scholarly journals A New Joint Denoising Algorithm for High-G Calibration of MEMS Accelerometer Based on VMD-PE-Wavelet Threshold

2021 ◽  
Vol 2021 ◽  
pp. 1-16 ◽  
Author(s):  
Huiliang Cao ◽  
Zekai Zhang ◽  
Yu Zheng ◽  
Hao Guo ◽  
Rui Zhao ◽  
...  
Keyword(s):  
Peak Amplitude ◽  
Measuring System ◽  
Permutation Entropy ◽  
Complex Signal ◽  
Signal Distortion ◽  
Denoising Method ◽  
Mems Accelerometer ◽  
Dynamic Part ◽  
Calibration Accuracy ◽  
The Imf

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).

scholarly journals Displacement Estimation Based on Optical and Inertial Sensor Fusion

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1390
Author(s):  
Tomasz Ursel ◽  
Michał Olinski
Keyword(s):  
Sensor Fusion ◽  
Inertial Sensor ◽  
External Disturbance ◽  
Linear Displacement ◽  
Measuring System ◽  
Displacement Estimation ◽  
Mems Accelerometer ◽  
Pedestrian Navigation ◽  
Complementary Filter ◽  
Optical Flow Sensor

This article aims to develop a system capable of estimating the displacement of a moving object with the usage of a relatively cheap and easy to apply sensors. There is a growing need for such systems, not only for robots, but also, for instance, pedestrian navigation. In this paper, the theory for this idea, including data postprocessing algorithms for a MEMS accelerometer and an optical flow sensor (OFS), as well as the developed complementary filter applied for sensor fusion, are presented. In addition, a vital part of the accelerometer’s algorithm, the zero velocity states detection, is implemented. It is based on analysis of the acceleration’s signal and further application of acceleration symmetrization, greatly improving the obtained displacement. A test stand with a linear guide and motor enabling imposing a specified linear motion is built. The results of both sensors’ testing suggest that the displacement estimated by each of them is highly correct. Fusion of the sensors’ data gives even better outcomes, especially in cases with external disturbance of OFS. The comparative evaluation of estimated linear displacements, in each case related to encoder data, confirms the algorithms’ operation correctness and proves the chosen sensors’ usefulness in the development of a linear displacement measuring system.

Design of Distance Measuring System Based on MEMS Accelerometer

2019 ◽  
Vol 14 (4) ◽  
pp. 1675-1682
Author(s):  
Lei Xu ◽  
Liqing Fang ◽  
Chunsheng Lin ◽  
Deqing Guo ◽  
Ziyuan Qi ◽  
...  
Keyword(s):  
Measuring System ◽  
Mems Accelerometer ◽  
Distance Measuring

scholarly journals Modeling and Solution of Signal Oscillation Mechanism of the Multi-Coil Sensor

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3563
Author(s):  
Jiangbo Huang ◽  
Haowen Wang ◽  
Zhihong Fu ◽  
Wei Fu
Keyword(s):  
Equivalent Circuit ◽  
Signal Transmission ◽  
Equivalent Circuit Model ◽  
Complex Signal ◽  
Signal Distortion ◽  
Transmission Characteristics ◽  
Excitation Field ◽  
Signal Conversion ◽  
Oscillation Mechanism ◽  
Conversion Performance

The multi-coil sensor consisting of a series of sub-coils provides a reliable way to avoid signal distortion from excitation field. Compared with conventional coil sensors, the multi-coil sensor exhibits more complex signal conversion performance, and the conventional equivalent circuit cannot reveal the possible attenuated oscillation, which seriously degrades the detection reliability. Based on a novel equivalent circuit model, this research investigates the causes of signal oscillation and proposes and validates an effective solution, which contributes to the signal transmission characteristics of multi-coil sensors for engineering applications.

scholarly journals A Denoising Method for Fiber Optic Gyroscope Based on Variational Mode Decomposition and Beetle Swarm Antenna Search Algorithm

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 765
Author(s):  
Pengfei Wang ◽  
Yanbin Gao ◽  
Menghao Wu ◽  
Fan Zhang ◽  
Guangchun Li ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Fiber Optic ◽  
Search Algorithm ◽  
Permutation Entropy ◽  
Navigation Systems ◽  
Variational Mode Decomposition ◽  
Fiber Optic Gyroscope ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Penalty Factor

Fiber optic gyroscope (FOG) is one of the important components of Inertial Navigation Systems (INS). In order to improve the accuracy of the INS, it is necessary to suppress the random error of the FOG signal. In this paper, a variational mode decomposition (VMD) denoising method based on beetle swarm antenna search (BSAS) algorithm is proposed to reduce the noise in FOG signal. Firstly, the BSAS algorithm is introduced in detail. Then, the permutation entropy of the band-limited intrinsic mode functions (BLIMFs) is taken as the optimization index, and two key parameters of VMD algorithm, including decomposition mode number K and quadratic penalty factor α , are optimized by using the BSAS algorithm. Next, a new method based on Hausdorff distance (HD) between the probability density function (PDF) of all BLIMFs and that of the original signal is proposed in this paper to determine the relevant modes. Finally, the selected BLIMF components are reconstructed to get the denoised signal. In addition, the simulation results show that the proposed scheme is better than the existing schemes in terms of noise reduction performance. Two experiments further demonstrate the priority of the proposed scheme in the FOG noise reduction compared with other schemes.

