基于云模型和支持向量机的辐射源识别算法

Approach Based on Cloud Model and SVM for Emitter Identification

针对雷达辐射源识别中支持向量机无法解决多分类问题,且只能处理定量数据,不能处理定性数据,因而造成辐射源不能正确识别的问题,提出了一种基于云模型和核函数的支持向量机多分类识别算法,该算法利用云模型来实现定性概念到定量区间值的转换,利用改进的核函数实现区间类型的矢量输入到区间类型型号输出的非线性映射,并利用决策树来解决支持向量机多分类问题。仿真实验表明,该算法不仅能处理区间类型的输入矢量,而且能够处理标量类型的输入矢量,且对特征参数相似度很高的雷达辐射源信号有较好的分类效果。

For radar emitter recognition,support vector machine can handle only quantitative data,while it can not deal with qualitative data,besides it will not resolve the multi-classification problems,resulting in a problem that the radiation source can not be identified correctly. So this paper proposes a recognition algorithm of support vector machine( SVM) and multi-classification based on cloud model and kernel function. The algorithm achieves the conversion from the qualitative concept to quantitative interval value by using the cloud model and the nonlinear mapping from the vector input of interval type to the model output of interval type by using the modified kernel function,and using SVM of decision tree modified the problem of multi-class classifier. The simulation results show that this method can not only deal with input vector of the interval type,but also handle the input vector of the scalar type. What's more,for radar emitter signals which have the high similarity in characteristic parameters,it has better classification results.