MEMS Inertial Measurement Unit Research Articles

A Multisignal Wavelet Variance-Based Framework for Inertial Sensor Stochastic Error Modeling

The calibration of low-cost inertial sensors has become increasingly important over the last couple of decades, especially when dealing with sensor stochastic errors. This procedure is commonly performed on a single error measurement from an inertial sensor taken over a certain amount of time, although it is extremely frequent for different replicates to be taken for the same sensor, thereby delivering important information which is often left unused. In order to address the latter problem, this paper presents a general wavelet variance-based framework for multisignal inertial sensor calibration, which can improve the modeling and model selection procedures of sensor stochastic errors using all replicates from a calibration procedure and allows to understand the properties, such as stationarity, of these stochastic errors. The applications using microelectromechanical system inertial measurement units confirm the importance of this new framework, and a new graphical user interface makes these tools available to the general user. The latter is developed based on an R package called mgmwm and allows the user to select a type of sensor for which different replicates are available and to easily make use of the approaches presented in this paper in order to carry out the appropriate calibration procedure.

A new positioning method for remotely operated vehicle of the nuclear power plant

PurposeThe purpose of this study is to develop a new positioning method for remotely operated vehicle (ROV) in the nuclear power plant. The ROV of the nuclear power plant is developed to inspect the reactor cavity pools, the component pools and spent-fuel storage pools. To enhance the operational safety, the ability of localizing the ROV is indispensable.Design/methodology/approachTherefore, the positioning method is proposed based on the MEMS inertial measurement unit and mechanical scanning sonar in this paper. Firstly, the ROV model and on board sensors are introduced in detail. Then the sensor-based Kalman filter is deduced for attitude estimation. After that, the positioning method is proposed that divided into static positioning and dynamic positioning. The improved iterative closest point-Kalman filter is deduced to estimate the global position by the whole circle scanning sonar data in static, and the relative positioning method is proposed by the small scale scanning sonar data in dynamic.FindingsThe performance of the proposed method is verified by comparing with the visual positioning system. Finally, the effectiveness of the proposed method is proved by the experiment in the reactor simulation pool of the Daya Bay Nuclear Power Plant.Originality/valueThe research content of this manuscript is aimed at the specific application needs of nuclear power plants and has high theoretical significance and application value.

A Nonlinear Observer for Attitude Estimation of Vehicle-Mounted Satcom-on-the-Move

In this paper, a low-cost attitude estimation system (AES) is designed to estimate the attitude information of the vehicle utilizing MEMS inertial measurement unit (MIMU) and single baseline global positioning system (GPS). The proposed AES adopts a nonlinear attitude observer to fuse different sensors and estimate the vehicle's attitude. With the help of single baseline GPS, a maneuver accelerations compensation algorithm is used to improve the accuracy of the attitude angles and gyro biases. Modified quaternions are also used for the attitude observer to separate the yaw angle from the gravity angles easily when the single baseline GPS information is absent. The proposed AES judges the maneuver state of the vehicle depending on a switch criteria and adjusts the gain of the observer during GPS outages. The rules can efficiently detect the maneuver state of the vehicle and make full use of the inertial sensor outputs than classical rules. The experimental tests show that the proposed AES has a good attitude estimation accuracy, which meets the requirements of the Satcom-on-the-Move (SOTM) systems.

Performance Assessment of an Ultra Low-Cost Inertial Measurement Unit for Ground Vehicle Navigation.

