基于高阶累积量和支持向量机的信号调制分类

Signal Modulation Classification Based on Support Vector Machines and High-Order Cumulants

给出了一种基于支持向量机的数字调制信号分类器设计方法。将接收信号的二阶、四阶、六阶累积量作为分类特征向量,利用支持向量机把分类特征向量映射到一个高维空间,并在高维空间中构造最优分类超平面以实现信号分类。文中选用了径向基核函数,使用一对一或一对余多类构造法,并利用交叉验证网格搜索法优化核函数参数,构建了快速稳定的多类支持向量机分类器。仿真实验表明:基于支持向量机的分类器具有很高的分类性能和良好的稳健性。

A classification method based on Support Vector Machine （SVM） is given in the digital modulation signal classification. The second, fourth and sixth order cumulants of received signals are used as classification vectors, then the kernel thought is used to map the feature vector to the high dimensional feature space and the optimum separating hyperplane is constructed in space to realize signal recognition. In order to build an effective and robust SVM classifier, the radial basis kernel function is selected, one against one or one against the rest of multi-class classifier is designed, and the method of parameter selection using cross-validating grid is adopted. Simulation experiments show that the classifier based on SVM has high performance and is more robust.