Abstract
Recently, the High‐G MEMS accelerometer (HGMA) has been used in navigation, mechanical property detection, consumer electronics, and other fields widely. As the core component of a measuring system, it is very crucial to enhance the calibration accuracy of the accelerometer. In order to remove the noises in the accelerometer output signals to enhance its calibration accuracy, a combined denoising method which combines variational mode decomposition (VMD) with permutation entropy (PE) and wavelet threshold is given in this article. For the sake of overcoming the defect of signal distortion caused by the traditional denoising methods, this joint denoising method combines the good decomposition characteristics of VMD and the good denoising ability of wavelet threshold and introduces PE as a judgment criterion to achieve a good balance between denoising effect and signal fidelity. The combination of PE and VMD not only avoids the phenomenon of mode aliasing but also improves the ability to identify the noise components, which makes the wavelet threshold denoising more specific. Firstly, some intrinsic mode functions (IMFs) are obtained by using VMD to decompose the complex signal containing noise which is outputted from the accelerometer. Secondly, the IMF components can be divided into noise IMF components, mixed IMF components, and useful IMF components by PE algorithm. Thirdly, the noise IMF components can be discarded directly, and then the mixed IMF components can be denoised by wavelet threshold to obtain the noiseless IMF components; in addition, the useful IMF components need to be retained. Finally, the final denoising signal can be obtained by reconstructing the IMF components which have been denoised by the wavelet threshold and the useful IMF components retained before denoising. The experimental results prove that the combined denoising algorithm combines the merits of VMD, PE, and wavelet threshold, and this new algorithm has a good performance in the calibration denoising of accelerometer. Compared with the serious signal distortion caused by using only EMD or wavelet threshold, this method not only has a good denoising effect (the noises in the static part are eliminated by 99.97% and the SNR of the dynamic part is raised to 18.56) but also can maintain a good signal fidelity (the error of shock peak amplitude is 3.4%, the error of vibration peak amplitude is 0.4%, and the correlation coefficient between the denoising signals and dynamic part is as high as 0.982).
Highlights
With the rapid progress of MEMS technologies, the research on inertial sensor components has been developed well. e High-G MEMS accelerometer is an outstanding representative of the inertial sensors
In order to remove noises to enhance the accuracy of the accelerometer, variational mode decomposition (VMD) is combined with the wavelet threshold; in addition, permutation entropy (PE) is introduced as the judgment criterion, forming the VMD-PE-wavelet threshold combined denoising method. e principle of this joint denoising method is as follows: firstly, a series of intrinsic mode functions (IMFs) are obtained through using VMD to decompose the High-G MEMS accelerometer (HGMA) output signals. en, these IMF components can be divided into noise IMF components, mixed IMF components, and useful IMF components by PE. e noise IMF component can be discarded directly, and the mixed IMF components can be denoised by wavelet threshold to obtain the noiseless IMF components; in addition, the useful IMF components need to be retained
The final denoising signal can be obtained by reconstructing the IMF components which have been denoised by the wavelet threshold and useful IMF components retained before denoising. e innovation of this joint denoising method lies in the introduction of permutation entropy to classify the mode functions obtained after the signal decomposition. e combination of PE and VMD can avoid mode aliasing and has good recognition ability, making the denoising more targeted
Summary
With the rapid progress of MEMS technologies, the research on inertial sensor components has been developed well. e High-G MEMS accelerometer is an outstanding representative of the inertial sensors. With the introduction of VMD, this algorithm overcomes the inherent shortcomings of EMD and LMD such as mode aliasing and endpoint effect and has many merits such as good noise robustness and solid theoretical foundation, so it has been widely used since it was proposed. Compared with the denoising methods which only use EMD or wavelet threshold, the proposed method achieves a balance between denoising effect and signal fidelity. In order to eliminate the noises in the accelerometer output signals to enhance the calibration accuracy, this paper proposes a combined denoising algorithm (VMD-PEwavelet threshold) based on VMD and wavelet threshold and introduces permutation entropy (PE) as the judgment criterion.
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