Simulation Study of SVM-Based Approach in Extracting Parameters for Electromagnetic Detection of Buried Object  

作　　者：ZHANG Qinghe HE Siyuan ZHU Guoqiang 

机构地区：[1]School of Science, China Three Gorges University, Yichang443002, Hubei, China [2]School of Electronic Information, Wuhan University,Wuhan 430072, Hubei, China

出　　处：《Wuhan University Journal of Natural Sciences》2010年第6期510-515,共6页武汉大学学报（自然科学英文版）

基　　金：Supported by the National Natural Science Foundation of China (50679037,60671040)

摘　　要：In this paper,a new method for extracting the parameters of buried object is proposed.The center position and dielectric properties of 2-D buried object are estimated by means of a regression technique based on support vector machine（SVM）.The proposed method,after a proper training procedure,is able to reconstruct the center position and dielectric properties of a buried object inside a given investigation domain.Numerical simulation results indicate that SVM-based approach shows higher accuracy than the back-propagation neural networks（BPNN） algorithm.In this paper,a new method for extracting the parameters of buried object is proposed.The center position and dielectric properties of 2-D buried object are estimated by means of a regression technique based on support vector machine（SVM）.The proposed method,after a proper training procedure,is able to reconstruct the center position and dielectric properties of a buried object inside a given investigation domain.Numerical simulation results indicate that SVM-based approach shows higher accuracy than the back-propagation neural networks（BPNN） algorithm.

关 键 词：buried object detection inverse scattering support vector machine（SVM） 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置] V328[自动化与计算机技术—控制科学与工程]