MEMS Inertial Measurement Unit Research Articles

A Review on the Inertial Measurement Unit Array of Microelectromechanical Systems.

In recent years, microelectromechanical systems (MEMS) technology has developed rapidly, and low precision inertial devices have achieved small volume, light weight, and mass production. Under this background, array technology has emerged to achieve high precision inertial measurement under the premise of low cost. This paper reviews the development of MEMS inertial measurement unit (IMU) array technology. First, the different types of common inertial measurement unit arrays are introduced and the basic principles are explained. Secondly, IMU array's development status is summarized by analyzing the research results over the years. Then, the key technologies and corresponding development status of IMU array are described, respectively, including error analysis modeling and calibration, data fusion technology, fault detection, and isolation technology. Finally, the characteristics and shortcomings of the past research results are summarized, the future research direction is discussed, and some thoughts are put forward to further improve the accuracy of the IMU array.

A robust adaptive error state Kalman filter for MEMS IMU attitude estimation under dynamic acceleration

Accurate estimation of attitude (pitch, roll) is a prerequisite for ensuring safe navigation during vehicle control execution. In dynamic environments, the presence of acceleration often degrades the accuracy and robustness of attitude estimation using traditional algorithms with Micro-Electro-Mechanical Systems (MEMS) Inertial Measurement Unit (IMU). This paper proposes a robust adaptive error state Kalman filter (RAESKF) algorithm for attitude estimation of MEMS IMU under dynamic acceleration conditions. The RAESKF algorithm decomposes the true attitude Direction Cosine Matrix (DCM) into nominal and error components. An error state Kalman filter is utilized to fuse real-time measurements from MEMS gyroscope and accelerometer, estimating the error component, which is then used to correct the nominal attitude DCM. Furthermore, within the error state Kalman filtering framework, a dynamic acceleration robust adaptive adjustment strategy is implemented. This strategy relies on real-time data monitoring, threshold-based decision-making, and switching mechanisms. It adaptively selects between dynamic acceleration model compensation and online adjustment of the measurement noise covariance matrix. The primary objective of this strategy is to mitigate the impact of disturbance induced by dynamic acceleration on attitude estimation, thereby enhancing the accuracy and robustness. Laboratory rotation experiments have demonstrated that the algorithm improved pitch and roll measurement accuracy by at least 8.81% and 11.69%, respectively, under rotational conditions. In dynamic acceleration conditions, pitch and roll measurement accuracy improved by at least 42.76% and 67.21%, respectively.

Thermal calibration for triaxial gyroscope of MEMS-IMU based on segmented systematic method

With the progress of micro electromechanical system (MEMS) technology, the performance of MEMS inertial measurement unit (IMU) composed of gyroscopes and accelerometers has been improved. Among the inertial sensors, MEMS triaxial gyroscope plays an important role in attitude estimation, navigation and positioning of intelligent mobile terminals such as unmanned aerial vehicles and unmanned vehicles. However, the measured values of low and medium cost MEMS triaxial gyroscopes are mainly affected by temperature (or thermal effect) and random errors. As results, the drift errors correlated with temperature will reduce application accuracy of MEMS triaxial gyroscope. The traditional calibration method for thermal drift errors relies on the expensive equipment, such as turntable and the temperature control system, which increases the cost of calibration. Therefore, an effective thermal calibration method that is available when using low- or high-cost tools for MEMS triaxial gyroscope will be meaningful for majority users. Hence, this paper analyzed and established the thermal drift model of MEMS triaxial gyroscope, and proposed a segmented systematic calibration method based on 24 states according to this model. Simulation shows that the proposed method can obtain the results approaching the real parameter thermal drift curves. Calibration experiments and parameters test show that the max errors of attitude calculation are reduced to 10% of the results using the original parameters, which indicates that the effectiveness of the proposed method.

Multi-Position Inertial Alignment Method for Underground Pipelines Using Data Backtracking Based on Single-Axis FOG/MIMU.