Nowadays, navigation systems are becoming common in the automotive industry due to advanced driver assistance systems and the development of autonomous vehicles. The MPU-6000 is a popular ultra low-cost Microelectromechanical Systems (MEMS) inertial measurement unit (IMU) used in several applications. Although this mass-market sensor is used extensively in a variety of fields, it has not caught the attention of the automotive industry. Moreover, a detailed performance analysis of this inertial sensor for ground navigation systems is not available in the previous literature. In this work, a deep examination of one MPU-6000 IMU as part of a low-cost navigation system for ground vehicles is provided. The steps to characterize the performance of the MPU-6000 are divided in two phases: static and kinematic analyses. Besides, an additional MEMS IMU of superior quality is also included in all experiments just for the purpose of comparison. After the static analysis, a kinematic test is conducted by generating a real urban trajectory registering an MPU-6000 IMU, the higher-grade MEMS IMU, and two GNSS receivers. The kinematic trajectory is divided in two parts, a normal trajectory with good satellites visibility and a second part where the Global Navigation Satellite System (GNSS) signal is forced to be lost. Evaluating the attitude and position inaccuracies from these two scenarios, it is concluded in this preliminary work that this mass-market IMU can be considered as a convenient inertial sensor for low-cost integrated navigation systems for applications that can tolerate a 3D position error of about 2 m and a heading angle error of about 3 °.

Calibration, Alignment, and Dynamic Tilt Maintenance Method Based on Vehicular Hybrid Measurement Unit

Aiming at the urgently required for miniaturization, low cost and high reliability in north seeking, and attitude maintenance, a six-axis MEMS inertial measurement unit (IMU) and single-axis fiber optic gyro (FOG) composite measuring device is proposed, in which the six-axis MEMS IMU is installed in a conventional orthogonal configuration; while the single-axis FOG is mounted in parallel with the MEMS horizontal X-axis approximately. Based on this new combinational inertial measurement device, there are two functions can be achieved: North-seeking function, three-axis MEMS accelerometers, and single-axis FOG are used to complete four-position north seeking and initial horizontal attitude calculation. Once the alignment is completed, the novel equipment constructs an attitude maintaining device and transfer the calculated attitude and orientation results to the six-axis MEMS-IMU. Concerning the low-speed working environment and changeable motion state of the equipment, the static state and uniform motion of the instrument can be automatically detected. The MEMS gyro error and attitude error are corrected by filter while in static and uniform-speed state, and maintain attitude tracking in other condition. A detailed calibration, alignment, and attitude maintaining method of the new measuring device is introduced in this paper. The calibration of the turntable, the multi-position alignment of the turntable, and the dynamic performance experimental results demonstrate that the novel equipment and method can effectively and feasibility achieve the user index requirements. The three-axis alignment accuracy is better than 1.5 degree ( $3 \sigma $ ), and the 15-minute tilt maintaining accuracy is better than 2 degree ( $3 \sigma $ ).

Integrated piezoelectric micromechanical vibration platform for six degree of freedom motion

Piezoelectric micromechanical actuators are featured with a low driving voltage, fast response and large-range output, which make them desirable for applications of long-term drift error self-calibration in microelectromechanical systems (MEMS) inertial measurement units. A micromechanical vibration platform supported by four folded beams and excited by 32 partitioned electrodes based on PZT film is presented in this paper. PZT-silicon on insulator based wafer-level MEMS process and packaging technique are conducted in the manufacture and integration for the micro vibration platform. The six-degree-of-freedom (6 DOF) motion of the micro vibration platform is experimentally verified. In addition, the motion behavior control, damping characteristics and fatigue characteristics are analyzed. The 6 DOF micro vibration platform has a stable capability of large-range dynamic output.

Performance Enhancement Method for Angular Rate Measurement Based on Redundant MEMS IMUs

Aiming at the low-cost, wide-range, and accurate measurement requirement for Microelectromechanical System (MEMS) Inertial Measurement Unit (IMU) on a multi-rotor Unmanned Aerial Vehicle (UAV), the paper designs a heterogeneous parallel redundancy configuration scheme. In redundant MEMS IMUs, a high-cost and small-range MEMS gyroscope is combined with low-cost and large-range MEMS gyroscopes. Then, an adaptive data fusion method of redundant MEMS gyroscopes is proposed. By the designed experiments based on the simulation data and the sensor measurement data, the proposed method has been proved that it can effectively improve the angular rate measurement performance of the multi-rotor UAV and broaden the angular rate measurement range on the basis of saving the configuration cost and volume of the micro IMU.

