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| 基于支持矢量机和循环累积量的调制识别算法 | |
| 引用本文： | 李建东,冯祥. 基于支持矢量机和循环累积量的调制识别算法[J]. 系统工程与电子技术, 2007, 29(4): 520-523 |
| 作者姓名： | 李建东 冯祥 |
| 作者单位： | 西安电子科技大学ISN国家重点实验室信息科学研究所,陕西,西安,710071 |
| 基金项目： | 国家自然科学基金;教育部高等学校优秀青年教师教学科研奖励计划;国家自然科学基金;国家高技术研究发展计划(863计划);教育部科学技术研究重点项目;高等学校博士学科点专项科研项目 |
| 摘 要： | 利用通信信号的循环平稳特性,在循环累积量域内构造信号分类特征矢量,采用支持矢量机将分类特征矢量映射到高维空间并构建最优分类超平面,实现对QAM调制信号的自动识别。该算法解决了样本在低维空间中的不可分问题,具有良好的泛化推广性能,并且可在多种调制信号环境下实现对感兴趣信号类型的识别。理论分析和仿真结果均证明了算法的正确性和有效性。 |
| 关 键 词： | 调制识别 循环累积量 循环平稳性 支持矢量机 |
| 文章编号： | 1001-506X(2007)04-0520-04 |
| 修稿时间： | 2006-03-29 |
Modulation classification algorithm based on support vector machines and cyclic cumulants |
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| LI Jian-dong,FENG Xiang. Modulation classification algorithm based on support vector machines and cyclic cumulants[J]. System Engineering and Electronics, 2007, 29(4): 520-523 | |
| Authors: | LI Jian-dong FENG Xiang |
| Abstract: | A support vector machines(SVM) based algorithm for the automatic classification of QAM signals is proposed.The algorithm utilizes the cyclostationary property of communication signals and presents classification features in cyclic cumulants domain.SVM maps the classification features nonlinearly into the high dimension space and constructs the optimal separating hyperplane in the space to classify interesting signals.The algorithm resolves the non-separable problem in low dimension space and has high generalization performance.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. |
| Keywords: | modulation classification cyclic cumulants cyclostationary support vector machine |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |