梁琼,
谭倩颖
中南大学信息科学与工程学院 ? ?长沙 ? ?410075
基金项目:国家自然科学基金（61102139），中南大学研究生自主探索创新项目（2017zzts481）
详细信息
作者简介:雷文太(1979–)，男，河南南阳人，博士，副教授。2006年在国防科技大学获得博士学位，现担任中南大学信息科学与工程学院副教授，主要研究方向为探地雷达数据处理，目前已发表学术论文50余篇。E-mail: lei_wentai@163.com
梁琼：梁 琼(1993–)，女，湖南娄底人，中南大学硕士生，主要研究方向为探地雷达信号处理与特征提取
谭倩颖(1995–)，女，湖南娄底人，中南大学硕士生，主要研究方向为探地雷达信号处理
通讯作者:雷文太 ?lei_wentai@163.com
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出版历程
收稿日期:2017-11-27
修回日期:2017-12-25
A New Ground Penetrating Radar Signal Denoising Algorithm Based on Automatic Reversed-phase Correction and Kurtosis Value Comparison
Lei Wentai,,Liang Qiong,
Tan Qianying
Institute of Information Science and Engineering, Central South University, Changsha 410075, China
Funds:The National Natural Science Foundation of China (61102139), The Graduate Independent Exploration and Innovation of Central South University (2017zzts481)
摘要
摘要:运用探地雷达对复杂地下介质层进行探测时，雷达回波信号易受噪声影响。为了提高探地雷达的探测分辨率和数据解译效果，该文提出基于自动反相校正和峰度值比较的探地雷达回波信号去噪算法。首先，含噪的回波信号与随机噪声拟合得到两路信号，经过独立分量分析算法后得到高峰度值信号和低峰度值噪声，对高峰度值信号进行相位判断并进行自动反相校正，再进行完全总体经验模态算法分解得到多个分解分量。将独立分量分析得出的噪声的峰度值作为阈值，峰度值高于该阈值的分解分量视为信号分量，累加得到重构后的信号，完成去噪处理。所提的去噪算法解决了独立成分分析算法中的信号相位不定性问题，且在进行完全总体经验模态分解算法后无需依靠传统的人工方式进行噪声剔除的步骤。仿真和实测数据的处理结果验证了所提算法的有效性。
关键词:探地雷达/
自动反相校正/
峰度值比较/
去噪算法
Abstract:When using Ground Penetrating Radar (GPR) on the occasion of complex underground medium detection, radar echo can be easily affected by various noise. In order to improve GPR detection resolution and data interpretation quality, this paper proposed a new GPR denoising algorithm based on automatic reversed-phase correction and kurtosis value comparison. GPR echo signal and random noise with the same length were fitted and two signals can be obtained. By using Independent Component Analysis (ICA) algorithm, these two signals can be decomposed into two other signals, one with high kurtosis named S1 and one with low kurtosis named S2. S1 signal’s phase was determined and automatic phase correction was carried out. By using Complete Ensemble Empirical Mode Decomposition (CEEMD) algorithm, S1 after automatic phase correction was decomposed, several Intrinsic Mode Function (IMF) can be obtained and kurtosis value of each IMF can be calculated. S2 signal’s kurtosis value was set as a threshold. The IMFs whose kurtosis values are lower than this threshold are classified as noise components, while the other IMFs whose kurtosis values are higher than this threshold are classified as signal components. By summing the IMFs of signal components, GPR echo signal can be reconstructed and denoising. This new GPR denoising algorithm solves the problems of phase uncertainty in ICA and manual IMF components classification in CEEMD and thus improves GPR denoising effects with higher computation efficiency. The effectiveness of the proposed algorithm is verified by simulation and real data processing experiments.
Key words:Ground Penetrating Radar (GPR)/
Automatic reversed-phase correction/
Kurtosis value comparison/
Denoising algorithm
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