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Artificial Intelligence for Micro Inertial Sensors

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

Deadline for manuscript submissions: 31 May 2026 | Viewed by 10219

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Prof. Dr. Xudong Zou

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State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
Interests: MEMS inertial sensors; MEMS resonators; PNT system; sensor fusion; 3D microsystem integration
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Prof. Dr. Chong Li

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Guest Editor

Micro System and Precision Control Laboratory, Ocean University of China, Shandong 266100, China
Interests: MEMS inertial sensors; gyroscopes; precision control systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advanced micro inertial sensors are embracing the impact of the blossoming of artificial intelligence (AI) technology. AI has proved its great potential to improve the design efficiency, fabrication quality, and measurement accuracy of MEMS inertial sensors. This special issue invites original research papers exploring AI for micro inertial sensors. Topics include but are not limited to machine learning, deep learning, edge computing for micro inertial sensor design optimization/acceleration, micro-fabrication virtual metrology, device self-testing, calibration techniques, and pose/position estimation.

Prof. Dr. Xudong Zou
Prof. Dr. Chong Li
Guest Editors

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Prof. Dr. Xudong Zou
Prof. Dr. Chong Li
Guest Editors

Manuscript Submission Information

Keywords

MEMS sensors
inertial sensors
machine learning
artificial intelligence

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Published Papers (6 papers)

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Research

20 pages, 4462 KB

Open AccessArticle

A Robust Adaptive Filtering Framework for Smartphone GNSS/PDR-Integrated Positioning

by Jijun Geng, Chao Liu, Chao Song, Chao Chen, Yang Xu, Qianxia Li, Peng Jiang and Congcong Wu

Micromachines 2026, 17(3), 353; https://doi.org/10.3390/mi17030353 - 13 Mar 2026

Viewed by 334

Abstract

Accurate and continuous outdoor pedestrian positioning using smartphones remains challenging in complex environments like urban canyons, where Global Navigation Satellite System (GNSS) signals are frequently degraded or blocked, and Pedestrian Dead Reckoning (PDR) suffers from cumulative errors. To address this, this paper proposes a novel fusion method based on a Robust Adaptive Cubature Kalman Filter (RACKF). The core of our approach is a two-stage filtering architecture: the first stage employs a quaternion-based RACKF to optimally fuse gyroscope and magnetometer data for robust heading estimation; the second stage performs the core fusion of GNSS observations with an enhanced 3D PDR solution. Key innovations include an adaptive noise estimation strategy combining fading and limited memory weighting, a robust M-estimator-based mechanism to suppress outliers, and the integration of differential barometric height measurements. Experimental results demonstrate that the proposed method achieves a horizontal positioning accuracy of 3.28 m (RMSE), outperforming standalone GNSS and improving 3D PDR by 25.97% and 10.39%, respectively. This work provides a practical, infrastructure-free solution for robust smartphone-based outdoor navigation. Full article

(This article belongs to the Special Issue Artificial Intelligence for Micro Inertial Sensors)

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18 pages, 6107 KB

Open AccessArticle

Design, Modeling, and Fabrication of a High-Q AlN Annular Gyroscope with Sub-10°/h Bias Instability

by Zhenxiang Qi, Jie Gu, Bingchen Zhu, Zhaoyang Zhai, Xiaorui Bie, Wuhao Yang and Xudong Zou

Micromachines 2026, 17(2), 268; https://doi.org/10.3390/mi17020268 - 20 Feb 2026

Viewed by 1571

Abstract

This work presents a high-performance piezoelectric MEMS yaw gyroscope fabricated on a single-crystal silicon platform, which achieves a quality factor of 75 k—the highest reported to date among silicon-based piezoelectric gyroscopes. The device employs a wide annular resonator that operates at 132 kHz in the in-plane wineglass mode. To maximize transduction efficiency, we develop an analytical model that relates output charge to the area-integrated in-plane stress under modal deformation, and we use this model to guide parametric optimization of the annular width. The resulting geometry simultaneously enhances the mechanical quality factor and the piezoelectric coupling. A back-etching fabrication process is used to eliminate front-side release holes, thereby preserving structural continuity and suppressing thermoelastic damping. In open-loop rate mode operation with a native frequency split of 28 Hz, the gyroscope demonstrates an angle random walk of 0.34°/√h and a bias instability of 8.19°/h. These performance metrics are comparable to those of state-of-the-art lead zirconate titanate (PZT)-based annular gyroscopes, while the use of lead-free aluminum nitride as the transduction material ensures compliance with RoHS environmental regulations. Full article

(This article belongs to the Special Issue Artificial Intelligence for Micro Inertial Sensors)

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20 pages, 5655 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Structure Design Optimization of a Differential Capacitive MEMS Accelerometer Based on a Multi-Objective Elitist Genetic Algorithm

by Dongda Yang, Yao Chu, Ruitao Liu, Xiwen Zhang, Saifei Yuan, Fan Zhang, Shengjie Xuan, Yunzhang Chi, Jiahui Liu, Zetong Lei and Rui You

