Adaptive modulation interval filtering algorithm based on empirical mode decomposition

Dao, Xinyu; Gao, Min; Li, Chaowang

doi:10.1016/j.measurement.2019.04.046

Cited by 12 publications

(5 citation statements)

References 14 publications

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“…At present, thresholding is widely used in denoising algorithms that are based on EMD. To improve the filtering effect, common threshold methods such as hard threshold method based on EMD (HT‐EMD) and soft threshold method based on EMD (ST‐EMD) have been proposed [15, 16, 21].…”

Section: Interference Suppression Algorithmmentioning

confidence: 99%

“…The interference signal can be treated as an impulse‐like signal in the time domain and as a white noise signal in the frequency domain [12]. A variety of threshold denoising algorithms have been proposed for white noise interference [13–16]. Non‐parametric thresholded algorithms are mostly inspired by the idea of wavelet thresholding [17].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Iterative modified threshold method based on EMD for interference suppression in FMCW radars

Yang

Lü

et al. 2020

IET Radar, Sonar & Navigation

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Section: Interference Suppression Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Iterative modified threshold method based on EMD for interference suppression in FMCW radars

Yang

Lü

et al. 2020

IET Radar, Sonar & Navigation

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“…Cheng et al (2021) adopted the EEMD method to distinguish the intrinsic mode function (IMF) of noise and signal, and removed the IMF whose main component was noise, and then used the SVD-LWT method to remove the noise in the IMF component containing signal, so as to extract signal in a fine manner. Dao et al (2019) proposed an adaptive modulation interval threshold denoising algorithm based on empirical mode decomposition, and finally raised the signal-to-noise ratio by 1-3dB. Compared with direct empirical mode decomposition denoising method and traditional threshold denoising method, the root -mean-square error was reduced by 10-25%.…”

Section: Introductionmentioning

confidence: 99%

A denoising method of coal charge signal under load based on IWT and ICEEMDAN algorithm

Yang

et al. 2023

Preprint

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With the continuous improvement of signal noise reduction performance requirements, in view of the noise existing in the electromagnetic radiation signal acquisition process, the wavelet threshold function of traditional denoising methods has problems such as non-progressiveness and discontinuity at the threshold value. In order to achieve better signal noise reduction effect, this paper proposes a collection based on improved wavelet threshold (IWT) function and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) combined denoising algorithm. Firstly, the algorithm is used to decompose the noisy signal, and calculate the intrinsic mode function (IMF) and correlation coefficient of each order to distinguish the noise from the correlated signal. Then, the IMF component dominated by the signal is reconstructed to complete the denoising. Simulations and experiments show that the algorithm can effectively remove the noise in the charge sensing signal, and its signal-to-noise ratio (SNR) is improved by 2.4062 compared with six similar algorithms such as the improved threshold function, CEEMD algorithm, and the root mean square error (RMSE) is reduced by 0.0866, and its noise-to-noise ratio(Rnn) is reduced by 3.103 compared with four algorithms such as the improved threshold function, the improved threshold function combined with the ensemble empirical mode decomposition (EEMD), and has a good noise reduction performance, and the research results provide a new method for capturing real signals. Decomposition (EEMD) combined with denoising and other four algorithms is reduced by 3.103, which has better noise reduction performance. The research results provide a new method for collecting real signals, This indicates the feasibility of the joint denoising algorithm.

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“…Thus, it is essential to reduce noise interference [2,3]. The challenge of signal denoising is to preserve the effective components while removing the noise, especially when the signal is non-linear and non-stationary [4]. Short-time Fourier transform (STFT) has been widely used for processing signals [5].…”

Section: Introductionmentioning

confidence: 99%

A novel denoising method for non‐linear and non‐stationary signals

Wang

et al. 2022

IET Signal Processing

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Signal denoising is a crucial step in signal analysis. Various procedures have been attempted by researchers to remove the noise while preserving the effective components of the signal. One of the most successful denoising methods currently in use is the variational mode decomposition (VMD). Unfortunately, the effectiveness of VMD depends on the appropriate selection of the decomposition level and the effective modes to be reconstructed, and, like many other traditional denoising methods, it is often ineffective when the signal is non-linear and non-stationary. In view of these problems, this study proposes a new denoising method that consists of three steps. First, an improved VMD method is used to decompose the original signal into an optimal number of intrinsic mode functions (IMFs). Second, the energy variation ratio function is applied to distinguish between the effective and non-effective IMFs. Third, the valuable components are retained while the useless ones are removed, and the denoised signal is obtained by reconstructing the useful IMFs. Simulations and experiments on various noisy nonlinear and non-stationary signals demonstrated the superior performance of the proposed method over existing denoising approaches.

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Adaptive modulation interval filtering algorithm based on empirical mode decomposition

Cited by 12 publications

References 14 publications

Iterative modified threshold method based on EMD for interference suppression in FMCW radars

Iterative modified threshold method based on EMD for interference suppression in FMCW radars

A denoising method of coal charge signal under load based on IWT and ICEEMDAN algorithm

A novel denoising method for non‐linear and non‐stationary signals

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