[1]QIN Lilong,WANG Zhenyu.Marginal spectrum and multifractal theoryand its application in modulation recognition[J].CAAI Transactions on Intelligent Systems,2014,9(6):756-762.[doi:10.3969/j.issn.1673-4785.201301031]
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Marginal spectrum and multifractal theoryand its application in modulation recognition

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 9 Number of periods: 2014 6 Page number: 756-762 Column: 学术论文—机器感知与模式识别 Public date: 2014-12-25
Title:
Marginal spectrum and multifractal theoryand its application in modulation recognition
Author(s):
QIN Lilong1; 2;  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
Keywords:
modulation recognition; marginal spectrum; fractal theory; support vector machine
CLC:
TP18;TN911.7
DOI:
10.3969/j.issn.1673-4785.201301031
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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