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Multi-scale eigenvalues Empirical Mode Decomposition for geomagnetic signal filtering.

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Title: Multi-scale eigenvalues Empirical Mode Decomposition for geomagnetic signal filtering.
Authors: Qiao, Nan1 (AUTHOR), Wang, Li-hui1 (AUTHOR) wlhseu@163.com, Liu, Qing-ya1 (AUTHOR), Zhai, Hong-qi1 (AUTHOR)
Source: Measurement (02632241). Nov2019, Vol. 146, p885-891. 7p.
Subjects: Signal filtering, Standard deviations, Hilbert-Huang transform, Eigenvalues, Filters & filtration
Abstract: • Empirical Mode Decomposition has trouble in finding dividing point and mode mixing. • Multi-scale eigenvalues is useful to restrain the mode mixing in filtering. • Autocorrelation ratio can help find the dividing point. Geomagnetic signals are susceptible to random magnetic signals and short-term, high-amplitude magnetic signals. These interferences can bring nonlinear error and degrade the navigation accuracy. Traditional Empirical Mode Decomposition (EMD) can reduce the nonlinear error of geomagnetic signal. However, with the mode mixing and the poor stability of finding dividing point by using energy criterion, traditional EMD filter is limited. In this paper, multi-scale eigenvalues EMD (ME-EMD) is proposed. To solve the problem of mode mixing, multi-scale eigenvalues are analyzed to extract the interference signal. To find the precise dividing point, autocorrelation ratio is defined. ME-EMD estimates the SNR of intrinsic mode function (IMF) and finds the dividing point. Experiments demonstrate that ME-EMD can restrain the mode mixing and find the optimal dividing point, and the filter effect of ME-EMD is better than EMD with morphology, and Modified Ensemble EMD, etc., when the geomagnetic signal is interfered by transient signal. ME-EMD reduced the Root Mean Square Error from 23.3041 µT to 1.2689 µT. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:• Empirical Mode Decomposition has trouble in finding dividing point and mode mixing. • Multi-scale eigenvalues is useful to restrain the mode mixing in filtering. • Autocorrelation ratio can help find the dividing point. Geomagnetic signals are susceptible to random magnetic signals and short-term, high-amplitude magnetic signals. These interferences can bring nonlinear error and degrade the navigation accuracy. Traditional Empirical Mode Decomposition (EMD) can reduce the nonlinear error of geomagnetic signal. However, with the mode mixing and the poor stability of finding dividing point by using energy criterion, traditional EMD filter is limited. In this paper, multi-scale eigenvalues EMD (ME-EMD) is proposed. To solve the problem of mode mixing, multi-scale eigenvalues are analyzed to extract the interference signal. To find the precise dividing point, autocorrelation ratio is defined. ME-EMD estimates the SNR of intrinsic mode function (IMF) and finds the dividing point. Experiments demonstrate that ME-EMD can restrain the mode mixing and find the optimal dividing point, and the filter effect of ME-EMD is better than EMD with morphology, and Modified Ensemble EMD, etc., when the geomagnetic signal is interfered by transient signal. ME-EMD reduced the Root Mean Square Error from 23.3041 µT to 1.2689 µT. [ABSTRACT FROM AUTHOR]
ISSN:02632241
DOI:10.1016/j.measurement.2019.07.012