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| Citation: | Lei Wentai, Liang Qiong, Tan Qianying. A New Ground Penetrating Radar Signal Denoising Algorithm Based on Automatic Reversed-phase Correction and Kurtosis Value Comparison[J]. Journal of Radars, 2018, 7(3): 294-302. doi: 10.12000/JR17113
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A New Ground Penetrating Radar Signal Denoising Algorithm Based on Automatic Reversed-phase Correction and Kurtosis Value Comparison
doi: 10.12000/JR17113
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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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References
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- Figure 1. The flow diagram of ICA
- Figure 2. The flow diagram of the algorithm
- Figure 3. The forward model diagram of GPR
- Figure 4. The GPR no-noise echo signal by forward modeling
- Figure 5. The GPR noise signal and the generated equal length random noise signal obtained by adding noise
- Figure 6. Two channel signals obtained after random fitting
- Figure 7. Two channel signals separated by ICA algorithm
- Figure 8. The IMF component waveform diagram after CEEMD decomposition
- Figure 9. The signal of cumulative reconfiguration after the IMF components classified by kurtosis threshold value
- Figure 10. The contrast diagram of the variation curve of denoising error and signa-to-noise ratio of the algorithm and the conventional algorithm
- Figure 11. The original GPR B-Scan
- Figure 12. The contrast diagram of the original B-Scan1 with noise and the denoising result of B-Scan2 in the present algorithm
- Figure 13. The contrast diagram of the original A-Scan with noise and the denoising result of A-Scan in the present algorithm