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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]
Copyright of Measurement (02632241) is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
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  Data: Multi-scale eigenvalues Empirical Mode Decomposition for geomagnetic signal filtering.
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  Data: <searchLink fieldCode="AR" term="%22Qiao%2C+Nan%22">Qiao, Nan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Li-hui%22">Wang, Li-hui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wlhseu@163.com</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Qing-ya%22">Liu, Qing-ya</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhai%2C+Hong-qi%22">Zhai, Hong-qi</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Measurement+%2802632241%29%22">Measurement (02632241)</searchLink>. Nov2019, Vol. 146, p885-891. 7p.
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  Data: <searchLink fieldCode="DE" term="%22Signal+filtering%22">Signal filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink><br /><searchLink fieldCode="DE" term="%22Hilbert-Huang+transform%22">Hilbert-Huang transform</searchLink><br /><searchLink fieldCode="DE" term="%22Eigenvalues%22">Eigenvalues</searchLink><br /><searchLink fieldCode="DE" term="%22Filters+%26+filtration%22">Filters & filtration</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: • 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]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Measurement (02632241) is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.measurement.2019.07.012
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      – Code: eng
        Text: English
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        PageCount: 7
        StartPage: 885
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      – SubjectFull: Signal filtering
        Type: general
      – SubjectFull: Standard deviations
        Type: general
      – SubjectFull: Hilbert-Huang transform
        Type: general
      – SubjectFull: Eigenvalues
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      – SubjectFull: Filters & filtration
        Type: general
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      – TitleFull: Multi-scale eigenvalues Empirical Mode Decomposition for geomagnetic signal filtering.
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            NameFull: Wang, Li-hui
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            NameFull: Liu, Qing-ya
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            NameFull: Zhai, Hong-qi
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            – D: 01
              M: 11
              Text: Nov2019
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              Y: 2019
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              Value: 146
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