Optimization-based gravity-assisted calibration and axis alignment of 9-degrees of freedom inertial measurement unit without external equipment

Applicable in numerous fields, low-cost micro-electromechanical system inertial measurement units often require on-sight calibration by the end user due to the existence of systematic errors. A 9-degrees of freedom inertial measurement unit comprises a tri-axis accelerometer, a tri-axis gyroscope, and a tri-axis magnetometer. Various proposed multi-position calibration methods can calibrate tri-axis accelerometers and magnetometers to a degree. Yet the full calibration of a tri-axis gyroscope and axis alignment of all the sensors still often requires equipment such as a rate table to generate a priori known angular velocities and attitudes or relies on the disturbance-prone magnetometer output as a reference. This study proposes an augmentation to the popular multi-position calibration scheme, capable of fully calibrating and aligning the sensor axes of the 9-degrees of freedom inertial measurement unit while eliminating the reliance on external equipment or magnetometer. The algorithm does not rely on the inertial measurement unit attitude during various stages of the multi-position data acquisition. Instead, it uses the gravity vector measured by the accelerometer to calibrate the gyroscope and align the magnetometer axes with the sensor body frame. Experimental results using a navigation module with factory calibration and extensive simulation results indicate the current method's ability in estimating large calibration parameters with relative errors below 0.5%.

A Navigation Solution Using a MEMS IMU, Model-Based Dead-Reckoning, and One-Way-Travel-Time Acoustic Range Measurements for Autonomous Underwater Vehicles

Recent advances in acoustic navigation methods are enabling extended autonomous underwater vehicles' (AUVs) mission time while maintaining their XY position error within appropriate limits. Furthermore, while advances in inertial sensor technology are drastically lowering the size, power consumption, and cost of these sensors, these sensors remain noisy and accrue error over time. This paper builds on the research and recent developments in single beacon one-way-travel-time acoustic navigation and investigates the degree of bounding position error for small AUVs with a minimal navigation strapdown sensor suite, relying on a consumer grade microelectromechanical system inertial measurement unit (IMU) and a vehicle's dynamic model velocity. An implementation of an extended Kalman filter that includes IMU bias estimation and coupled with a range filter is obtained in the field on two types of AUVs. Results from these field trials in controlled environments and ocean show that the reported navigation solution possesses an accuracy comparable to existing methods.

A Low-Cost Chamber Prototype for Automatic Thermal Analysis of MEMS IMU Sensors in Tilt Measurements Perspective.

In this work, a low-cost, open-source and replicable system prototype for thermal analysis of low-cost Micro Electro-Mechanical Systems (MEMS) Inertial Measurement Unit (IMU) sensors in tilt measurement perspective is presented and tested. The system is formed of a 3D printed frame, a thermal cell consisting in a Peltier element mounted over a heat sink, and a control and power system. The frame is designed to allow the independent biaxial tilting of the thermal cell through two servomotors. The control board is formed by an Arduino® and a self-made board including a power drive for controlling the thermal unit and servomotors. We tested the chamber analyzing the behavior of multiple MEMS IMU onboard accelerometers suitable for measuring tilt. Our results underline the variability of the thermal behavior of the sensors, also for different sensor boards of the same model, and consequently the need for the adoption of a thermal compensation strategy based on thermal analysis results. These data suggesting the need for the analysis of the thermal behavior of MEMS-based sensors, indicate the potential of our system in making low-cost sensors suitable in medium-to-high precision monitoring applications.

