Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement—A Review

Research and industrial studies have indicated that small size, low cost, high precision, and ease of integration are vital features that characterize microelectromechanical systems (MEMS) inertial sensors for mass production and diverse applications. In recent times, sensors like MEMS accelerometers and MEMS gyroscopes have been sought in an increased application range such as medical devices for health care to defense and military weapons. An important limitation of MEMS inertial sensors is repeatedly documented as the ease of being influenced by environmental noise from random sources, along with mechanical and electronic artifacts in the underlying systems, and other random noise. Thus, random error processing is essential for proper elimination of artifact signals and improvement of the accuracy and reliability from such sensors. In this paper, a systematic review is carried out by investigating different random error signal processing models that have been recently developed for MEMS inertial sensor precision improvement. For this purpose, an in-depth literature search was performed on several databases viz., Web of Science, IEEE Xplore, Science Direct, and Association for Computing Machinery Digital Library. Forty-nine representative papers that focused on the processing of signals from MEMS accelerometers, MEMS gyroscopes, and MEMS inertial measuring units, published in journal or conference formats, and indexed on the databases within the last 10 years, were downloaded and carefully reviewed. From this literature overview, 30 mainstream algorithms were extracted and categorized into seven groups, which were analyzed to present the contributions, strengths, and weaknesses of the literature. Additionally, a summary of the models developed in the studies was presented, along with their working principles viz., application domain, and the conclusions made in the studies. Finally, the development trend of MEMS inertial sensor technology and its application prospects were presented.

[1]  Huiliang Cao,et al.  Temperature Drift Compensation for High-G MEMS Accelerometer Based on RBF NN Improved Method , 2019, Applied Sciences.

[2]  Zhongxu Hu,et al.  Extended Kalman filtering based parameter estimation and drift compensation for a MEMS rate integrating gyroscope , 2016 .

[3]  Li Ma,et al.  Research of on-line modeling and real-time filtering for MEMS gyroscope random noise , 2017, 2017 IEEE International Conference on Real-time Computing and Robotics (RCAR).

[4]  Benjamin Braun.  Performance Analysis of GNSS-Aided Inertial Navigation Systems on Spinning Flight Vehicles , 2019, Journal of Spacecraft and Rockets.

[5]  Niclas Roxhed,et al.  Integrating MEMS and ICs , 2015 .

[6]  Neelesh Kumar,et al.  Errors in micro-electro-mechanical systems inertial measurement and a review on present practices of error modelling , 2018, Trans. Inst. Meas. Control.

[7]  Ting Lie,et al.  Advances in Intelligent Systems and Computing , 2014 .

[8]  Liang Xue,et al.  Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal , 2015, Micromachines.

[9]  Chong Shen,et al.  Improved Morphological Filter Based on Variational Mode Decomposition for MEMS Gyroscope De-Noising , 2018, Micromachines.

[10]  Kunhong Han,et al.  Notice of RetractionA noise removal method for MEMS gyroscope , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[11]  Ahmad Ghanbari,et al.  The New Boundary Condition Effect on The Free Vibration Analysis of Micro-beams Based on The Modified Couple Stress Theory , 2015 .

[12]  A Nazemipour,et al.  MEMS Gyroscope Raw Data Noise Reduction Using Fading Memory Filter , 2018 .

[13]  Derek K. Shaeffer,et al.  MEMS inertial sensors: A tutorial overview , 2013, IEEE Communications Magazine.

[14]  Farrokh Ayazi,et al.  High-Q Timing and Inertial Measurement Unit Chip (TIMU) with 3D Wafer-Level Packaging , 2019, 2019 IEEE Custom Integrated Circuits Conference (CICC).

[15]  G. A. Ramadass,et al.  Autonomous underwater vehicles - challenging developments and technological maturity towards strategic swarm robotics systems , 2019 .

[16]  Xiaojuan Zhang,et al.  An Adaptive Compensation Algorithm for Temperature Drift of Micro-Electro-Mechanical Systems Gyroscopes Using a Strong Tracking Kalman Filter , 2015, Sensors.

