摘要
针对支持向量机的核函数对检测性能的影响,分析了核函数在特征空间中的作用.利用混响和目标回波的非高斯特性上的差异设计了特征核函数,即将核函数改进为任意2个样本高阶统计量的几何均值与原核函数乘积的形式,使其自适应调整核函数值,从而提高分类性能.实验证明了采用特征核函数后扩大了2类样本间的距离,并且仍然满足Mercer定理.将特征核支持向量机应用于高斯或非高斯分布混响背景中的信号检测,结合实际应用给出了训练和检测算法.实验及仿真研究表明,当选取2类样本差异较大的高阶统计量作为特征量时,混响背景为非高斯分布时,其检测性能优于匹配滤波器以及基于传统核函数的支持向量机.
Because kernel functions in a support vector machine influence detection performance, analysis of the effects of kernel features is needed to effectively design feature kernel functions. A new kernel was developed by the product of original kernel functions and the geometric mean value of the high order statistics of two arbitrary samples. The new kernel function is adaptively adjusted by using the non-Gaussian differences between reverberation and target echoes for improving classification performance. It was also proven that it is possible to enlarge the differences between two kinds of samples when using the feature kernel. The suggested kernel also satisfies the Mercer theorem. The proposed feature kernel support vector machine was used for signal detection of Gaussian and nonGaussian reverberation. The training and detecting algorithms used in testing are given. The results of experiments and simulations showed that when the data tested has significant differences in features, and the reverberation has a non-Gaussian distribution, the new algorithm's performance is better than matching filters and support vector machines based on traditional kernel functions.
出处
《哈尔滨工程大学学报》
EI
CAS
CSCD
北大核心
2009年第1期52-59,共8页
Journal of Harbin Engineering University
关键词
信号检测
混响
非高斯分布
高阶统计量
支持向量机
signal detection
reverberation
non-Gaussian distribution
high order statistics
support vector ma- chines