Individual Identification Method for Communication Emitters Based on Improved Variational Modal Decomposition and Multiple Features
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摘要: 针对通信辐射源指纹特征难以提取和单一特征识别率不高的问题，并考虑到通信辐射源细微特征的非线性、非平稳特点，该文提出了一种基于改进变分模态分解和多特征的通信辐射源个体识别方法。首先，为了获得变分模态分解的分解层数和惩罚因子的最优组合，采用鲸鱼优化算法对通信辐射源符号波形信号的变分模态分解方法进行了改进，该方法以序列复杂度为停止准则，使每个符号波形信号能够自适应地分解出包含非线性指纹特征的高频信号分量和数据信息的低频分量；然后，根据相关阈值选取能够最佳表征辐射源非线性特征的高频信号分量层数，分别对其提取模糊熵、排列熵、Higuchi维数以及Katz维数并组成多域联合特征向量；最后，通过卷积神经网络实现通信辐射源个体识别分类，利用ORACLE公开数据集进行实验。实验结果表明：该方法有较高的识别精度且具有良好的抗噪声性能。Abstract: Aiming at the difficulties in extracting fingerprint features from communication emitters and the low recognition rate of single features, considering the nonlinear and non-stationary characteristics of subtle features of communication emitters, this paper proposes an individual identification method for communication emitters based on improved variational mode decomposition and multiple features. Firstly, in order to obtain the optimal combination of decomposition levels and penalty factors for variational mode decomposition, the variational modal decomposition of communication emitter symbol waveform signals is improved with whale optimization algorithm, in which the sequence complexity is used as the stopping criterion in this method to enable each symbol waveform signal to adaptively decompose several high-frequency signal components containing nonlinear fingerprint features and low-frequency components of data information; Then, according to the relevant threshold, the number of high-frequency signal component layers is selected that can best represent the nonlinear characteristics of the radiation source and the fuzzy entropy, permutation entropy, Higuchi dimension, and Katz dimension are extracted to form a multi-domain joint feature vector; Finally, the recognition and classification of communication emitters are achieved through convolutional neural networks, and recognition and classification experiments are conducted using the Oracle public dataset. The experimental results show that this method has high recognition accuracy and good noise immunity.
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表 1 不同层数在不同信噪比下的识别率(%)
层数 信噪比SNR(dB) –4 dB –2 dB 0 dB 2 dB 4 dB 3 66.5 69.5 72.1 73.4 75.3 4 64.2 71 73.2 80.3 81 5 67.1 73.8 76 83.1 89 6 69.5 77.9 82 89.8 96.3 7 68.8 78 83.3 89.1 92.6 
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