Error characteristics analysis and calibration testing for MEMS IMU gyroscope

Microelectromechanical systems’ (MEMS) inertial measurement unit (IMU) is widely used in many scenarios for its small size, low weight, and low-power consumptions. However, it possesses relatively low positioning accuracy compared with other high-grade IMUs, as errors accumulate quickly over time. This paper mainly focuses on the error characteristics of the gyro part of MEMS IMU by analyzing different kinds of error parameters. As there is no published standard for MEMS gyro error characterization, three dominant error parameters are selected and investigated, namely, scale factor, in-run bias stability, and angular random walk. In addition, Allan variance analysis is deployed as an important part of the scheme with relative results presented in this paper. Not only is theoretical analysis presented, but experimental verification is also carried out correspondingly with an ADIS16490 MEMS IMU. By comparison, we find that the results of in-run bias stability exceed the given features by up to ten times, while the rest of the results agree quite well with the given features. Possible reasons for the exceeding part are given. Calibration testing results not only provide concrete characterization for MEMS gyro errors, but also enhance the importance of overall calibration of MEMS IMU before use.

A New Time-Frequency Feature Extraction Method for Action Detection on Artificial Knee by Fractional Fourier Transform.

With the aim of designing an action detection method on artificial knee, a new time–frequency feature extraction method was proposed. The inertial data were extracted periodically using the microelectromechanical systems (MEMS) inertial measurement unit (IMU) on the prosthesis, and the features were extracted from the inertial data after fractional Fourier transform (FRFT). Then, a feature vector composed of eight features was constructed. The transformation results of these features after FRFT with different orders were analyzed, and the dimensions of the feature vector were reduced. The classification effects of different features and different orders are analyzed, according to which order and feature of each sub-classifier were designed. Finally, according to the experiment with the prototype, the method proposed above can reduce the requirements of hardware calculation and has a better classification effect. The accuracies of each sub-classifier are 95.05%, 95.38%, 91.43%, and 89.39%, respectively; the precisions are 78.43%, 98.36%, 98.36%, and 93.41%, respectively; and the recalls are 100%, 93.26%, 86.96%, and 86.68%, respectively.

Time-delayed multiple linear regression for de-noising MEMS inertial sensors

Compensation of errors from MicroElectroMechanical Systems (MEMS) inertial sensors is mandatory for real-world applications. Recently, several machine learning methods focused on dealing with nonlinear behaviors have been proposed to increase MEMS inertial sensors performance. However, manufacturers of MEMS inertial sensors claim that nonlinearity in these devices is negligible in most cases. This article provides a rigorous analysis of the viability of Time Delayed Multiple Linear regression technique (TD-MLR) for decreasing white noise observed in MEMS inertial sensors. TD-MLR is evaluated on four MEMS inertial measurement units and compared to two well-known methods with different complexity levels, i.e., Moving Average (MA) filtering and the Multi Layer Perceptron (MLP). A strong statistical framework is applied on the three methods to guarantee that optimal models are obtained during the adjustment phase. Experimental results show that no significant statistical differences exist between TD-MLR and MLP. In addition, TD-MLR proves to be a remarkable improvement over MA.

Performance Enhancement of Pedestrian Navigation Systems Based on Low-Cost Foot-Mounted MEMS-IMU/Ultrasonic Sensor.

The pedestrian navigation system (PNS) based on inertial navigation system-extended Kalman filter-zero velocity update (INS-EKF-ZUPT or IEZ) is widely used in complex environments without external infrastructure owing to its characteristics of autonomy and continuity. IEZ, however, suffers from performance degradation caused by the dynamic change of process noise statistics and heading estimation errors. The main goal of this study is to effectively improve the accuracy and robustness of pedestrian localization based on the integration of the low-cost foot-mounted microelectromechanical system inertial measurement unit (MEMS-IMU) and ultrasonic sensor. The proposed solution has two main components: (1) the fuzzy inference system (FIS) is exploited to generate the adaptive factor for extended Kalman filter (EKF) after addressing the mismatch between statistical sample covariance of innovation and the theoretical one, and the fuzzy adaptive EKF (FAEKF) based on the MEMS-IMU/ultrasonic sensor for pedestrians was proposed. Accordingly, the adaptive factor is applied to correct process noise covariance that accurately reflects previous state estimations. (2) A straight motion heading update (SMHU) algorithm is developed to detect whether a straight walk happens and to revise errors in heading if the ultrasonic sensor detects the distance between the foot and reflection point of the wall. The experimental results show that horizontal positioning error is less than 2% of the total travelled distance (TTD) in different environments, which is the same order of positioning error compared with other works using high-end MEMS-IMU. It is concluded that the proposed approach can achieve high performance for PNS in terms of accuracy and robustness.

