GPR Signal Denoising and Target Extraction With the CEEMD Method

Jing Li, Cai Liu, Zhaofa Zeng, Lingna Chen

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

In this letter, we apply a time and frequency analysis method based on the complete ensemble empirical mode decomposition (CEEMD) method in ground-penetrating radar (GPR) signal processing. It decomposes the GPR signal into a sum of oscillatory components, with guaranteed positive and smoothly varying instantaneous frequencies. The key idea of this method relies on averaging the modes obtained by empirical mode decomposition (EMD) applied to several realizations of Gaussian white noise added to the original signal. It can solve the mode-mixing problem in the EMD method and improve the resolution of ensemble EMD (EEMD) when the signal has a low signal-to-noise ratio. First, we analyze the difference between the basic theory of EMD, EEMD, and CEEMD. Then, we compare the time and frequency analysis with Hilbert-Huang transform to test the results of different methods. The synthetic and real GPR data demonstrate that CEEMD promises higher spectral-spatial resolution than the other two EMD methods in GPR signal denoising and target extraction. Its decomposition is complete, with a numerically negligible error.
Original languageEnglish (US)
Pages (from-to)1615-1619
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume12
Issue number8
DOIs
StatePublished - Apr 17 2015

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