| 基于复杂度特征的数字调制模式识别 |
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| 引用本文： | 赵守林,秦立龙,陈翔.基于复杂度特征的数字调制模式识别[J].计算机工程与应用,2015,51(4):226-231. |
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| 作者姓名： | 赵守林 秦立龙 陈翔 |
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| 作者单位： | 1.安徽国防科技职业学院，安徽 六安 237011
2.国防科学技术大学 电子科学与工程学院，长沙 410073
3.解放军电子工程学院，合肥 230037 |
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| 基金项目： | 国家自然科学基金项目(No.61040007)；安徽省电工电子与自动化省级示范实验实训中心项目(No.20101687)。 |
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| 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，在对复杂度理论加以分析的基础上，提出了一种新的特征提取方法。该方法首先引入希尔伯特-黄变换求得样本的边际谱，然后利用分形和Lempel-Ziv复杂度的方法提取用于调制识别的特征参数，最后利用RBF神经网络分类器进行数字信号调制模式的分类识别。仿真结果表明该算法具有较好性能。
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| 关 键 词： | 调制识别 边际谱 复杂度 RBF神经网络 分形原理 |
Digital modulation recognition based on complexity measure |
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| ZHAO Shoulin,QIN Lilong,CHEN Xiang.Digital modulation recognition based on complexity measure[J].Computer Engineering and Applications,2015,51(4):226-231. |
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| Authors: | ZHAO Shoulin QIN Lilong CHEN Xiang |
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| Affiliation: | 1.Anhui Vocational College of Defense Technology, Lu’an, Anhui 237011, China
2.School of Electronic Science and Engineering, National University of Defence Technology, Changsha 410073, China
3.Electronic Engineering Institute of PLA, Hefei 230037, China |
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| Abstract: | On the basis of the marginal spectrum and complexity theory, a new feature extraction method is proposed to improve the accuracy of the digital modulation recognition under the low signal-to-noise ratio. Firstly, the Hilbert-Huang Transform is put forward to obtain the marginal spectrum of the samples. Secondly, the fractal dimensions and the Lempel-Ziv complexity of the samples after Hilbert-Huang Transform are calculated to extract the feature parameters. Finally, the identification problem is solved by using artificial neural network. The simulation results verify the performance of the proposed algorithm. |
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| Keywords: | modulation recognition marginal spectrum complexity measure RBF neural network fractals theory |
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