基于时频图像分析的核爆与雷电电磁脉冲识别

根据核爆和雷电电磁脉冲信号非平稳、非线性特点，对核爆电磁脉冲(NEMP)和雷电电磁脉冲(LEMP)信号进行了Hilbert谱分析，计算了二者Hilbert谱的图像区域特征，对二者进行了识别研究，并且从NEMP和LEMP不同的产生机理上对识别结果进行了分析。实验结果表明：以Hilbert谱的面积和重心，以及六维图像区域特征作为特征，对NEMP和LEMP的识别率达到了90％以上，可以对二者进行有效识别。

Discrimination of nuclear explosion and lightning electromagnetic pulse using time-frequency image analysis

In this paper, the Hilbert spectrum is used to analyze the nuclear explosion and lightning electromagnetic pulse for NEMP and LEMP signals based on their non-stationary and non-linear characters. The region features of Hilbert spectrum of NEMP and LEMP signals are calculated to discriminate them, and the discrimination result is analyzed based on different mechanisms of the nuclear explosion and lightning. The experiment results indicate that, using the nearest neighbor pattern classification method, the average discrimination rate of NEMP and LEMP signals is over 90% based on area, the center of gravity and six dimension image region features of Hilbert spectrum. Finally, we get a conclusion that the image region features of the Hilbert spectrum are effective features to discriminate NEMP and LEMP.