摘要
针对目前支持向量机多类分类方法存在的缺点,文中对支持向量机的高斯核函数进行改进,并提出一种结合留一法和单一验证法的参数选择新方案。基于3种雷达目标的HRRP数据,设计了相应的预处理算法,利用改进的SVM分类法用于高分辨距离像的雷达目标识别。从实验目标姿态稳定性、训练集大小稳定性和抗噪能力三个方面验证改进SVM的稳健性。
To solve the problems and defects of existing methods in Support Vector Machine（SVM） multiclass classification, an improved Gaussian kernel in SVM is proposed and a new SVM model selection scheme combining Leave-One-Out method with One-Validation method is presented in this paper. Based on the High Resolution Range Profile（HRRP） of three-type target, a preprocessing method is designed, A classification algorithm for HRRP based on this improved SVM is applied. Finally, experimental results prove that the improved SVM classifier has better performance on target-aspect stability, training set-size stability and anti-noise ability than traditional SVM.
出处
《通信技术》
2009年第1期300-302,共3页
Communications Technology
