Digital modulation classification using kernel fisher discriminant analysis for reconfigurable software radio

In this paper, a new classification method based on kernel fisher discriminant analysis is used in the digital signals classification. The second, fourth and sixth order cumulants of the received signals are used as the classification vector 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. The radial basis kernel function is selected and one against one or one against rest of multi-class classifier is designed and method of parameter selection using cross-validating grid is adopted to build an effective and robust KFDA classifier. Through the experiments it can be concluded that compared with SVM classifier, KFDA can get almost the same classification accuracy and requires less time.