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基于高阶循环累积量和支持矢量机的分级调制分类算法 被引量：2

Hierarchical Modulation Classification Algorithm Based on Higher-order Cyclic Cumulants and Support Vector Machines
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摘要 利用观测样本的高阶循环累积量特征,提出一种基于支持矢量机的分级调制分类算法,实现了对QAM调制信号的自动识别。该算法具有较快的分类器训练速度和较低的复杂度,对时延和相位旋转具有稳健性,并可在干扰环境下实现对感兴趣信号调制类型的识别。理论分析和仿真结果均证明了算法的正确性和有效性。 A support vector machines （SVM） based hierarchical algorithm for the automatic classification of QAM modulation signals is proposed. The algorithm utilizes the cyclostationary property of communication signals and presents classification features in cyclic cumulants domain. The algorithm is less complex computationally and has faster classifier training speed compared with other algorithms. Moreover, it is robust to the presence of time delay and phase offsets. Interesting signals can also be classified under the presence of interference signals. The efficiency of the proposed classification algorithm is verified via theoretical analysis and extensive simulations.
作者 冯祥 元洪波
机构地区 空军第一航空学院基础部
出处 《电讯技术》 北大核心 2012年第6期878-882,共5页 Telecommunication Engineering
关键词 QAM调制信号 自动识别 调制分类 高阶循环累积量 循环平稳性 支持矢量机 QAM modulation signal automatic identification modulation classification higher - order cyclic cu- mulants cyclostationary support vector machine
分类号 TN911 [电子电信—通信与信息系统]
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参考文献12

  • 1Kim K,Spooner C M,Akbar I. Specific emitter identification for congnitive radio with application to IEEE 802.11[A].New Orleans,LO:IEEE,2008.1-5.
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二级参考文献17

  • 1吕新正,魏平,肖先赐.利用高阶累积量实现数字调制信号的自动识别[J].电子对抗技术,2004,19(6):3-6. 被引量：42
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共引文献15

同被引文献23

引证文献2

二级引证文献16

电讯技术
2012年 第6期

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