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Chaotic Signal Denoising Based on Adaptive Smoothing Multiscale Morphological Filtering
Joint Authors
Tang, Gui-Ji
Wang, Xiaolong
Yan, Xiaoli
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-02-17
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Nonlinear time series denoising is the prerequisite for extracting effective information from observation sequence.
An effective chaotic signal denoising method not only has a good signal-to-noise ratio (SNR) enhancement performance, but also can remain as a good unpredictable denoised signal.
However, the inherent characteristics of chaos, such as extreme sensitivity to initial values and broadband spectrum, pose challenges for noise reduction of polluted chaotic signals.
To address these issues, an adaptive smoothing multiscale morphological filtering (ASMMF) is proposed to reconstruct chaotic signals.
In the process of noise reduction for contaminated chaotic signals, firstly, a multiscale morphological filter is constructed adaptively according to the multiscale permutation entropy (MPE) of morphological filter residuals, and the contaminated signals are filtered.
Secondly, the weight coefficients of scale structural element are calculated by the residual sum of squares operation, and the chaotic signals are reconstructed.
Finally, the resampled filter signals are smoothed by cubic B-spline interpolation operation.
In the experiment, the Lorenz signal with white Gaussian noise, the measured sunspot, and the chaotic vibration signal are reconstructed by four comparison methods.
The test results show that the proposed ASMMF method has obvious advantages in noise suppression and topological trajectory restoration.
American Psychological Association (APA)
Tang, Gui-Ji& Yan, Xiaoli& Wang, Xiaolong. 2020. Chaotic Signal Denoising Based on Adaptive Smoothing Multiscale Morphological Filtering. Complexity،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1143658
Modern Language Association (MLA)
Tang, Gui-Ji…[et al.]. Chaotic Signal Denoising Based on Adaptive Smoothing Multiscale Morphological Filtering. Complexity No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1143658
American Medical Association (AMA)
Tang, Gui-Ji& Yan, Xiaoli& Wang, Xiaolong. Chaotic Signal Denoising Based on Adaptive Smoothing Multiscale Morphological Filtering. Complexity. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1143658
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1143658