一种通信辐射源正交调制细微特征提取和识别方法

      A method for extracting and identifying fine features of quadrature modulator in communication emitters

      • 摘要: 针对Transformer网络在通信辐射源识别中局部细微特征不敏感和内存容量需求大的问题,提出了一种基于局部双谱和Longformer网络的通信辐射源正交调制细微特征提取和识别方法。首先分析了正交调制器的失配特性,建立了正交调制器输出通信信号的镜频干扰数学模型,并推导了正交分解和符号同步分段后的通信辐射源信号的局部离散双谱表示式;然后分析了Longformer网络中基于位置编码的局部自注意力与全局自注意力结合的稀疏自注意力机制,给出了基于Longformer网络和softmax分类器的正交调制细微特征识别分类框架;最后对4台同类型正交频分复用(orthogonal frequency division multiplex, OFDM)辐射源正交调制细微特征的分类识别进行了计算机仿真,分析了该方法的识别性能、抗噪声性和网络复杂度。仿真结果表明:Longformer网络比Transformer网络和径向基函数(radial basis function, RBF)识别效果更好,准确率达到了90%以上。

         

        Abstract: Aiming at the problems of insensitive local subtle features and large memory capacity required in the recognition of communication emitters in Transformer network, we propose a method for extracting and identifying fine features of quadrature modulator of communication radiation sources based on local bispectrum and Longformer networks. Firstly, we analyzed the mismatch characteristics of orthogonal modulators and established a mathematical model for mirror frequency interference in the output communication signal of quadrature modulators; Secondly, we derived the local discrete bispectral representation of the communication radiation source signal after orthogonal decomposition and symbol synchronization segmentation. Then, we analyzed the sparse self attention mechanism combining position encoding local self attention and global self attention in Longformer networks, and proposed an orthogonal modulation fine feature recognition and classification framework based on Longformer networks and softmax classifiers. Finally, We simulated the classification and recognition process of fine features of four quadrature modulators of the same type of orthogonal frequency division mlltiplex OFDM radiation source, and analyzed the recognition performance, noise resistance, and network complexity of this method.The simulation results show that Longformer network has better recognition performance than Transformer network and radial basis function network, achieving an accuracy of over 90%.

         

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