基于K-SVD和稀疏表示的数字调制模式识别  

Digital Modulation Recognition Based on Sparse Representation and K-SVD

作　　者：王振宇[1] 秦立龙[2] 刁俊良[1] 

机构地区：[1]解放军电子工程学院,合肥230037 [2]国防科学技术大学电子科学与工程学院,长沙410073

出　　处：《计算机科学》2013年第10期65-67,91,共4页Computer Science

基　　金：国家自然科学基金项目(61040007)资助

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,通过分析基于稀疏表示的模式识别,提出了一种基于K-SVD和稀疏表示的特征提取方法。该方法首先引入主成分分析对样本进行降维,然后利用K-SVD算法构造稀疏字典并构建稀疏线性模型,最后通过l1范数最优化求解测试样本的稀疏系数,根据稀疏系数的分布提取特征值。利用支持向量机分类器进行信号的分类识别,仿真研究证明,新方法提取的特征值具有较好的有效性。With the analysis of the pattern recognition based on sparse representation,a new feature extraction method using K-SVD and sparse representation was proposed to improve the accuracy of the digital modulation recognition un-der the low signal-to-noise ratio. Firstly, the principle component analysis was put forward to reduce the dimensionality of the samples. Secondly, the sparse dictionary was constructed by the algorithm of K-SVD. Finally, the sparse represen-tation of the sample was calculated by e^1-minimization, and the feature was extracted according to the distribution of the sparse coefficient values. The identification problem was solved by using SVM classification machine. The simulation re-sults indicate that the performance of this feature values extracted by this new algorithm is feasible in engineering appli-cation.

关 键 词：调制识别 稀疏字典 稀疏表示 支持向量机 

分 类 号：TN911.7[电子电信—通信与信息系统]