The MPSK signals modulation classification based on Kernel methods

In this paper, a new classification method based on Kernel Fisher Discriminant Analysis(KFDA) is brought forward in the MPSK signals modulation classification. The fourth order cumulants of the received signals are used as the classification vector firstly, then the kernel thought is used to map the feature vector impliedly to the high dimensional feature space and linear fisher discriminant analysis is applied to signal classification. The two classifiers based on kernel function - Support Vector Machine and Kernel Fisher Discriminant Analysis are introduced in detail. In order to build effective and robust SVM and KFDA classifiers and compared with each other, the radial basis kernel function is selected, one against one or one against rest of multi-class classifier is designed, and method of parameter selection using cross-validating grid is adopted. Through the experiments it can be concluded that compared with SVM classifier, KFDA can get almost the same classification accuracy and requires less time.