Wavelet signal denoising applied on electromagnetic traces

Safta, Mariana; Svasta, Paul; Dima, Mihai-Octavian

doi:10.1109/siitme.2017.8259934

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…The Wavelet threshold denoising is an effective denoising method based on wavelet transform. It uses the different characteristics of the effective signal and random noise after wavelet transform distinguishes the two, so as to achieve the purpose of noise reduction [11]. However, with some certain defects, the traditional hard threshold wavelet transform can cause mutations in the wavelet domain and finally resulting in local jitter after denoising.…”

Section: Introductionmentioning

confidence: 99%

MEMS Accelerometer Calibration Denoising Method for Hopkinson Bar System Based on LMD-SE-TFPF

Yan

Hou

Zhang³

et al. 2019

IEEE Access

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High-G MEMS accelerometer (HGMA) is widely used in the aerospace field and the precise control of missiles. Therefore, its calibration accuracy is critical to sensor performance and the overall control system. In order to decrease the influence of noise on the HGMA output signal, a hybrid denoising algorithm which is based on the Time-frequency peak filtering (TFPF), Local mean decomposition (LMD) and Sample entropy (SE) has been proposed in this article. For the problem that the TFPF algorithm is limited in the choice of window length, LMD and SE are used to distinguish components, which can improve the TFPF algorithm effectively. It provides a better balance between noise reduction and signal fidelity. Firstly, the noise-containing signal can be decomposed by LMD to obtain PFs. Secondly, calculate the sample entropy values of each PFs, then divide the signal into mixed component, useful component and noise component according to the similarity of sample entropy. Thirdly, the mixed component can use long-window TFPF to reduce noise, the short-window TFPF can reduce the noise for the useful component, and the noise component can be wiped off directly. Finally, the useful component and the mixed component are both reconstructed to form the final denoised signal. Experiments have showed that this method can not only remove noise (the noise of static signal is reduced by 91.76%, the signal-noise ratio of dynamic signal has increased to 17.6), but also retain the details of frequency and amplitude (the shock peak amplitude error is 0.062% and the vibration amplitude error is 0.04%). Therefore, this method can reduce the noise of the High-G MEMS accelerometer signal with maintaining the characteristics of the original signal, thereby greatly improves the performance of the accelerometer, making it widely used. INDEX TERMS High-G MEMS accelerometer (HGMA), denoising, local mean decomposition (LMD), sample entropy (SE), time-frequency peak filtering (TFPF), Hopkinson bar.

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Section: Introductionmentioning

confidence: 99%