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

Lu, Qijing; Pang, Lixin; Huang, Haoqian; Shen, Chong; Cao, Huiliang; Shi, Yunbo; Li, Jun

doi:10.3390/mi10020134

Cited by 30 publications

(26 citation statements)

References 21 publications

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“…In traditional VMD noise reduction method, the demarcation point is determined by calculating the continuous mean square error (CMSE) of each BLIMFs. Where the BLIMF with the first minimum value of CMSE is the demarcation point

[ 40 ]. The calculation formula of CMSE and the definition of demarcation point are shown in Equations ( 23 ) and ( 24 ).…”

Section: Methodsmentioning

confidence: 99%

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

Wang¹,

Gao²,

Wu³

et al. 2020

Entropy

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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.

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[ 40 ]. The calculation formula of CMSE and the definition of demarcation point are shown in Equations ( 23 ) and ( 24 ).…”

Section: Methodsmentioning

confidence: 99%

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

Wang¹,

Gao²,

Wu³

et al. 2020

Entropy

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“…The wafer is turned over and the metal Al is deposited by magnetron sputtering on the front side to form metal wires, as shown in Figure 6 (10). After that, the structure is released by ICP etching, as shown in Figure 6 (11). Finally, the back side of silicon substrate is bonded with the glass substrate by anode bonding technology.…”

Section: Process Of the Hgmamentioning

confidence: 99%

Calibration Analysis of High-G MEMS Accelerometer Sensor Based on Wavelet and Wavelet Packet Denoising

Shi

Zhang

Jiao

et al. 2021

Sensors

Self Cite

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High-G accelerometers are mainly used for motion measurement in some special fields, such as projectile penetration and aerospace equipment. This paper mainly explores the wavelet threshold denoising and wavelet packet threshold denoising in wavelet analysis, which is more suitable for high-G piezoresistive accelerometers. In this paper, adaptive decomposition and Shannon entropy criterion are used to find the optimal decomposition layer and optimal tree. Both methods use the Stein unbiased likelihood estimation method for soft threshold denoising. Through numerical simulation and Machete hammer test, the wavelet threshold denoising is more suitable for the dynamic calibration of a high-G accelerometer. The wavelet packet threshold denoising is more suitable for the parameter extraction of the oscillation phase.

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“…Step 2: Gravity Filtering Raw accelerometer data include gravity components, which makes it difficult to use motion sensors to reflect the change of celerity and position of a smartphone at the time. In this paper, we applied a novel gravity filtering method based on the combination of EMD (Empirical Mode Decomposition) and the wavelet threshold, which is proposed by Lu et al [53].…”

Section: Data Preprocessing Modulementioning

confidence: 99%

Sensing-HH: A Deep Hybrid Attention Model for Footwear Recognition

2020

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The human gait pattern is an emerging biometric trait for user identification of smart devices. However, one of the challenges in this biometric domain is the gait pattern change caused by footwear, especially if the users are wearing high heels (HH). Wearing HH puts extra stress and pressure on various parts of the human body and it alters the wearer’s common gait pattern, which may cause difficulties in gait recognition. In this paper, we propose the Sensing-HH, a deep hybrid attention model for recognizing the subject’s shoes, flat or different types of HH, using smartphone’s motion sensors. In this model, two streams of convolutional and bidirectional long short-term memory (LSTM) networks are designed as the backbone, which extract the hierarchical spatial and temporal representations of accelerometer and gyroscope individually. We also introduce a spatio attention mechanism into the stacked convolutional layers to scan the crucial structure of the data. This mechanism enables the hybrid neural networks to capture extra information from the signal and thus it is able to significantly improve the discriminative power of the classifier for the footwear recognition task. To evaluate Sensing-HH, we built a dataset with 35 young females, each of whom walked for 4 min wearing shoes with varied heights of the heels. We conducted extensive experiments and the results demonstrated that the Sensing-HH outperformed the baseline models on leave-one-subject-out cross-validation (LOSO-CV). The Sensing-HH achieved the best Fm score, which was 0.827 when the smartphone was attached to the waist. This outperformed all the baseline methods at least by more than 14%. Meanwhile, the F1 Score of the Ultra HH was as high as 0.91. The results suggest the proposed model has made the footwear recognition more efficient and automated. We hope the findings from this study paves the way for a more sophisticated application using data from motion sensors, as well as lead to a path to a more robust biometric system based on gait pattern.

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

Cited by 30 publications

References 21 publications

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

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

Calibration Analysis of High-G MEMS Accelerometer Sensor Based on Wavelet and Wavelet Packet Denoising

Sensing-HH: A Deep Hybrid Attention Model for Footwear Recognition

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