The inertial measurement method of pipelines utilizes a Micro-Electro-Mechanical Systems Inertial Measurement Unit (MIMU) to get the three-dimensional trajectory of underground pipelines. The initial attitude is significant for the inertial measurement method of pipelines. The traditional method to obtain the initial attitude uses three-axis magnetometers to measure the Earth's magnetic field. However, the magnetic field in urban underground pipelines is intricate, which leads to the initial attitude being inaccurate. To overcome this challenge, a novel multi-position initial alignment method based on data backtracking for a single-axis FOG and a three-axis Micro-Electro-Mechanical Inertial Measurement Unit (MIMU) is proposed. Firstly, the configuration of the sensors is determined. Secondly, according to the three-point support structure of the pipeline measuring instrument, a three-position alignment scheme is designed. Additionally, an initial alignment algorithm using the data backtracking method is developed. In this algorithm, a rough initial alignment is conducted by the data from single-axis FOG, and a fine initial alignment is conducted by the data from FOG/MIMU. Finally, an experiment was conducted to validate this method. The experiment results indicate that the pitch and roll angle errors are less than 0.05°, and the azimuth angle errors are less than 0.2°. This improved the precision of the 3-D trajectory of underground pipelines.

Accuracy and reliability improved GNSS partial ambiguity resolution assisted by a MEMS inertial measurement unit

Traditional partial ambiguity resolution (PAR) methods based on integer least-squares (ILS-PAR) assume that the ambiguity terms are unbiased integers, which is seldomly true in challenging environments of the Global Navigation Satellite System (GNSS). The bias will lead to unreliable positioning solutions. This study proposes a reliable PAR method based on the tightly coupling of GNSS and inertial navigation system (INS) and the solution separation with external solution (SSE) statistical testing algorithm. First, the inertial measurement unit (IMU) mechanization results are compensated using the latest fixed real-time kinematic (RTK) solutions, thus high-accuracy INS predictions are obtainable at the current GNSS epoch. Second, the SSE method based on double-differenced (DD) carrier-phase measurements are applied to select and validate the ambiguity subset and perform the GNSS/INS integration. A dynamic vehicular experiment with a low-cost microelectromechanical systems (MEMS) device has been conducted and the results indicate that the proposed method can ensure good performance of both accuracy and reliability.

Multi-level information fusion enhanced by scene constraint: Key to improve autonomous positioning accuracy in urban underground pipeline using MEMS inertial sensors

Accurate positioning inside confined urban underground pipelines is challenging due to the lack of geo-reference and the difficulty of access for geodetic apparatus. Micro-electromechanical system (MEMS) inertial navigation systems (INS) have become the mainstream positioning approach but face challenges in maintaining accuracy over a long time period because aiding for INS is limited. We propose a factor graph optimization (FGO) architecture for aided INS inside pipes, which enhances positioning accuracy by involving information fusion from three levels: measurements from physical sensors, motion constraints, and scene constraints. We achieved autonomous positioning using a typical industrial-grade MEMS inertial measurement unit within a 1700 m pipeline, with positioning accuracy of up to 1.0 m horizontally and 0.5 m vertically. Results demonstrate that the accuracy in horizontal and vertical directions improved by at least 45% and 26%, respectively, using FGO compared with traditional Kalman filtering methods.

Measurement System for the Calibration of Accelerometer Arrays

This paper addresses accelerometer array calibration, focusing on determining the errors between multiple sensors. Micro-electromechanical system (MEMS) based triaxial accelerometers, key components of Inertial Measurement Units (IMUs), are used in localization, robotics, and navigation systems. The requirements of these applications necessitate low-cost sensors, which makes MEMS IMUs a reasonable choice. However, these low-cost IMUs are significantly affected by systematic (i.e., bias, misalignment, scale-factor) and random errors. Achieving reliable sensor output depends on the precision of the executed calibration method. While traditional laboratory-based sensor calibration using specialized equipment (i.e., three-axis turntable) is accurate, it is time-consuming and costly. In contrast, in-field calibration techniques, which can be performed using a mechatronic actuator or a robotic arm, have gained popularity. These techniques involve comparing sensor measurements to established reference values. The MEMS sensors are increasingly being used in multi-sensor applications, which demands not only individual sensor error calibration but also important to determine the axis misalignment between the used sensors. During calibration process, various optimization algorithms (e.g., GA, PSO) can also be used to find the error parameters. The proposed measurement system allows for individual calibration of misalignment, bias, and scale factor of the sensor array, and eliminates between-sensor misalignment errors.

