Volume 7 Issue 3
Jul.  2018
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Article Navigation >  Journal of Radars  > 2018  >  7(3): 294-302
XIA Deping, ZHANG Liang, WU Tao, et al. A multiple interference suppression algorithm based on airborne bistatic polarization radar[J]. Journal of Radars, 2022, 11(3): 399–407. doi: 10.12000/JR21212
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
shu

A New Ground Penetrating Radar Signal Denoising Algorithm Based on Automatic Reversed-phase Correction and Kurtosis Value Comparison

DOI: 10.12000/JR17113

  • 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)
  • Received Date: 2017-11-27
  • Rev Recd Date: 2017-12-25
  • Publish Date: 2018-06-28
    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(18)
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