基于回归分析理论的辐射源个体识别技术

针对目前辐射源个体识别未能将信号特征与硬件组成相联系的问题，该文使用高阶谱分析和变分模态分解(VMD)两种特征提取手段，进行研究分析，采用围线双谱积分以及改进变分模态分解技术对半实物平台仿真信号以及软件仿真(ADS)输出信号进行特征提取并分析。通过软件仿真定量分析辐射源相位噪声以及功率放大电路非线性失真对信号无意调制特征的影响，对变量进行相关性分析，并对其中显著相关的变量进行回归拟合，得到其相关回归函数。然后利用硬件与特征的相关性，改进传统支持向量机(SVM)分类器，构建相关性权重支持向量机分类器。最后分别以软件仿真输出信号以及半实物仿真平台实测信号为样本进行验证，结果表明，同信噪比下权重支持向量机与传统支持向量机相比分类准确率提升在10%以上。

Specific Emitter Identification Based on Regression Analysis Theory

Focusing on the problem that the signal characteristics are not related to the hardware composition in the specific emitter identification, two feature extraction methods are used in this paper: high-order spectral analysis and Variational Modal Decomposition (VMD) for research and analysis. The surrounding-line bispectral integration and improved VMD technology are used to extract and analyze the features of the hardware in the semi-physical simulation signal and Advanced Design System (ADS) output signal. Through ADS, the influence of emitter phase noise and nonlinear distortion of power amplifier on signal unintentional modulation characteristics is quantitatively analyzed, the correlation of variables is analyzed, and the significantly related variables are regressed and fitted to obtain their correlation regression function. Using the correlation between hardware and features, the traditional Support Vector Machines (SVM) classifier is improved to construct a correlation weight SVM classifier. Finally, the results show that the classification accuracy of weighted SVM is improved by more than 10% compared with single core SVM under the same signal-to-noise ratio.