Robust Attitude Estimation for Low-Dynamic Vehicles Based on MEMS-IMU and External Acceleration Compensation.

Attitude determination based on a micro-electro-mechanical system inertial measurement unit (MEMS-IMU) has attracted extensive attention. The non-gravitational components of the MEMS-IMU have a significant effect on the accuracy of attitude estimation. To improve the attitude estimation of low-dynamic vehicles under uneven soil conditions or vibrations, a robust Kalman filter (RKF) was developed and tested in this paper, where the noise covariance was adaptively changed to compensate for the external acceleration of the vehicle. The state model for MEMS-IMU attitude estimation was initially constructed using a simplified direction cosine matrix. Subsequently, the variance of unmodeled external acceleration was estimated online based on filtering innovations of different window lengths, where the acceleration disturbance was addressed by tradeoffs in time-delay and prescribed computation cost. The effectiveness of the RKF was validated through experiments using a three-axis turntable, an automatic vehicle, and a tractor tillage test. The turntable experiment demonstrated that the angle result of the RKF was 0.051° in terms of root mean square error (RMSE), showing improvements of 65.5% and 29.2% over a conventional KF and MTi-300, respectively. The dynamic attitude estimation of the automatic vehicle showed that the RKF achieves smoother pitch angles than the KF when the vehicle passes over speed bumps at different speeds; the RMSE of pitch was reduced from 0.875° to 0.460° and presented a similar attitude trend to the MTi-300. The tractor tillage test indicated that the RMSE of plough pitch was improved from 0.493° with the KF to 0.259° with the RKF, an enhancement of approximately 47.5%, illustrating the superiority of the RKF in suppressing the external acceleration disturbances of IMU-based attitude estimation.

A Near-Vertical Well Attitude Measurement Method with Redundant Accelerometers and MEMS IMU/Magnetometers

Vertical drilling is the first stage of petroleum exploitation and directional well technology. The near-vertical attitude at each survey station directly determines the whole direction accuracy of the borehole trajectory. However, the attitude measurement for near-vertical wells has poor azimuth accuracy because the poor signal-to-noise ratio of radial accelerometers hardly obtains the correct horizontal attitude, especially the roll angle. In this paper, a novel near-vertical attitude measurement method was proposed to address this issue. The redundant micro-electromechanical system (MEMS) accelerometers were employed to replace the original accelerometers from MEMS inertial measurement unit (IMU)/magnetometers for calculating horizontal attitude under near-vertical conditions. In addition, a simplified four-position calibration method for the redundant accelerometers was proposed to compensate for the installation and non-orthogonal error. We found that the redundant accelerometers enhanced the signal-to-noise ratio to upgrade the azimuth accuracy at the near-vertical well section. Compared with the traditional method, the experiment results show that the average azimuth errors and roll errors are reduced from 34.45° and 27.09° to 5.7° and 0.61°, respectively. The designed configuration scheme is conducive to the miniaturized design and low-cost requirements of wellbore measuring tools. The proposed attitude measurement method can effectively improve the attitude accuracy of near-vertical wells.

Calibration method of strapdown inertial measurement unit based on two-axis turntable

Abstract This paper analyzes the causes and effects of error in the Strapdown Micro-Electro-Mechanical System Inertial Measurement Unit (MEMS-IMU), utilizing a sensor calibration method based on a two-axis turntable. By considering deterministic errors such as sensor bias, scale factor, and non-orthogonality of the sensitive axis, an error model was established to assess their impact on measurement accuracy. An expression was provided to represent the relationship between sensor data collection and external input stimuli, storing the error parameters of the accelerometer and the gyroscope separately in parameter matrices. The error parameter matrix between input and output quantities was fitted using the least squares method, and error compensation was completed in conjunction with the error model. By comparing the original data with the error-compensated data, the calibration results were obtained. This improved the consistency of the measurements from the sensitive axes to the stimulus source, reduced the zero bias error of each sensitive axis, and enhanced the positioning accuracy of the Strapdown MEMS-IMU.