[17]  Alireza Babaei,et al.  Longitudinal vibration responses of axially functionally graded optimized MEMS gyroscope using Rayleigh–Ritz method, determination of discernible patterns and chaotic regimes , 2019, SN Applied Sciences.

[18]  Jerzy Sasiadek.  Modern inertial technology: Navigation, guidance, and control: by Anthony LAWRENCE; Springer-Verlag Series on Instrumentation and Systems; Springer-Verlag; Berlin, Germany; 1993; 268 pp.; DM 118; ISBN: 3-540-97868-2 , 1995 .

[19]  Xin Xu,et al.  Sensor fusion-based attitude estimation using low-cost MEMS-IMU for mobile robot navigation , 2011, 2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference.

[20]  Guo Zhanshe,et al.  Research development of silicon MEMS gyroscopes: a review , 2015 .

[21]  Jun Liu,et al.  High-G Calibration Denoising Method for High-G MEMS Accelerometer Based on EMD and Wavelet Threshold , 2019, Micromachines.

[22]  Mir Mohammad Ettefagh,et al.  Optimal novel super-twisting PID sliding mode control of a MEMS gyroscope based on multi-objective bat algorithm , 2018 .

[23]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[24]  Juntao Fei,et al.  Adaptive Fuzzy Sliding Mode Control for a Micro Gyroscope with Backstepping Controller , 2020, Micromachines.

[25]  James A. Bucklew,et al.  Support vector machine techniques for nonlinear equalization , 2000, IEEE Trans. Signal Process..

[26]  Anthony Lawrence,et al.  Modern Inertial Technology: Navigation, Guidance, and Control , 1993 .

[27]  Xin Wang,et al.  Design and Implementation of Strong Tracking Combined Filtering Algorithm for MEMS Gyroscope , 2018, 2018 5th International Conference on Information Science and Control Engineering (ICISCE).

[28]  G. D. Hammond,et al.  Measurement of the Earth tides with a MEMS gravimeter , 2016, Nature.

[29]  Bo Hou,et al.  Modeling and Compensation of Random Drift of MEMS Gyroscopes Based on Least Squares Support Vector Machine Optimized by Chaotic Particle Swarm Optimization , 2017, Sensors.

[30]  Qiang Shen,et al.  A signal processing technique for compensating random drift of MEMS gyros , 2014, Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference.

[31]  Xinxin Li,et al.  Electromechanical Sigma–Delta Modulators ( $\Sigma \Delta {\mathrm{ M}}$ ) Force Feedback Interfaces for Capacitive MEMS Inertial Sensors: A Review , 2016, IEEE Sensors Journal.

[32]  Thomas W. Kenny,et al.  Effective quality factor tuning mechanisms in micromechanical resonators , 2018, Applied Physics Reviews.

[33]  Farid Golnaraghi,et al.  An Algorithm for the In-Field Calibration of a MEMS IMU , 2017, IEEE Sensors Journal.

[34]  Wanru Juan,et al.  Adaptive fuzzy sliding mode control using adaptive sliding gain for MEMS gyroscope , 2013 .

[35]  M. S. Noorani,et al.  Temperature-dependent free vibration analysis of functionally graded micro-beams based on the modified couple stress theory , 2017 .

[36]  Changhui Jiang,et al.  A MEMS Gyroscope Noise Suppressing Method Using Neural Architecture Search Neural Network , 2019, Mathematical Problems in Engineering.

[37]  Chong Shen,et al.  Dual-Mass MEMS Gyroscope Parallel Denoising and Temperature Compensation Processing Based on WLMP and CS-SVR , 2020, Micromachines.

[38]  Rastislav Pirník,et al.  Integration of Inertial Sensor Data into Control of the Mobile Platform , 2015 .

[39]  Ales Janota,et al.  Intelligent Real-Time MEMS Sensor Fusion and Calibration , 2016, IEEE Sensors Journal.

[40]  Samrat L. Sabat,et al.  MEMS-Based IMU Drift Minimization: Sage Husa Adaptive Robust Kalman Filtering , 2020, IEEE Sensors Journal.

[41]  Jianping Hu,et al.  A High-Performance Digital Interface Circuit for a High-Q Micro-Electromechanical System Accelerometer , 2018, Micromachines.

