中文摘要：

针对通信辐射源的个体识别问题,提出一种基于希尔伯特—黄变换（HHT,Hilbert-Huang transform）和多尺度分形特征的新方法。首先,通过HHT得到时频能量谱,将其视为三维空间中的复杂曲面,即3D-Hilbert能量谱;然后,利用分形理论通过多尺度分块提取差分盒维数和多重分形维数二维特征组成特征向量;最后,采用支持向量机分类器结合二维特征向量实现通信辐射源的个体分类。分别利用仿真信号和调制方式相同的实际通信信号,验证并对比了所提方法与另外2种方法在2类及3类目标情况下的识别性能。实验结果表明,所提方法的识别率远高于其他2种方法,能够克服低信噪比和少训练样本数量对识别性能的负面影响,证明了所提特征的稳定性、充分性及可分性。

英文摘要：

For communication emitter identification, a novel method based on Hilbert-Huang transform （HHT） and multi-scale fractal features was proposed. First, the time frequency energy spectrum was derived via HHT, which was called a complicated curved surface in the three-dimension space, namely, 3D-Hilbert energy spectnma. Then, the differential box dimension and the multi-fractal dimension was extracted to compose the feature vector under multi-scale segmentation using fractal theory. Finally, communication emitter individual identification was obtained using the two di-mensions of features above and the support vector machine （SVM）. Moreover, the novel method was compared with two existing methods to identify simulated and actual signals with different and the same modulation modes, respectively. Results show that the identification rate of the novel method is higher than that of the two other methods. The features extracted by the novel method have high stability, sufficiency, and identifiability, also outweigh the negative effects of the change of signal-to-noise ratio and the number of training samples and emitters.