scholarly journals High-G Calibration Denoising Method for High-G MEMS Accelerometer Based on EMD and Wavelet Threshold

Micromachines ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 134 ◽  
Author(s):  
Qing Lu ◽  
Lixin Pang ◽  
Haoqian Huang ◽  
Chong Shen ◽  
Huiliang Cao ◽  
...  
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Original Signal ◽  
Denoising Method ◽  
Mems Accelerometer ◽  
Mode Decomposition ◽  
Impact Peak ◽  
Reconstructed Signal ◽  
Mems Accelerometers ◽  
The Impact

High-G MEMS accelerometers have been widely used in monitoring natural disasters and other fields. In order to improve the performance of High-G MEMS accelerometers, a denoising method based on the combination of empirical mode decomposition (EMD) and wavelet threshold is proposed. Firstly, EMD decomposition is performed on the output of the main accelerometer to obtain the intrinsic mode function (IMF). Then, the continuous mean square error rule is used to find energy cut-off point, and then the corresponding high frequency IMF component is denoised by wavelet threshold. Finally, the processed high-frequency IMF component is superposed with the low-frequency IMF component, and the reconstructed signal is denoised signal. Experimental results show that this method integrates the advantages of EMD and wavelet threshold and can retain useful signals to the maximum extent. The impact peak and vibration characteristics are 0.003% and 0.135% of the original signal, respectively, and it reduces the noise of the original signal by 96%.

scholarly journals A Novel Denoising Algorithm of Electromagnetic Ultrasonic Detection Signal Based on Improved EEMD Method

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Wenkang Gong ◽  
Qi Liu ◽  
Wenhao Du ◽  
Weichen Xu ◽  
Gang Wang
Keyword(s):  
White Noise ◽  
Permutation Entropy ◽  
Denoising Method ◽  
Noise Component ◽  
Ultrasonic Signals ◽  
Crack Location ◽  
Residual Difference ◽  
Eemd Method ◽  
Wavelet Transform Modulus Maximum ◽  
Improved Eemd

In this paper, we propose a new denoising algorithm for electromagnetic ultrasonic signals based on the improved EEMD method, which can adaptively adjust for added noise and average times in different noisy environments, so that the effect of the residual difference of white noise on the results can be eliminated as far as possible. First, the way to add white noise in the EEMD method is processed, and then the permutation entropy algorithm is used to identify the nature of the components obtained during the decomposition. Then the wavelet transform modulus maximum denoising method is used to deal with the IMF components of the high-frequency part obtained before. Finally, the processed IMF results and residual difference are summed up. The results show that after processing, the noise component in the signal is less and the original information is more reserved, which prevents the signal distortion to a great extent and provides more effective data for subsequent processing. In the experiment, the crack defect data collected by the electromagnetic ultrasonic experiment system were processed by the improved EEMD method. Compared with the traditional EEMD method, it can retain the information of crack location more accurately, which proves the effectiveness of the proposed method.

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

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 113901-113915 ◽  
Author(s):  
Zeyu Yan ◽  
Boyang Hou ◽  
Jingchun Zhang ◽  
Chong Shen ◽  
Yunbo Shi ◽  
...  
Keyword(s):  
Hopkinson Bar ◽  
Denoising Method ◽  
Mems Accelerometer

scholarly journals EVALUATION OF SENSOR SIGNAL PROCESSING METHODS IN TERMS OF INFORMATION THEORY

2018 ◽  
Vol 58 (6) ◽  
pp. 339-345 ◽  
Author(s):  
Patrik Flegner ◽  
Ján Kačur
Keyword(s):  
Signal Processing ◽  
Information Content ◽  
Digital Systems ◽  
Measuring System ◽  
Complex Signal ◽  
Signal Evaluation ◽  
Sensor Signals ◽  
Analog Systems ◽  
The Time Domain ◽  
Signal Processing Methods

The paper deals with the examination of basic methods of evaluation of sensor signals in terms of the information content of the given method and the used technical means. In this respect, methods based on classical analog systems, digital systems in the time domain of signal processing, hybrid systems and digital systems evaluating signal in the frequency domain are compared. A significant increase in entropy in individual systems is demonstrated in the case of a more complex signal evaluation. For each measuring system, the experimental setups, results, and discussions are described in the paper. The issue described in the article is particularly topical in connection with the development of modern technologies used in the processes and subsequent use of information. The main purpose of the article is to show that the information content of the signal is increased because the signal is more complexly processed.

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