基于模糊超球面SVM的雷达高分辨距离像识别  

Radar High Range Resolution Profile Identification Based on Fuzzy Hypersphere SVM

作　　者：史朝辉[1] 王坚[1] 华继学[1] 

机构地区：[1]空军工程大学防空反导学院,西安710051

出　　处：《弹箭与制导学报》2015年第3期166-169,共4页Journal of Projectiles,Rockets,Missiles and Guidance

基　　金：国家自然科学基金(61273275)资助

摘　　要：高分辨距离像(HRRP)分类是对雷达复杂目标分类的一种重要方法。标准的一对一超球面SVM多值分类方法需要训练k(k-1)个子分类器,计算量大、训练时间长,并且存在决策盲区,不适宜用来进行HRRP目标识别。为了减少分类器数量,提高训练速度,文中根据超球面的几何特征引入了一种"倒数对称"的一维隶属度,构造了模糊超球面SVM分类器,该方法仅需训练k(k-1)/2个子分类器,既提高了训练速度又解决了决策盲区,HRRP实测数据识别实验表明了该方法的有效性。High resolution range profile （HRRP） classification is an important method for radar complex target classification. Since stand- ard one-against-one hypersphere support vector machine （SVM） has the defects of large computation, long training time for its k （ k - 1 ） sub-classifiers, and, decision bland area, it is not fit for HRRP target recognition. In order to reduce the number of classifiers in the one- against-one multi-class, a new one-dimensional membership function based on geometry feature named ＂reciprocal symmetry ＂ has been defined, and the corresponding fuzzy hypersphere SVM has been given. This new method only needs k （k - 1 ）/2 sub-classifiers, it not only improves the training speed, but also clears away the decision bland area. The HRRP real data experimental results show that this algorithm has better HRRP classification performance.

关 键 词：超球面支持向量机 高分辨距离像 隶属度 

分 类 号：TN957[电子电信—信号与信息处理]