Positioning Accuracy of a Pipeline Surveying System Based on MEMS IMU and Odometer: Case Study

An inertial navigation system (INS) aided by odometers and a series of reference points has been extensively used to determine the location of pipeline inspection gauges (PIGs) in long-range gas and oil pipeline inspection. However, little attention has been paid to short-range urban underground pipeline surveying and the use of a low-cost inertial measurement unit (IMU) for location determination. The positioning performance of PIGs using low-cost microelectromechanical systems (MEMS) INS for short-range urban underground pipeline surveying applications has seldom been comprehensively evaluated. In this paper, we analyzed the positioning accuracy of a PIG based on a MEMS IMU and odometer through a real pipeline surveying case study. Numerous pipeline surveying cases, covering different pipeline lengths, pipe conditions, and surveying times, are included to achieve a practical conclusion with respect to the surveying accuracy. The results demonstrated that the maximum measurement repeatability errors of PIGs based on MEMS IMU and odometer do not exceed 0.25% and 0.1% of the pipeline length in the horizontal and vertical directions, respectively. These results not only provide powerful support for the PIG developers but also provide the expected PIG accuracy in urban underground pipeline surveying by using low-cost IMUs.

Evaluation of Experimental GNSS and 10-DOF MEMS IMU Measurements for Train Positioning

Integrating new candidate sensors, such as Global Navigation Satellite System (GNSS) and inertial measurement unit (IMU), into fail-safe train positioning systems have recently become a prominent area of research. Although there are a number of contributions related to the design of data fusion algorithms, the lack of details in raw measurements analysis has directly motivated this paper. This paper aims to record data from a variety of sensors (such as GNSS, IMU, magnetometer, barometer, tachometers, and Doppler radars) to evaluate train velocity and railway features (track slope, curve cant, and radius) extending previous works in the instrumentation and measurement field. The field test designed and concisely described in this paper presents several challenging environments, such as a tunnel, which can be used to analyze the candidate sensors limitations. In addition, a demonstration of a data fusion algorithm is presented to calculate train velocity based on measurements from the candidate sensors. The results obtained by an extended Kalman filter using GNSS and IMU are compared with velocity recorded by tachometers and Doppler radars, which is considered to be the reference value. The calculated velocity by IMU and GNSS when both sensors measurements are available presents an absolute error in velocity lower than 2 km/h in more than 90% of test duration. Finally, railway features (curve radius, cant, and slope) are calculated and analyzed according to train and railway dynamics.

Performance Analysis of a Deep Simple Recurrent Unit Recurrent Neural Network (SRU-RNN) in MEMS Gyroscope De-Noising.

Microelectromechanical System (MEMS) Inertial Measurement Unit (IMU) is popular in the community for constructing a navigation system, due to its small size and low power consumption. However, limited by the manufacturing technology, MEMS IMU experiences more complicated noises and errors. Thus, noise modeling and suppression is important for improving accuracy of the navigation system based on MEMS IMU. Motivated by this problem, in this paper, a deep learning method was introduced to MEMS gyroscope de-noising. Specifically, a recently popular Recurrent Neural Networks (RNN) variant Simple Recurrent Unit (SRU-RNN) was employed in MEMS gyroscope raw signals de-noising. A MEMS IMU MSI3200 from MT Microsystem Company was employed in the experiments for evaluating the proposed method. Following two problems were furtherly discussed and investigated: (1) the employed SRU with different training data length were compared to explore whether there was trade-off between the training data length and prediction performance; (2) Allan Variance was the most popular MEMS gyroscope analyzing method, and five basic parameters were employed to describe the performance of different grade MEMS gyroscope; among them, quantization noise, angle random walk, and bias instability were the major factors influencing the MEMS gyroscope accuracy, the compensation results of the three parameters for gyroscope were presented and compared. The results supported the following conclusions: (1) considering the computation brought from training dataset, the values of 500, 3000, and 3000 were individually sufficient for the three-axis gyroscopes to obtain a reliable and stable prediction performance; (2) among the parameters, the quantization noise, angle random walk, and bias instability performed 0.6%, 6.8%, and 12.5% improvement for X-axis gyroscope, 60.5%, 17.3%, and 34.1% improvement for Y-axis gyroscope, 11.3%, 22.7%, and 35.7% improvement for Z-axis gyroscope, and the corresponding attitude errors decreased by 19.2%, 82.1%, and 69.4%. The results surely demonstrated the effectiveness of the employed SRU in this application.

