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物探与化探  2019, Vol. 43 Issue (4): 899-903    DOI: 10.11720/wtyht.2019.1363
     方法研究·信息处理·仪器研制 本期目录 | 过刊浏览 | 高级检索 |
基于马氏距离模板特征的地雷目标识别研究
王成浩1,2, 程丹丹1
1. 中国电波传播研究所,山东 青岛 266107
2. 近地探测技术重点实验室,江苏 无锡 214035
A study of landmine target recognition based on Mahalanobis distance template feature
Cheng-Hao WANG1,2, Dan-Dan CHENG1
1. China Research Institute of Radiowave Propagation, Qingdao 266107, China
2. Science and Technology on Near Surface Detection Laboratory, Wuxi 214035, China
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摘要 

通过探地雷达进行探雷是一个重要的应用方向,其对非金属地雷或者金属含量少的地雷的探测效果显著。本文针对探地雷达对地雷进行探测时目标特征提取困难的问题,提出了基于马氏距离模板特征的SVM识别算法,并给出了识别结果。该方法能有效提取地雷目标特征,有助于探地雷达数据解释和地雷目标的识别定位。

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王成浩
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关键词 探地雷达探雷马氏距离目标识别    
Abstract

Mine detection by ground penetrating radar is an important application direction, and its detection effect on non-metallic mines or mines with low metal content is remarkable. In this paper, aimed at tackling the problem that the target feature extraction is difficult when the ground penetrating radar detects the mine, the authors propose the SVM recognition algorithm based on the Mahalanobis distance template feature and give the recognition result. This method can effectively extract the target characteristics of mines, and is helpful to data interpretation of ground penetrating radar and recognition and location of mine targets.

Key wordsground penetrating radar (GPR)    landmine detection    mahalanobis distance    target recognition
收稿日期: 2018-10-11      出版日期: 2019-08-15
:  P631  
基金资助:近地面探测技术重点实验室基金开放课题(6142414060112)
作者简介: 王成浩(1989-), 男,山东昌邑人,工程师,硕士研究生,主要研究方向为探地雷达信号处理。
引用本文:   
王成浩, 程丹丹. 基于马氏距离模板特征的地雷目标识别研究[J]. 物探与化探, 2019, 43(4): 899-903.
Cheng-Hao WANG, Dan-Dan CHENG. A study of landmine target recognition based on Mahalanobis distance template feature. Geophysical and Geochemical Exploration, 2019, 43(4): 899-903.
链接本文:  
https://www.wutanyuhuatan.com/CN/10.11720/wtyht.2019.1363      或      https://www.wutanyuhuatan.com/CN/Y2019/V43/I4/899
Fig.1  雷达二维剖面
Fig.2  算法流程
Fig.3  目标与背景图谱
Fig.4  马氏距离模板特征向量
Fig.5  欧式距离模板特征向量
Fig.6  正样本
Fig.7  负样本
Fig.8  SVM原理
Fig.9  识别结果
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