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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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