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

• 作者: Bin YANG Xin ZHOU Ying WU
• 来源期刊: 无线传感网络(英文)
• 学科: 工学
• 关键词: HIGH-ORDER CUMULANTS Support VECTOR Machine KERNEL Function SIGNAL Classification
• 年，卷（期）: 2010,（1）
• 页码范围: 48-52
• 页数: 5页
• 分类号: TP39

摘要

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.