基于稀疏表示的数字调制模式识别  

The Digital Modulation Recognition Based on Sparse Representation

作　　者：王振宇[1] 秦立龙[1] 黄振和[1] 

机构地区：[1]解放军电子工程学院,合肥230037

出　　处：《火力与指挥控制》2014年第4期10-13,18,共5页Fire Control & Command Control

基　　金：国家自然科学基金资助项目(61040007)

摘　　要：通过分析稀疏表示在模式识别应用的基础上,提出了一种基于稀疏表示的特征提取方法。该方法首先引入主成分分析对新的样本进行降维,然后利用降维后的训练样本构建稀疏线性模型,通过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。最后利用支持向量机分类器进行信号的分类识别。并在求解最小范数优化问题中,提出一种通用的解决方案,利用粒子群算法确定最优惩罚系数。新方法提取的特征值经计算机仿真研究证明,该算法具有较好的有效性和一定的工程应用性。With the analysis of the digital modulation recognition based on sparse representation,a new feature extraction method was proposed. Firstly,the principle component analysis was put forward to reduce the dimensionality of the new samples. Secondly,the sparse representation of the sample after dimensionality reduction was calculated by -minimization,the feature was extracted according to the distribution of the sparse coefficient values. Finally,the identification task was solved by using SVM classification machine. In order to determine the optimal punishment coefficient in minimum norm optimization problem,a universal solution using particle swarm optimization algorithm was proposed. The computer simulation results show that the performance of this feature values extracted by this new algorithm is feasible in engineering application.

关 键 词：调制识别 粒子群算法 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]