[42]  Alireza Babaei,et al.  Forced vibration analysis of non-local strain gradient rod subjected to harmonic excitations , 2020, Microsystem Technologies.

[43]  Pavol Bozek,et al.  Experimental investigations of a highly maneuverable mobile omniwheel robot , 2017 .

[44]  Ivan Kuric,et al.  Case study: Performance analysis and development of robotized screwing application with integrated vision sensing system for automotive industry , 2020 .

[45]  Wen Jin,et al.  The improvements of BP neural network learning algorithm , 2000, WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000.

[46]  Cheng Yu,et al.  The Development of Micromachined Gyroscope Structure and Circuitry Technology , 2014, Sensors.

[47]  Jingbiao Liu,et al.  A deep-sea drilling rig MEMS gyroscope random drift error correction method , 2013, 2013 OCEANS - San Diego.

[48]  Feilong Liu,et al.  An improved noise reduction algorithm based on wavelet transformation for MEMS gyroscope , 2015 .

[49]  Javaid Iqbal,et al.  Microfabrication Process-Driven Design, FEM Analysis and System Modeling of 3-DoF Drive Mode and 2-DoF Sense Mode Thermally Stable Non-Resonant MEMS Gyroscope , 2020, Micromachines.

[50]  Mehran Rahmani,et al.  MEMS gyroscope control using a novel compound robust control. , 2017, ISA transactions.

[51]  Yuwei Chen,et al.  A Mixed Deep Recurrent Neural Network for MEMS Gyroscope Noise Suppressing , 2019, Electronics.

[52]  Farrokh Ayazi,et al.  A high-performance single-chip timing and inertial measurement unit with robust mode-matched gyroscopes , 2018, 2018 IEEE Micro Electro Mechanical Systems (MEMS).

[53]  Milan Sága,et al.  Design of a robotic manipulator for handling products of automotive industry , 2020 .

[54]  Shi Qiang Liu,et al.  A Wearable Flow-MIMU Device for Monitoring Human Dynamic Motion , 2020, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[55]  Sukhan Lee,et al.  Micromachined inertial sensors , 1999, Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289).

[56]  Friedrich M. Wahl,et al.  A sensor fusion approach to angle and angular rate estimation , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[57]  Jaw Kuen Shiau,et al.  MEMS Gyroscope Null Drift and Compensation Based on Neural Network , 2011 .

[58]  Guowei Gao,et al.  Application of Improved Wavelet Thresholding Method and an RBF Network in the Error Compensating of an MEMS Gyroscope , 2019, Micromachines.

[59]  Yin Liang,et al.  Analysis and design of the system of a total digital Si-gyroscope , 2017 .

[60]  Yanshun Zhang,et al.  An Adaptive Filtering Approach Based on the Dynamic Variance Model for Reducing MEMS Gyroscope Random Error , 2018, Sensors.

[61]  Prabir Bhattacharya,et al.  An Enhanced MEMS Error Modeling Approach Based on Nu-Support Vector Regression , 2012, Sensors.

[62]  Mohammad Habibur Rahman,et al.  A new hybrid robust control of MEMS gyroscope , 2020, Microsystem Technologies.

[63]  Javad Poshtan,et al.  Distributed Interacting Multiple Filters for Fault Diagnosis of Navigation Sensors in a Robotic System , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[64]  Yu-Liang Hsu,et al.  Random Drift Modeling and Compensation for MEMS-Based Gyroscopes and Its Application in Handwriting Trajectory Reconstruction , 2019, IEEE Access.

[65]  George T. Flowers,et al.  The Underwater Effects of High Power, High Frequency Acoustic Noise on MEMS Gyroscopes , 2011 .

[66]  Masao Masugi,et al.  Performance analysis of α- β- γtracking filters using position and velocity measurements , 2015, EURASIP J. Adv. Signal Process..

[67]  Yanshun Zhang,et al.  Modeling and compensation of MEMS gyroscope output data based on support vector machine , 2011, 2011 Seventh International Conference on Natural Computation.

[68]  Yuwei Chen,et al.  A MEMS IMU De-Noising Method Using Long Short Term Memory Recurrent Neural Networks (LSTM-RNN) , 2018, Sensors.

