基于最大相关熵的通信辐射源个体识别方法

doi:10.11959/j.issn.1000-436x.2016283

通信学报 ›› 2016, Vol. 37 ›› Issue (12): 171-175.doi: 10.11959/j.issn.1000-436x.2016283

基于最大相关熵的通信辐射源个体识别方法

唐哲,雷迎科()

电子工程学院，安徽合肥 230037

出版日期:2016-12-25 发布日期:2017-05-15
基金资助:
国防科技重点实验室基金资助项目;总装预研基金资助项目;国家自然科学基金资助项目

Method of individual communication transmitter identification based on maximum correntropy

Zhe TANG,Ying-ke LEI()

Electronic Engineering Institute, Hefei 230037, China

Online:2016-12-25 Published:2017-05-15
Supported by:
National Defense Science and Technology Key Laboratory Fund Project;Preliminary Assembling Project Fund;The National Natural Science Foundation of China

摘要/Abstract

摘要：

采用相关熵度量辐射源细微特征之间的相似性，提出一种基于最大相关熵的通信辐射源个体识别方法。首先提取矩形积分双谱特征来表征辐射源个体差异，并基于最大相关熵准则构造优化函数；其次利用半二次优化技术，将非线性的优化问题转化为加权线性最小二乘问题；最后利用有效集算法得到稀疏系数构造分类器，实现通信辐射源的个体识别。在实际采集的同厂家同型号的FM电台数据集上，验证了方法的可行性与有效性。

关键词: 通信辐射源个体识别, 最大相关熵, 稀疏表示, 半二次优化, 线性互补问题, 有效集算法

Abstract:

To measure the similarity between the fine features of communication transmitters, a method of individual communication transmitter identification based on maximum correntropy was put forward. Firstly, the square integral bispectra was extracted to characterise the communication transmitters, and then optimization function based on maximum correntropy criterion was constructed. Secondly, the half-quadratic technique transformed the nonlinear op-timization problem to a weighted linear least squares problem. Finally, for identifying the communication transmitters, the sparse coefficient computed by active set algorithm was utilized to construct the classifier. Using the FM radios with same manufacturer and model, the actually collected data sets verified the feasibility and effectiveness of the method.

Key words: individual communication transmitter identification, maximum correntropy, sparse representation, half-quadratic, linear complementary problem, active set algorithm

唐哲,雷迎科. 基于最大相关熵的通信辐射源个体识别方法[J]. 通信学报, 2016, 37(12): 171-175.

Zhe TANG,Ying-ke LEI. Method of individual communication transmitter identification based on maximum correntropy[J]. Journal on Communications, 2016, 37(12): 171-175.

图/表 4

参考文献 18

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训练样本个数	NNC	SVM	CESRC	MCERC
20	49.94%	46.72%	54.81%	56.91%
40	59.18%	50.54%	66.36%	68.21%
60	65.33%	54.38%	72.17%	74.79%
80	67.00%	55.00%	76.00%	76.95%
100	68.31%	57.69%	76.75%	79.56%
120	71.67%	57.17%	81.25%	81.83%
140	70.13%	56.00%	78.88%	80.50%
160	73.25%	58.25%	81.50%	83.25%

基于最大相关熵的通信辐射源个体识别方法

Method of individual communication transmitter identification based on maximum correntropy

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