| 基于熵特征和支持向量机的调制识别方法 |
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| 引用本文： | 李一兵,葛娟,林云.基于熵特征和支持向量机的调制识别方法[J].系统工程与电子技术,2012,34(8):1691-1695. |
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| 作者姓名： | 李一兵 葛娟 林云 |
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| 作者单位： | 哈尔滨工程大学信息与通信工程学院, 黑龙江 哈尔滨 150001 |
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| 基金项目： | 国家重点基础研究发展计划(973计划)，船舶工业国防科技预研项目，中央高校基本科研业务费专项资金(HEUCF100810)资助课题 |
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| 摘 要： | 通信信号调制识别在非合作通信领域是一项重要的研究课题。针对当前算法计算量大，能识别的调制类型少的特点，提出了一种基于熵特征和支持向量机（support vector machine, SVM）的调制识别新方法。该算法通过提取接收信号的多维熵特征，作为调制识别的特征参数，并利用基于二叉树的SVM作为分类器，对接收信号进行调制识别。除了信号的信噪比，该算法不需要信号带宽和载频等其他先验知识。理论分析与计算机仿真结果表明，该方法具有很高的识别率，计算量小，具有很好的应用价值。
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| 关 键 词： | 调制识别 非合作通信 支持向量机 多维熵特征 |
Modulation recognition using entropy features and SVM |
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| LI Yi-bing
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Ge Juan
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LIN Yun.Modulation recognition using entropy features and SVM[J].System Engineering and Electronics,2012,34(8):1691-1695. |
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| Authors: | LI Yi-bing Ge Juan LIN Yun |
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| Institution: | College of the Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China |
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| Abstract: | The modulation recognition of communication signals is an important research issue in non-cooperative fields.A method based on entropy features and support vector machine(SVM) for modulation recognition is put forward.Multidimensional entropy features are extracted as the input coefficients of the classifier.The performance of the binary tree based SVM classifier is investigated using these entropy features.Besides signal-to-noise ratio,the algorithm needs no further information for the received signal such as signal bandwidth or carrier frequency.Theoretical analysis and simulation results show that the algorithm is practically valuable for its high recognition accuracy and less computation load. |
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| Keywords: | modulation recognition non-cooperative communication support vector machine(SVM) multidimensional entropy feature |
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