[69]  Arash Rahmani,et al.  Vibration analysis of rotating thermally-stressed gyroscope, based on modified coupled displacement field method , 2020 .

[70]  Mohammad Reza Taban,et al.  A Low Complexity Integrated Navigation System for Underwater Vehicles , 2018, Journal of Navigation.

[71]  Tao Zhang,et al.  Recent Advancements in Inertial Micro-Switches , 2019 .

[72]  Hongwei Zhang,et al.  Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter , 2017, Sensors.

[73]  Vittorio M. N. Passaro,et al.  Gyroscope Technology and Applications: A Review in the Industrial Perspective , 2017, Sensors.

[74]  Yong Sheng Shi,et al.  Study on MEMS Gyro Signal De-Noising Based on Improved Wavelet Threshold Method , 2013 .

[75]  Ibrahim M. Elfadel,et al.  MEMS multi-vibrating ring gyroscope for space applications , 2020 .

[76]  Rui Zhang,et al.  Adaptive Fuzzy Sliding Mode Control of MEMS Gyroscope with Finite Time Convergence , 2016, J. Sensors.

[77]  Yu-Liang Hsu,et al.  Drift modeling and compensation for MEMS-based gyroscope using a Wiener-type recurrent neural network , 2017, 2017 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL).

[78]  Igor Nevlydov,et al.  MEMS-Based Inertial Sensor Signals and Machine Learning Methods for Classifying Robot Motion , 2018, 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP).

[79]  S. R. Munasinghe,et al.  Recursive least square based estimation of MEMS inertial sensor stochastic models , 2010, 2010 Fifth International Conference on Information and Automation for Sustainability.

[80]  James C. Kinsey,et al.  A Navigation Solution Using a MEMS IMU, Model-Based Dead-Reckoning, and One-Way-Travel-Time Acoustic Range Measurements for Autonomous Underwater Vehicles , 2019, IEEE Journal of Oceanic Engineering.

[81]  Mohammad Habibur Rahman,et al.  A new adaptive fractional sliding mode control of a MEMS gyroscope , 2018 .

[82]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[83]  Siyuan Liang,et al.  High-Efficiency Wavelet Compressive Fusion for Improving MEMS Array Performance , 2020, Sensors.

[84]  Quoc V. Le,et al.  Neural Architecture Search with Reinforcement Learning , 2016, ICLR.

[85]  Zhuangde Jiang,et al.  A low noise capacitive MEMS accelerometer with anti-spring structure , 2019, Sensors and Actuators A: Physical.

[86]  Bin Wang,et al.  Design and Fabrication of a Slanted-Beam MEMS Accelerometer , 2017, Micromachines.

[87]  George Eastman House,et al.  Sparse Bayesian Learning and the Relevance Vector Machine , 2001 .

[88]  Jean-Fu Kiang,et al.  A CMOS-MEMS single-chip dual-axis gyroscope , 2009, 2009 4th International Microsystems, Packaging, Assembly and Circuits Technology Conference.

[89]  Tayfun Akin,et al.  A high-performance silicon-on-insulator MEMS gyroscope operating at atmospheric pressure , 2007 .

[90]  Yuwei Chen,et al.  Performance Analysis of a Deep Simple Recurrent Unit Recurrent Neural Network (SRU-RNN) in MEMS Gyroscope De-Noising , 2018, Sensors.

[91]  Liang Xue,et al.  Dynamic Performance Comparison of Two Kalman Filters for Rate Signal Direct Modeling and Differencing Modeling for Combining a MEMS Gyroscope Array to Improve Accuracy , 2015, Sensors.

[92]  Antonio Pietrosanto,et al.  Development of a test plan and a testbed for performance analysis of MEMS-based IMUs under vibration conditions , 2020 .

[93]  Muhammad Ali,et al.  Compensation of temperature and acceleration effects on MEMS gyroscope , 2016, 2016 13th International Bhurban Conference on Applied Sciences and Technology (IBCAST).

[94]  John Cole,et al.  Optimizing noise and stability of MEMS accelerometers for various applications , 2016, 2016 IEEE/ION Position, Location and Navigation Symposium (PLANS).

