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Micromachines
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MEMS Inertial Sensors, 2nd Edition
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MEMS Inertial Sensors, 2nd Edition

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "A：Physics".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 3922

Special Issue Editors

Dr. Chong Li

E-Mail Website
Guest Editor
Micro System and Precision Laboratory, Ocean University of China, Shandong 266100, China
Interests: MEMS inertial sensors; gyroscopes; precision control systems
Special Issues, Collections and Topics in MDPI journals
Dr. Xudong Zou

E-Mail Website
Guest Editor
The State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100010, China
Interests: MEMS inertial sensors; MEMS resonators; PNT system; sensor fusion; 3D microsystem integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

MEMS technology is revolutionary to inertial measurement because of its unique advantages, i.e., miniaturized size, low power consumption, high dynamic range, and low cost. It is particularly suitable for navigation and control systems in robotics, autonomous cars, personal indoor scenarios, and other military applications. Nevertheless, MEMS inertial sensors still suffers scientific barriers towards high-end applications. Major challenges include but are not limited to microfabrication processes, new materials, device design and optimization, simulation techniques, interface circuits, measurement instrumentation, signal processing, and sensor fusion. This Special Issue calls for original research papers and reviews with state-of-the-art results in the relevant topics.

Dr. Chong Li
Dr. Xudong Zou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Micromachines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • MEMS sensor
  • inertial sensor
  • accelerometer
  • gyroscopes
  • inertial navigation
  • sensor fusion

Related Special Issue

Published Papers (3 papers)

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Research

13 pages, 4862 KiB  
Article
A Scale Factor Calibration Method for MEMS Resonant Accelerometers Based on Virtual Accelerations
by Zhaoyang Zhai, Xingyin Xiong, Liangbo Ma, Zheng Wang, Kunfeng Wang, Bowen Wang, Mingjiang Zhang and Xudong Zou
Micromachines 2023, 14(7), 1408; https://doi.org/10.3390/mi14071408 - 12 Jul 2023
Cited by 1 | Viewed by 1287
Abstract
This paper presents a scale factor calibration method based on virtual accelerations generated by electrostatic force. This method uses a series of voltage signals to simulate the inertial forces caused by the acceleration input, rather than frequent and laborious calibrations with high-precision instruments. [...] Read more.
This paper presents a scale factor calibration method based on virtual accelerations generated by electrostatic force. This method uses a series of voltage signals to simulate the inertial forces caused by the acceleration input, rather than frequent and laborious calibrations with high-precision instruments. The error transfer model of this method is systematically analyzed, and the geometrical parameters of this novel micromachined resonant accelerometer (MRA) are optimized. The experimental results demonstrate that, referring to the traditional earth’s gravitational field tumble calibration method, the error of the scale factor calibration is 0.46% within ±1 g by using our method. Moreover, the scale factor is compensated by virtual accelerations. After compensation, the maximum temperature drift of the scale factor decreases from 2.46 Hz/g to 1.02 Hz/g, with a temperature range from 40 °C to 80 °C. Full article
(This article belongs to the Special Issue MEMS Inertial Sensors, 2nd Edition)
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15 pages, 4085 KiB  
Article
A Fault Diagnosis Method of Four-Mass Vibration MEMS Gyroscope Based on ResNeXt-50 with Attention Mechanism and Improved EWT Algorithm
by Yikuan Gu, Yan Wang, Zhong Li, Tiantian Zhang, Yuanhao Li, Guodong Wang and Huiliang Cao
Micromachines 2023, 14(7), 1287; https://doi.org/10.3390/mi14071287 - 23 Jun 2023
Viewed by 904
Abstract
In this paper, a fault identification algorithm combining a signal processing algorithm and machine learning algorithm is proposed, using a four-mass vibration MEMS gyroscope (FMVMG) for signal acquisition work, constructing a gyroscope fault dataset, and performing the model training task based on this [...] Read more.
In this paper, a fault identification algorithm combining a signal processing algorithm and machine learning algorithm is proposed, using a four-mass vibration MEMS gyroscope (FMVMG) for signal acquisition work, constructing a gyroscope fault dataset, and performing the model training task based on this dataset. Combining the improved EWT algorithm with SEResNeXt-50 reduces the impact of white noise in the signal on the identification task and significantly improves the accuracy of fault identification. The EWT algorithm is a wavelet analysis algorithm with adaptive wavelet analysis, which can significantly reduce the impact of boundary effects, and has a good effect on decomposition of signal segments with short length, but a reconstruction method is needed to effectively separate the noise signal and effective signal, and so this paper uses multiscale permutation entropy for calculation. For the reason that the neural network has a better ability to characterize high-dimensional signals, the one-dimensional signal is reconstructed into a two-dimensional image signal and the signal features are extracted. Then, the constructed image signals are fed into the SEResNeXt-50 network, and the characterization ability of the model is further improved in the network with the addition of the Squeeze-and-Excitation module. Finally, the proposed model is applied to the FMVMG fault dataset and compared with other models. In terms of recognition accuracy, the proposed method improves about 30.25% over the BP neural network and about 1.85% over ResNeXt-50, proving the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue MEMS Inertial Sensors, 2nd Edition)
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18 pages, 4509 KiB  
Article
Accurate Stride-Length Estimation Based on LT-StrideNet for Pedestrian Dead Reckoning Using a Shank-Mounted Sensor
by Yong Li, Guopei Zeng, Luping Wang and Ke Tan
Micromachines 2023, 14(6), 1170; https://doi.org/10.3390/mi14061170 - 31 May 2023
Cited by 2 | Viewed by 1134
Abstract
Pedestrian dead reckoning (PDR) is a self-contained positioning technology and has been a significant research topic in recent years. Pedestrian-stride-length estimation is the core part of the PDR system and directly affects the performance of the PDR. The current stride-length-estimation method is difficult [...] Read more.
Pedestrian dead reckoning (PDR) is a self-contained positioning technology and has been a significant research topic in recent years. Pedestrian-stride-length estimation is the core part of the PDR system and directly affects the performance of the PDR. The current stride-length-estimation method is difficult to adapt to changes in pedestrian walking speed, which leads to a rapid increase in the error of the PDR. In this paper, a new deep-learning model based on long short-term memory (LSTM) and Transformer, LT-StrideNet, is proposed to estimate pedestrian-stride length. Next, a shank-mounted PDR framework is built based on the proposed stride-length-estimation method. In the PDR framework, the detection of pedestrian stride is achieved by peak detection with a dynamic threshold. An extended Kalman filter (EKF) model is adopted to fuse the gyroscope, accelerometer, and magnetometer. The experimental results show that the proposed stride-length-estimation method can effectively adapt to changes in pedestrian walking speed, and our PDR framework has excellent positioning performance. Full article
(This article belongs to the Special Issue MEMS Inertial Sensors, 2nd Edition)
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