通信特定辐射源识别的多特征融合分类方法

A multi-feature fusion classification method based on multi-channel transform projection, integrated deep learning and generative adversarial network (GAN) was proposed for communication specific emitter identification. First, three-dimensional feature images were obtained by performing various transformations, the time and frequency domain projection of the signal was constructed to construct the feature datasets. GAN was used to expand the datasets. Then, a two-stage recognition and classification method based on multi-feature fusion was designed. Deep neural networks were used to learn the three feature datasets, and the initial classification results were obtained. Finally, through fusion and re-learning of the initial classification result, the final classification result was obtained. Based on the measurement and analysis of the actual signals, the experimental results show that the method has higher accuracy than the single feature extraction method. The multipath fading channel has been used to simulate the outdoor propagation environment, and the method has certain generalization performance to adapt to the complex wireless channel environments.