A complete ensemble empirical mode decomposition for GPR signal time-frequency analysis

Jing Li; Lingna Chen; Shugao Xia; Penglong Xu; Fengshan Liu

doi:10.1117/12.2050432

29 May 2014 A complete ensemble empirical mode decomposition for GPR signal time-frequency analysis

Jing Li, Lingna Chen, Shugao Xia, Penglong Xu, Fengshan Liu

Proceedings Volume 9077, Radar Sensor Technology XVIII; 90770C (2014) https://doi.org/10.1117/12.2050432
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

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.

Citation Download Citation

Jing Li, Lingna Chen, Shugao Xia, Penglong Xu, and Fengshan Liu "A complete ensemble empirical mode decomposition for GPR signal time-frequency analysis", Proc. SPIE 9077, Radar Sensor Technology XVIII, 90770C (29 May 2014); https://doi.org/10.1117/12.2050432

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