Redundant Configuration Method of MEMS Sensors for Bottom Hole Assembly Attitude Measurement.

Micro-electro-mechanical systems inertial measurement units (MEMS-IMUs) are increasingly being employed for measuring the attitude of bottom hole assemblies (BHAs). However, the reliability and measurement precision of a single MEMS-IMU may not meet drilling's stringent needs. Redundant MEMS-IMU systems can effectively enhance the reliability and precision. This paper proposes a redundant configuration method for MEMS sensors tailored to BHA attitude measurement. Firstly, based on reliability theory and a cost-benefit analysis, considering factors such as cost, size, and reliability, the optimal number of sensors in the redundant system was determined to be six. Considering the structural characteristics of the BHA, a hollow hexagonal prism-shaped redundant configuration scheme was proposed, ensuring the circulation of drilling fluid within the drill pipe. Next, by employing Kalman filtering to integrate the output data from the six sensors, a virtual IMU (VIMU) was formed. Finally, experimental verification was carried out. The results confirmed that, after redundancy implementation, the velocity random walk of the accelerometer decreased by an average of 58% compared to a single MEMS-IMU, and bias instability was reduced by an average of 54%. The angular random walk of the gyroscope decreased by an average of 58%, and bias instability was reduced by an average of 37%. This research provides a theoretical foundation for enhancing the precision and reliability of BHA attitude measurements.

Development of a Motion Controller for the Electric Wheelchair of Quadriplegic Patients Using Head Movements Recognition

The robotic wheelchair is designed to allow independent movement for patients with severe musculoskeletal and neuromuscular disorders that interfere with normal wheelchair propulsion by hands. Control of the movement of the wheelchair necessities residual motion of the upper extremity which was not applicable for patients with upper extremity disability as in Quadriplegic patients. Several research efforts have been proposed to help quadriplegic patients independently use the robotic wheelchair. However, the proposed solutions had many problems with accuracy, simplicity, and cost. The current work aims to build an accurate, simple, and low-cost control system for robotic wheelchairs using head movement. The proposed system was based on the use of the MEMs Inertial measurement unit (IMU) to sense the head movement gestures and transfer the gestures into signals to control the wheelchair movements. A new lightweight algorithm for the detection of head gestures was proposed to improve the system’s accuracy. The proposed system has been applied and tested on volunteers. The results revealed that the accuracy of the proposed method in recognizing head movement was 97% on average.

A smart detection method for sleep posture based on a flexible sleep monitoring belt and vital sign signals

People spend approximately one-third of their lives in sleep, but more and more people are suffering from sleep disorders. Sleep posture is closely related to sleep quality, so related detection is very significant. In our previous work, a smart flexible sleep monitoring belt with MEMS triaxial accelerometer and pressure sensor has been developed to detect the vital signs, snore events and sleep stages. However, the method for sleep posture detection has not been studied. Therefore, to achieve high performance, low cost and comfortable experience, this paper proposes a smart detection method for sleep posture based on a flexible sleep monitoring belt and vital sign signals measured by a MEMS Inertial Measurement Unit (IMU). Statistical analysis and wavelet packet transform are applied for the feature extraction of the vital sign signals. Then the algorithm of recursive feature elimination with cross-validation is introduced to further extract the key features. Besides, machine learning models with 10-fold cross validation process, such as decision tree, random forest, support vector machine, extreme gradient boosting and adaptive boosting, were adopted to recognize the sleep posture. 15 subjects were recruited to participate the experiment. Experimental results demonstrate that the detection accuracy of the random forest algorithm is the highest among the five machine learning models, which reaches 96.02 %. Therefore, the proposed sleep posture detection method based on the flexible sleep monitoring belt is feasible and effective.

Robust Orientation Estimation from MEMS Magnetic, Angular Rate, and Gravity (MARG) Modules for Human-Computer Interaction.

