[1]秦立龙,王振宇.边际谱和多重分形在调制模式识别中的应用[J].智能系统学报,2014,9(06):756-762.[doi:10.3969/j.issn.1673-4785.201301031]
 QIN Lilong,WANG Zhenyu.Marginal spectrum and multifractal theoryand its application in modulation recognition[J].CAAI Transactions on Intelligent Systems,2014,9(06):756-762.[doi:10.3969/j.issn.1673-4785.201301031]
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边际谱和多重分形在调制模式识别中的应用
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第9卷
期数:
2014年06期
页码:
756-762
栏目:
出版日期:
2014-12-25

文章信息/Info

Title:
Marginal spectrum and multifractal theoryand its application in modulation recognition
作者:
秦立龙12 王振宇2
1. 国防科学技术大学 电子科学与工程学院, 湖南 长沙 410073;
2. 解放军电子工程学院 通信对抗工程系, 安徽 合肥230037
Author(s):
QIN Lilong12 WANG Zhenyu2
1. School of Electronic Science and Engineering, National University of Defence Technology, Changsha 410073, China;
2. Department of Communication Countermeasure Engineering, Electronic Engineering Institute, Hefei 230037, China
关键词:
调制识别边际谱分形理论支持向量机
Keywords:
modulation recognitionmarginal spectrumfractal theorysupport vector machine
分类号:
TP18;TN911.7
DOI:
10.3969/j.issn.1673-4785.201301031
文献标志码:
A
摘要:
为了提高数字信号调制模式识别在低信噪比下的正确率,根据对边际谱和多重分形理论原理的分析,提出了一种新的基于多重分形理论的特征提取方法。该方法首先引入HHT变换求得样本的边际谱,不同调制模式的边际谱具有明显的差异性,可以利用分形的方法提取边际谱的分形维数作为调制识别的特征参数。最后利用支持向量机分类器进行信号的分类识别。并在求解支持向量机优化问题中,利用通用的粒子群算法确定了最优系数。计算机仿真研究证明,新方法提取的特征能有效地提高识别正确率,具有较好的工程应用性。
Abstract:
Through the analysis of the marginal spectrum and multifractal theory, a new feature extraction method based on multifractal theory was proposed to improve the accuracy of the digital modulation recognition under the low signal-to-noise ratio. First, the Hilbert-Huang transform was put forward to obtain the marginal spectrum of the samples. There are differences among different modulation modes. The fractal dimensions of the sample after Hilbert-Huang transform were calculated by the fractal method. Next, the feature was extracted. Finally, the identification task was solved by using SVM classification machine. In order to determine the optimal coefficient of the support vector machine, a universal particle swarm optimization algorithm was used. The computer simulation results showed that the performance of this feature extracted by the new algorithm efficiently improves the accuracy of modulation recognition and could be feasible to use in engineering applications.

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备注/Memo

备注/Memo:
收稿日期:2013-1-16;改回日期:。
基金项目:国家自然科学基金资助项目(61040007).
作者简介:秦立龙,男,1988年生,博士研究生,主要研究方向为调制模式识别;王振宇,男,1956年生,副教授,主要研究方向为信号处理。
通讯作者:秦立龙.E-mail:tank2908989@163.com.
更新日期/Last Update: 2015-06-16