Micromachines 2026, 17(1), 129; https://doi.org/10.3390/mi17010129 - 19 Jan 2026

Cited by 1 | Viewed by 1346

Abstract

This article describes a global structure optimization methodology for microelectromechanical system devices based on a multi-objective elitist genetic algorithm. By integrating a parameterized model with a multi-objective evolutionary framework, the approach can efficiently explore design space and concurrently optimize multiple metrics. A differential capacitive MEMS accelerometer is presented to demonstrate the method. Four key objectives, including resonant frequency, static capacitance, dynamic capacitance, and feedback force, are simultaneously optimized to enhance sensitivity, bandwidth, and closed-loop driving capability. After 25 generations, the algorithm converged to a uniformly distributed Pareto front. The experimental results indicate that, compared with the initial design, the sensitivity-oriented design achieves a 56.1% reduction in static capacitance and an 85.5% improvement in sensitivity. The global multi-objective optimization achieves a normalized hypervolume of 35.8%, notably higher than the local structure optimization, demonstrating its superior design space coverage and trade-off capability. Compared to single-objective optimization, the multi-objective approach offers a superior strategy by avoiding the limitation of overemphasizing resonant frequency at the expense of other metrics, thereby enabling a comprehensive exploration of the design space. Full article

(This article belongs to the Special Issue Artificial Intelligence for Micro Inertial Sensors)

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25 pages, 8468 KB

Open AccessArticle

An Autonomous Localization Vest System Based on Advanced Adaptive PDR with Binocular Vision Assistance

by Tianqi Tian, Yanzhu Hu, Xinghao Zhao, Hui Zhao, Yingjian Wang and Zhen Liang

Micromachines 2025, 16(8), 890; https://doi.org/10.3390/mi16080890 - 30 Jul 2025

Viewed by 963

Abstract

Despite significant advancements in indoor navigation technology over recent decades, it still faces challenges due to excessive dependency on external infrastructure and unreliable positioning in complex environments. This paper proposes an autonomous localization system that integrates advanced adaptive pedestrian dead reckoning (APDR) and binocular vision, designed to provide a low-cost, high-reliability, and high-precision solution for rescuers. By analyzing the characteristics of measurement data from various body parts, the chest is identified as the optimal placement for sensors. A chest-mounted advanced APDR method based on dynamic step segmentation detection and adaptive step length estimation has been developed. Furthermore, step length features are innovatively integrated into the visual tracking algorithm to constrain errors. Visual data is fused with dead reckoning data through an extended Kalman filter (EKF), which notably enhances the reliability and accuracy of the positioning system. A wearable autonomous localization vest system was designed and tested in indoor corridors, underground parking lots, and tunnel environments. Results show that the system decreases the average positioning error by 45.14% and endpoint error by 38.6% when compared to visual–inertial odometry (VIO). This low-cost, wearable solution effectively meets the autonomous positioning needs of rescuers in disaster scenarios. Full article

(This article belongs to the Special Issue Artificial Intelligence for Micro Inertial Sensors)

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16 pages, 3304 KB

Open AccessArticle

Full-System Simulation and Analysis of a Four-Mass Vibratory MEMS Gyroscope

by Chenguang Ouyang, Wenzheng He, Lu Jia, Peng Wang, Kaichun Zhao, Fei Xing and Zheng You

Micromachines 2025, 16(4), 414; https://doi.org/10.3390/mi16040414 - 30 Mar 2025

Cited by 1 | Viewed by 3776

Abstract

This study presents a full-system simulation methodology for MEMS, addressing the critical need for reliable performance prediction in microsystem design. While existing digital tools have been widely adopted in related fields, current approaches often remain fragmented and focused on specific aspects of device behavior. In contrast, our proposed framework conducts comprehensive device physics-level analysis by integrating mechanical, thermal and electrical modeling with process simulation. The methodology features a streamlined workflow that enables direct implementation of simulation results into fabrication processes. We model a MEMS gyroscope as an example to verify our simulation approach. Multiphysics coupling is considered to capture real-world device behavior, followed by quantitative assessment of manufacturing variations through virtual prototyping and experimental validation demonstrating the method’s accuracy and practicality. The proposed approach not only improves design efficiency but also provides a robust framework for MEMS gyroscope development. With its ability to predict device performance, this methodology is expected to become an essential tool in microsensor research and development. Full article

(This article belongs to the Special Issue Artificial Intelligence for Micro Inertial Sensors)

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22 pages, 1604 KB

Open AccessArticle

Maximum Correntropy Criterion Kalman/Allan Variance-Assisted FIR Integrated Filter for Indoor Localization

by Manman Li, Lei Deng, Yide Zhang, Yuan Xu and Yanli Gao

Micromachines 2025, 16(3), 303; https://doi.org/10.3390/mi16030303 - 4 Mar 2025

Cited by 1 | Viewed by 1038

Abstract

To obtain more accurate information on using an inertial navigation system (INS)-based integrated localization system, an integrated filter with maximum correntropy criterion Kalman filter (mccKF) and finite impulse response (FIR) is proposed for the fusion of INS-based multisource sensor data in this work. In the realm of medical applications, precise localization is crucial for various aspects, such as tracking the movement of a medical instrument within the human body or monitoring its position in the human body during procedures. This study uses ultra-wideband (UWB) technology to rectify the position errors of the INS. In this method, the difference between the positions of the INS and UWB is used as the measurement of the filter. The main data fusion filter in this study is the mccKF, which utilizes the maximum correntropy criterion (mcc) method to enhance the robustness of the Kalman filter (KF). This filter is used for fusing data from multiple sources, including the INS. Moreover, we use the Mahalanobis distance to verify the performance of the mccKF. If the performance of the mccKF is lower than the preset threshold, the Allan Variance-assisted FIR filter is used to replace the mccKF, which is designed in this work. This adaptive approach ensures the resilience of the system in demanding medical environments. Two practical experiments were performed to evaluate the effectiveness of the proposed approach. The findings indicate that the mccKF/FIR integrated method reduces the localization error by approximately 32.43% and 37.5% compared with the KF and mccKF, respectively. These results highlight the effectiveness of the proposed approach. Full article

(This article belongs to the Special Issue Artificial Intelligence for Micro Inertial Sensors)

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