Recognition of far-region nuclear electromagnetic pulse based on wavelet fractal technique
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摘要: 针对远区核爆电磁脉冲(NEMP)和闪电电磁脉冲(LEMP)的识别率不能满足实际需求的问题，提出了一种基于小波包分形技术的识别方法。首先，对实测的NEMP和LEMP做插值、归一化等预处理；然后，基于小波包理论对预处理后的信号进行2层小波包分解，并利用小波包系数重构信号的分形维数，组成信号的特征向量；最后，采用最小二乘支持向量机(LSSVM)作为分类器，利用五折交叉验证法选取最优的模型参数，将特征向量输入分类器中进行训练后获得测试结果。实验结果表明，小波包分形方法在NEMP和LEMP的识别上效果显著，平均识别率达到99%以上，具有较高的应用价值。Abstract: Aiming at the difficulty to identify far-region nuclear and lightning electromagnetic pulse correctly, a recognition method based on wavelet fractal technique was proposed. First, the signals of nuclear electromagnetic pulse (NEMP) and lightning electromagnetic pulse (LEMP), preprocessed by interpolation and normalization, were decomposed by wavelet packet at level two. Then, fractal dimensions of reconstructed signal from wavelet packet coefficients were calculated to form eigenvectors of NEMP and LEMP. Least squares support vector machine was chosen as the classifier and the model parameters were obtained by the five-fold cross validation. Finally, the eigenvectors were input into least squares support vector machine (LSSVM) for training and testing. The experimental results show that the combination of wavelet fractal technique and LSSVM performs well in recognition of NEMP and LEMP; the average recognition rate is more than 99%.
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图 4 不同组合下小波包分形维数特征分布
Figure 4. Distribution diagram of the fractal dimensions on wavelet packet
表 1 不同分类器的识别结果
Table 1. Accuracy of NEMP and LEMP in different classifier
classifier accuracy/% NEMP LEMP ANN 95.00 98.42 SVM 95.00 98.64 LSSVM 99.00 99.07 
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