While the availability of low-cost micro electro-mechanical systems (MEMS) accelerometers, gyroscopes, and magnetometers initially seemed to promise the possibility of using them to easily track the position and orientation of virtually any object that they could be attached to, this promise has not yet been fulfilled. Navigation-grade accelerometers and gyroscopes have long been the basis for tracking ships and aircraft, but the signals from low-cost MEMS accelerometers and gyroscopes are still orders of magnitude poorer in quality (e.g., bias stability). Therefore, the applications of MEMS inertial measurement units (IMUs), containing tri-axial accelerometers and gyroscopes, are currently not as extensive as they were expected to be. Even the addition of MEMS tri-axial magnetometers, to conform magnetic, angular rate, and gravity (MARG) sensor modules, has not fully overcome the challenges involved in using these modules for long-term orientation estimation, which would be of great benefit for the tracking of human-computer hand-held controllers or tracking of Internet-Of-Things (IoT) devices. Here, we present an algorithm, GMVDμK (or simply GMVDK), that aims at taking full advantage of all the signals available from a MARG module to robustly estimate its orientation, while preventing damaging overcorrections, within the context of a human-computer interaction application. Through experimental comparison, we show that GMVDK is more robust to magnetic disturbances than three other MARG orientation estimation algorithms in representative trials.

Assessment of Noise of MEMS IMU Sensors of Different Grades for GNSS/IMU Navigation.

Inertial measurement units (IMUs) are key components of various applications including navigation, robotics, aerospace, and automotive systems. IMU sensor characteristics have a significant impact on the accuracy and reliability of these applications. In particular, noise characteristics and bias stability are critical for proper filter settings to perform a combined GNSS/IMU solution. This paper presents an analysis based on the Allan deviation of different IMU sensors that correspond to different grades of micro-electromechanical systems (MEMS)-type IMUs in order to evaluate their accuracy and stability over time. The study covers three IMU sensors of different grades (ascending order): Rokubun Argonaut navigator sensor (InvenSense TDK MPU9250), Samsung Galaxy Note10 phone sensor (STMicroelectronics LSM6DSR), and NovAtel PwrPak7 sensor (Epson EG320N). The noise components of the sensors are computed using overlapped Allan deviation analysis on data collected over the course of a week in a static position. The focus of the analysis is to characterize the random walk noise and bias stability, which are the most critical for combined GNSS/IMU navigation and may differ or may not be listed in manufacturers' specifications. Noise characteristics are calculated for the studied sensors and examples of their use in loosely coupled GNSS/IMU processing are assessed. This work proposes a structured and reproducible approach for working with sensors for their use in navigation tasks in combination with GNSS, and can be used for sensors of different levels to supplement missing or incorrect sensor manufacturers' data.

Attitude and heading measurement based on adaptive complementary Kalman filter for PS/MIMU integrated system.

The bionic polarization sensor (PS)/MEMS inertial measurement unit (MIMU) integrated system can provide reliable attitude and heading information for unmanned vehicles in the case of GNSS rejection. However, the existing measurement methods have poor adaptability to inclining, sheltering, and other harsh environments, and do not make full use of the complementary characteristics of the gyroscopes, accelerometers, and PS, which seriously affects the system performance. Therefore, this paper proposes an attitude and heading measurement method based on an adaptive complementary Kalman filter (ACKF), which corrects the gyroscopes according to the gravity measured by the accelerometers to improve the attitude accuracy and fuses the IMU heading and tilt-compensated polarization heading by Kalman optimal estimation. On this basis, the maximum correlation entropy of the measured gravity and the theoretical gravity is used to construct an adaptive factor to realize the adaptive complementary of the gyroscopes and the accelerometers. Finally, the effectiveness of the method is verified by the outdoor rotation test without occlusion and the vehicle test with occlusion. Compared with the traditional Kalman filter, the pitch, roll, and heading RMSE of the vehicle test are reduced by 89.3%, 93.2% and, 9.6% respectively, which verifies the great advantages.

Hovering Flight of a Robotic Hummingbird: Dynamic Observer and Flight Tests

The paper reports on flight tests at hovering of the COLIBRI robot. After a short review of the control model and the stabilization strategy, two different approaches are considered for the attitude reconstruction from the MEMS Inertial Measurement Unit (IMU): the complementary filter and the full-state dynamic observer, implemented in a specially designed flight control board. It is shown that both strategies provide adequate stabilization at hovering in spite of the strong vibration excitation resulting from the flapping of the wings. Moreover, it is shown that the residual wandering due to noise, robot imperfection, etc., can be significantly reduced by a cascade control loop based on the axial and lateral velocities reconstructed by the full-state observer. Experiments show that this approach based on onboard measurements allows for a station keeping as good as that obtained with velocities reconstructed from an external tracking system. The paper also reports endurance tests conducted with two different robot configurations; the maximum flight time observed is 4 min 30 s.

