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

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.