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has been cited by the following article:

• TITLE: Signal Classification Method Based on Support Vector Machine and High-Order Cumulants

  AUTHORS: Xin ZHOU, Ying WU, Bin YANG

  KEYWORDS: High-Order Cumulants, Support Vector Machine, Kernel Function, Signal Classification

  JOURNAL NAME: Wireless Sensor Network, Vol.2 No.1, January 13, 2010

  ABSTRACT: In this paper, 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 the received signals are used as classification vectors firstly, 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 rest of multi-class classifier is designed, and method of parameter selection using cross- validation grid is adopted. Through the experiments it can be concluded that the classifier based on SVM has high performance and is more robust.