Abstract:

A new classification method based on kernel Fisher discriminant analysis(KFDA) is used in the MPSK automatic signal classification. The fourth cumulants of the received signals are used as the classification vectors firstly, then the kernel thought is used to map the feature vector to the high dimensional feature space and linear fisher discriminant analysis is applied to signal classification. In order to build an effective and robust KFDA classifier, the radial basis kernel function is selected, one against one or one against rest of multiclass classifiers is designed, and a method of parameter selection using crossvalidating grid is adopted. Through the experiments it can be concluded that compared with the SVM classifier, KFDA can almost get the same classification accuracy and requires less time.