【摘要】 发射机模拟元器件的非完美特性会导致射频信号的失真，这些失真可以作为特定辐射源识别（SEI）的指纹特征用于辐射源识别。SEI特征通常基于失真模型的方法或基于机器学习的方法提取。该文将这两种方法联合起来进行辐射源的特征提取。将专业知识（即失真模型）集成到神经网络中，提出了一种级联网络的模式来提取辐射源的同相-正交不平衡和相位噪声模型参数，既保证了特征的可解释性，又提高了识别精度。数字仿真结果表明，该方法在特征提取性能上优于传统单纯基于失真模型和机器学习的方法。更多还原

【Abstract】 Imperfections in the analog components of emitters cause distortions in the transmitted signals.These distortions serve as unique fingerprints for the purpose of specific emitter identification(SEI). Features for SEI are typically extracted using either a distortion-models based method or a machine learning based method. In this study, these two methods for feature extraction from emitters are investigated. Incorporating specialist knowledge, the distortion model, into the neural network, a cascade network mode is proposed to extract the parameters in-phase/quadrature imbalance and phase noise models of the emitter, which not only ensures the interpretability of the extracted features and enhance identification accuracy. Simulation results demonstrate that this scheme outperforms both the conventional distortion-models-based and the machine learning-based methods in terms of feature extraction performance.更多还原