Analysis of Coupled Vibration Characteristics of Linear-Angular and Parameter Identification

Abstract A steady-state sinusoidal and distortion-free excitation source is very important for the accuracy and consistency of the calibration parameters of micro-electro-mechanical systems (MEMS) inertial sensors. To solve the problem that the current MEMS inertial measurement unit (IMU) calibration device is unable to reproduce the spatial motion of linear and angular vibration coupling, research topics on the coupling vibration characteristics and parameter identification for an electromagnetic linear-angular vibration exciter are proposed. This research paper used Ampere’s law and Lorentz force to establish the analytical expressions for the electromagnetic force and electromagnetic torque of the electromagnetic linear-angular vibration exciter. Then, the main purpose of this paper is to establish uniaxial and coupled vibration electromechanical analogy models containing mechanical parameters based on the admittance-type electromechanical analogy principle, and the parameter identification model is also obtained by combining the impedance formula with the additional mass method. Finally, the validity of the coupling vibration characteristics and the parameter identification model are verified by the frequency response simulation and the additional mass method, and the relative error of each parameter identification is within 5% in this paper.

Dual-antenna GNSS-aided robust MEMS IMU initial alignment under the earth-centered-earth-fixed frame

The micro-electro-mechanical system (MEMS), with its small size, low cost and low power, has attracted widespread attentions in recent year, and the initial alignment of the inertial measurement unit (IMU) is an indispensable step before the navigation computation. In the traditional alignment methods with only the velocity measurements, the yaw angle is subject to divergence under low dynamic scenarios, and the measurements are also susceptible to the external environments. Furthermore, the navigation frame (n frame) is usually chosen as the reference frame. Although intuitive, it is more computationally complex compared with the earth-centered-earth-fixed frame (e frame). Therefore, to improve the alignment performance and computational efficiency, a robust MEMS IMU initial alignment method with yaw assistance under the e frame is proposed. In this method, to address the issues of the yaw divergency and computational inefficiency in the traditional methods, the relationship between the yaw angle, derived from dual-antenna global navigation satellite system baseline solution, and IMU error is derived and incorporated into the state space model with e frame as the reference frame. Besides, a robust extended Kalman filtering is adopted to further improve the system’s performance of outlier resistance, whose robust factors are determined by the Institute of Geodesy and Geophysics Ⅲ model. To validate the performance of the proposed method, two field experiments with the velocity of about 0.7 m s−1 and 1.5 m s−1 was conducted, and the collected data were processed for contrastive analysis. The results show that the effectiveness of the proposed method at suppressing yaw angle divergence and improving the alignment accuracy by resisting outliers under low dynamic scenarios compared with the traditional method. Moreover, the elapsed time is reduced by about 11% when the reference frame in data processing is switched from the n frame to the e frame.

Industrial-Grade MEMS IMU Calibration

The paper is devoted to the development of the calibration method for MEMS-sensors based inertial measurement unit (IMU). The general purpose of this work was to create the industrial-grade MEMS IMU calibration method. The method was supposed to reduce the temperature instability of inertial sensors static signal to a level of 100 °/h for rate sensors and 1 mg of accelerometers. The article provides a general description of the error types and accuracy classes of inertial sensors. The inertial sensors calibration principle using a known inertial impact series and the obtained dependence approximation by the n-th power polynomial is described too. The calibration methods for single axis and triaxial accelerometers and rate sensors using low and high precision stands are considered. Based on the considered materials, the digital inertial sensors calibration method using a triaxial high precision stand was developed. The method includes the ARS calibration over the entire measurement range and accelerometers calibration within the ±1 g range. The inertial module calibration experiment showed that the developed method allows achieving the required output signal stability level of the calibrated product over the temperature range.