[95]  Ran Chen,et al.  MEMS Gyro Signal De-Noising Based on Adaptive Stationary Wavelet Threshold , 2012 .

[96]  Afsar Saranli,et al.  Characterization and calibration of MEMS inertial sensors for state and parameter estimation applications , 2012 .

[97]  Thomas Gustafsson,et al.  Adaptive Kalman filtering based navigation: An IMU/GPS integration approach , 2011, 2011 International Conference on Networking, Sensing and Control.

[98]  Pavol Božek,et al.  Navigation control and stability investigation of a mobile robot based on a hexacopter equipped with an integrated manipulator , 2017 .

[99]  Antonino D'Alessandro,et al.  A Review of the Capacitive MEMS for Seismology , 2019, Sensors.

[100]  S. Sherman,et al.  Single-chip surface micromachined integrated gyroscope with 50°/h Allan deviation , 2002, IEEE J. Solid State Circuits.

[101]  Changku Sun,et al.  A hybrid method for MEMS gyroscope signal error compensation , 2018, Sensor Review.

[102]  J. Suykens.  Nonlinear modelling and support vector machines , 2001, IMTC 2001. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics (Cat. No.01CH 37188).

[103]  Rajesh Kumar,et al.  Joint Angle Measurement Using MEMs Based Inertial Sensors for Biped Robot , 2018, 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA).

[104]  Yingjun Pan,et al.  Research and implementation of a new orientation & incline instrument used in oil and gas wells , 2009, 2009 9th International Conference on Electronic Measurement & Instruments.

[105]  Baihai Zhang,et al.  Adaptive filtering for MEMS gyroscope with dynamic noise model. , 2020, ISA transactions.

[106]  Mushfiqul Alam,et al.  Adaptive Data Filtering of Inertial Sensors with Variable Bandwidth , 2014, Sensors.

[107]  Yu Zhang,et al.  Simple Recurrent Units for Highly Parallelizable Recurrence , 2017, EMNLP.

[108]  G. Schmidt,et al.  Inertial sensor technology trends , 2001 .

[109]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[110]  Yan Guizhen,et al.  An FPGA implementation of the LMS adaptive filter for MEMS gyroscope , 2010, 2010 IEEE 5th International Conference on Nano/Micro Engineered and Molecular Systems.

[111]  Liang Xue,et al.  Analysis of Dynamic Performance of a Kalman Filter for Combining Multiple MEMS Gyroscopes , 2014, Micromachines.

[112]  Huiliang Cao,et al.  Temperature Energy Influence Compensation for MEMS Vibration Gyroscope Based on RBF NN-GA-KF Method , 2018, Shock and Vibration.

[113]  Celal Batur,et al.  Robust adaptive control for a MEMS vibratory gyroscope , 2009 .

[114]  Mingyue Ding,et al.  Decision-based adaptive morphological filter for fixed-value impulse noise removal , 2014 .

[115]  Farrokh Ayazi,et al.  A Sub-0.2$^{\circ}/$ hr Bias Drift Micromechanical Silicon Gyroscope With Automatic CMOS Mode-Matching , 2009, IEEE Journal of Solid-State Circuits.

[116]  Mingguo Zhao,et al.  Towards artificial general intelligence with hybrid Tianjic chip architecture , 2019, Nature.

[117]  Kumar Virwani,et al.  Vibrational modes of MEMS resonators , 1999, Other Conferences.

[118]  Fredrik Öhberg,et al.  Does the Femoral Head Size in Hip Arthroplasty Influence Lower Body Movements during Squats, Gait and Stair Walking? A Clinical Pilot Study Based on Wearable Motion Sensors , 2019, Sensors.

[119]  Zhongke Shi,et al.  Composite Neural Learning-Based Nonsingular Terminal Sliding Mode Control of MEMS Gyroscopes , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[120]  Salvatore Pennisi,et al.  Sub-Femto-Farad Resolution Electronic Interfaces for Integrated Capacitive Sensors: A Review , 2020, IEEE Access.

[121]  Chen Jiabin,et al.  Research on the compensation in MEMS gyroscope random drift based on time-series analysis and Kalman filtering , 2015, 2015 34th Chinese Control Conference (CCC).