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Proceedings Paper

A complete ensemble empirical mode decomposition for GPR signal time-frequency analysis
Author(s): Jing Li; Lingna Chen; Shugao Xia; Penglong Xu; Fengshan Liu
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Paper Abstract

In this paper, we apply a time and frequency analysis method based on the complete ensemble empirical mode decomposition (CEEMD) in 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 EMD applied to several realizations of Gaussian white noise added to the original signal. It can solve the mode mixing problem in empirical mode decomposition (EMD) method and improve the resolution of ensemble empirical mode decomposition (EEMD) when the signal has low signal noise ratio (SNR). First, we analyze the difference between the basic theory of EMD, EEMD and CEEMD. Then, we compare the time and frequency analysis results of different methods. The synthetic and real GPR data demonstrate that CEEMD promises higher spectral-spatial resolution than the other two EMDs method. Its decomposition is complete, with a numerically negligible error.

Paper Details

Date Published: 29 May 2014
PDF: 8 pages
Proc. SPIE 9077, Radar Sensor Technology XVIII, 90770C (29 May 2014); doi: 10.1117/12.2050432
Show Author Affiliations
Jing Li, Delaware State Univ. (United States)
Jilin Univ. (China)
Lingna Chen, Jilin Univ. (China)
Shugao Xia, Delaware State Univ. (United States)
Penglong Xu, Delaware State Univ. (United States)
Fengshan Liu, Delaware State Univ. (United States)

Published in SPIE Proceedings Vol. 9077:
Radar Sensor Technology XVIII
Kenneth I. Ranney; Armin Doerry, Editor(s)

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