flight modes,respectively,within the-10 dB to 10 dB noise range.Under aliasing noise scenarios involving electromagnetic noise,comb spectrum,and blocking interference,it attains accuracies of 91.97％and 90.56％,meeting intelligent recognition requirements for UAV detec-tion systems in both civilian and military applications.