基于3D-Hibert能量谱和多尺度分形特征的通信辐射源个体识别

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

摘要/Abstract

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。

关键词: 特定辐射源识别, 3D-Hilbert能量谱, 多尺度, 差分盒维数, 多重分形维数

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.

Key words: specific emitter identification, 3D-Hilbert energy spectrum, multi-scale, differential box dimension

中图分类号:

韩洁,张涛,王欢欢,任东方. 基于3D-Hibert能量谱和多尺度分形特征的通信辐射源个体识别[J]. 通信学报, 2017, 38(4): 99-109.

Jie HAN,Tao ZHANG,Huan-huan WANG,Dong-fang REN. Communication emitter individual identification based on 3D-Hibert energy spectrum and multi-scale fractal features[J]. Journal on Communications, 2017, 38(4): 99-109.

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方法	K			仿真信号识别率
方法	K	SNR=10 dB	SNR=15 dB	SNR=20 dB	SNR= 25 dB	SNR= 30 dB
文献[15]方法		62%	65%	75%	82%	88%
文献[16]方法	2	65%	70%	81%	89%	92%
本文方法		92%	94%	96%	97%	100%
文献[15]方法		40%	46%	53%	60%	63%
文献[16]方法	3	45%	55%	61%	67%	70%
本文方法		75%	77%	81%	85%	87%

方法	K			实际信号识别率
方法	K	N=75	N=100	N=125	N=150	N=175	N=200
文献[15]方法		50%	52%	52%	54%	66%	77%
文献[16]方法	2	100%	100%	100%	100%	100%	100%
本文方法		100%	100%	100%	100%	100%	100%
文献[15]方法		48%	49%	49%	49%	55%	74%
文献[16]方法	3	66%	66%	66%	66%	66%	66%
本文方法		100%	100%	100%	100%	100%	100%