Communication emitter individual identification based on 3D-Hibert energy spectrum and multi-scale fractal features

Jie HAN; Tao ZHANG; Huan-huan WANG; Dong-fang REN

doi:10.11959/j.issn.1000-436x.2017080

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Communication emitter individual identification based on 3D-Hibert energy spectrum and multi-scale fractal features

Papers | 更新时间：2024-06-05

- Communication emitter individual identification based on 3D-Hibert energy spectrum and multi-scale fractal features
- Journal on Communications Vol. 38, Issue 4, Pages: 99-109(2017)
- 作者机构：
  
  解放军信息工程大学信息系统工程学院，河南郑州 450001
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61572518)
- DOI：10.11959/j.issn.1000-436x.2017080
  CLC： TN975;TP911.7
- Online First：2017-04，
  
  Published：25 April 2017
- 稿件说明：
移动端阅览
Jie HAN, Tao ZHANG, Huan-huan WANG, et al. Communication emitter individual identification based on 3D-Hibert energy spectrum and multi-scale fractal features[J]. Journal on Communications, 2017, 38(4): 99-109.
DOI：

Jie HAN, Tao ZHANG, Huan-huan WANG, et al. Communication emitter individual identification based on 3D-Hibert energy spectrum and multi-scale fractal features[J]. Journal on Communications, 2017, 38(4): 99-109. DOI： 10.11959/j.issn.1000-436x.2017080.

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。

Abstract

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 spectrum.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 dimensions 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.

关键词

Keywords

references

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