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A New Ground Penetrating Radar Signal Denoising Algorithm Based on Automatic Reversed-phase Correction and Kurtosis Value Comparison
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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。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.
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图 5 加入噪声后模拟得到的GPR含噪信号和产生的等长度的随机噪声信号
Figure 5. The GPR noise signal and the generated equal length random noise signal obtained by adding noise
图 8 经过CEEMD分解后的各IMF分量波形图
Figure 8. The IMF component waveform diagram after CEEMD decomposition
图 9 各IMF分量采用峰度值阈值分类后累加重构的信号
Figure 9. The signal of cumulative reconfiguration after the IMF components classified by kurtosis threshold value
图 10 本算法和常规算法的去噪误差和信噪比的变化曲线对比图
Figure 10. The contrast diagram of the variation curve of denoising error and signa-to-noise ratio of the algorithm and the conventional algorithm
图 12 未去噪原始B-scan1和本算法去噪结果B-scan2对比图
Figure 12. The contrast diagram of the original B-Scan1 with noise and the denoising result of B-Scan2 in the present algorithm
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[1] Harry M J, 雷文太, 童孝忠, 周旸, 等译. 探地雷达理论与应用[M]. 北京: 电子工业出版社, 2011: 2–24.Harry M J. Lei Wen-tai, Tong Xiao-zhong, Zhou Yang, et al.. Ground Penetrating Radar: Theory and Application[M]. Beijing: Publishing House Of Electronics Industry, 2011: 2–24. [2] 冯晅, 曾昭发, 刘四新, 等. 探地雷达信号处理[M]. 北京: 科学出版社, 2014: 3–15.Feng Xuan, Zeng Zhao-fa, Liu Si-xin, et al.. Ground Penetrating Radar Signal Processing[M]. Beijing: Science Press, 2014: 3–15. [3] 杨峰, 彭苏萍. 地质雷达探测原理与方法研究[M]. 北京: 科学出版社, 2010: 5–30.Yang Feng and Peng Su-ping. Study on the Principle and Method of GPR Detection[M]. Beijing: Science Press, 2010: 5–30. [4] 石国杰, 王晋国, 侯兆阳, 等. 地质雷达数据处理新方法研究[J]. 洛阳理工学院学报(自然科学版), 2017, 27(1): 42–44. DOI: 10.3969/i.issn.1674-5403.2017.01.012Shi Guo-jie, Wang Jin-guo, Hou Zhao-yang, et al. Study on new methods of ground penetrating radar data analysis[J]. Journal of Luoyang Institute of Science and Technology(Natural Science Edition) , 2017, 27(1): 42–44. DOI: 10.3969/i.issn.1674-5403.2017.01.012 [5] 王超, 沈斐敏. 小波变换在探地雷达弱信号去噪中的研究[J]. 物探与化探, 2015, 39(2): 421–424. DOI: 10.11720/wtyht.2015.2.35Wang Chao and Shen Fei-min. Study of wavelet transform in ground penetration radar weak signal denoising[J]. Geophysical and Geochemical Exploration, 2015, 39(2): 421–424. DOI: 10.11720/wtyht.2015.2.35 [6] 许军才, 刘立桥, 任青文, 等. 探地雷达信号的EEMD时域分析方法[J]. 合肥工业大学学报(自然科学版), 2015, 38(5): 639–642. DOI: 10.3969/j.issn.1003-5060.2015.05.014Xu Jun-cai, Liu Li-qiao, Ren Qing-wen, et al. EEMD analysis of GPR signal in time domain[J]. Journal of Hefei University of Technology(Natural Science Edition) , 2015, 38(5): 639–642. DOI: 10.3969/j.issn.1003-5060.2015.05.014 [7] Abujarad F and Omar A. Comparison of Independent Component Analysis (ICA) algorithms for GPR detection of non-metallic land mines[C]. Proceedings of SPIE Image and Signal Processing for Remote Sensing XII, Stockholm, Sweden, 2006: 1–12. [8] 段炜. 基于小波变换的探地雷达信号去噪方法研究[D]. [硕士论文], 中南大学, 2008.Duan Wei. Research on denoising method of ground penetrating radar signal based on wavelet transform[D]. [Master dissertation], Central South University, 2008. [9] 张春城, 周正欧. 基于ICA的步进频率探地雷达目标信息提取研究[J]. 安徽理工大学学报(自然科学版), 2009, 29(2): 1–4. DOI: 10.3969/j.issn.1672-1098.2009.02.001Zhang Chun-cheng and Zhou Zheng-ou. Research on target information extraction in step frequency ground penetrating radar data based on independent component analysis[J]. Journal of Anhui University of Science and Technology(Natural Science) , 2009, 29(2): 1–4. DOI: 10.3969/j.issn.1672-1098.2009.02.001 [10] 陈玲娜. 基于CEEMD和PCA的探地雷达数据处理研究与应用[D]. [硕士论文], 吉林大学, 2016.Chen Ling-na. The study and application of GPR data processing based on CEEMD and PCA[D]. [Master dissertation], Jilin University, 2016. [11] Li J, Liu C, Zeng Z F, et al. GPR signal denoising and target extraction with the CEEMD method[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(8): 1615–1619. DOI: 10.1109/LGRS.2015.2415736 [12] 明锋, 杨元喜, 曾安敏. 共模误差PCA与ICA提取方法的比较[J]. 大地测量与地球动力学, 2017, 37(4): 385–389. DOI: 10.14075/j.jgg.2017.04.012Ming Feng, Yang Yuan-xi, and Zeng An-min. Analysis and comparison of common mode error extraction using principal component analysis and independent component analysis[J]. Journal of Geodesy and Geodynamics, 2017, 37(4): 385–389. DOI: 10.14075/j.jgg.2017.04.012 [13] Wu Z H and Huang N E. Ensemble empirical mode decomposition: A noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1–41. DOI: 10.1142/S1793536909000047 [14] Yen J R, Shieh J S, and Huang N E. Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method[J]. Advances in Adaptive Data Analysis, 2010, 2(2): 135–156. DOI: 10.1142/S1793536910000422 [15] Torres M E, Colominas M A, Schlotthauer G, et al.. A complete ensemble empirical mode decomposition with adaptive noise[C]. Proceedings of 2011 IEEE International Conference on Acoustics, Speech and Signal Processing, Prague, Czech Republic, 2011: 4144–4147. DOI: 10.1109/ICASSP.2011.5947265. [16] 王姣, 李振春, 王德营. 基于CEEMD的地震数据小波阈值去噪方法研究[J]. 石油物探, 2014, 53(2): 164–172. DOI: 10.3969/j.issn.1000-1441.2014.02.006Wang Jiao, Li Zhen-chun, and Wang De-ying. A method for wavelet threshold denoising of seismic data based on CEEMD[J]. Geophysical Prospecting for Petroleum, 2014, 53(2): 164–172. DOI: 10.3969/j.issn.1000-1441.2014.02.006 [17] Colominas M A, Schlotthauer G, and Torres M E. Improved complete ensemble EMD: A suitable tool for biomedical signal processing[J]. Biomedical Signal Processing and Control, 2014, 14: 19–29. DOI: 10.1016/j.bspc.2014.06.009 [18] Warren C, Giannopoulos A, and Giannakis I. gprMax: Open source software to simulate electromagnetic wave propagation for ground penetrating radar[J]. Computer Physics Communications, 2016, 209: 163–167. DOI: 10.1016/j.cpc.2016.08.020 -
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