A MEMS-IMU Assisted BDS Triple-Frequency Ambiguity Resolution Method in Complex Environments

Emerging technologies such as smart cities and unmanned vehicles all need Global Navigation Satellite Systems (GNSS) to provide high-precision positioning and navigation services. Fast and reliable carrier phase ambiguity resolution (AR) is a prerequisite for high-precision positioning. The poor satellite geometry and severe multipath effect caused by Beidou Navigation Satellite System (BDS) signal occlusion and reflection in complex environments will degrade the AR performance. In this contribution, a fast triple-frequency AR method combining Microelectromechanical System-Inertial Measurement Unit (MEMS-IMU) and BDS is proposed. First, the Extra-Wide Lane (EWL) ambiguity is fixed with the positioning parameters of MEMS-IMU instead of the pseudorange. Then, the phase noise variance of Narrow Lane (NL) observation is obtained from ambiguity-fixed EWL observation to reduce the total noise level of NL observation, and the NL ambiguity can be reliably fixed, and the BDS positioning result is obtained. Finally, the BDS positioning result is used as the posterior measurement of the extended Kalman filter to update the MEMS-IMU positioning parameters to form the coupling loop of MEMS-IMU and BDS. The data of urban road vehicle experiments were collected to verify the feasibility and effectiveness of the proposed algorithm. Results show that MEMS-IMU can speed up AR, and reduction of total noise level can significantly improve the reliability of AR.

Mems Technology Quality Requirements as Applied to Multibeam Echosounder

Abstract Small, lightweight, power-efficient and low-cost microelectromechanical system (MEMS) inertial sensors and microcontrollers available in the market today help reduce the instability of Multibeam Sonars. Current MEMS inertial measurement units (IMUs) come in many shapes, sizes, and costs - depending on the application and performance required. Although MEMS inertial sensors offer affordable and appropriately scaled units, they are not currently capable of meeting all requirements for accurate and precise attitudes, due to their inherent measurement noise. The article presents the comparison of different MEMS technologies and their parameters regarding to the main application, namely Multibeam Echo Sounders (MBES). The quality of MEMS parameters is crucial for further MBES record-processing. The article presents the results of undertaken researches in that area, and these results are relatively positive for low-cost MEMS. The paper undertakes some vital aspect of using MEMS in the attitude and heading reference system (AHRS) context. The article presents a few aspects of MEMS gyro errors and their estimation process in the context of INS processing flow, as well as points out the main difficulties behind the INS when using a few top MEMS technologies.

GNSS/MEMS IMU ultra‐tightly integrated navigation system based on dual‐loop NCO control method and cascaded channel filters

A coherent scheme of federated architecture for the ultra-tight integration of global navigation satellite system (GNSS) and MEMS (micro-electro-mechanical system) inertial measurement unit (IMU) is presented. Inside the receiver baseband, both the controls for code numerically controlled oscillator (NCO) and carrier NCO are designed. They are each composed of two parts, one part is derived from the navigation solutions of outer loop, and the other part is given on the basis of the channel pre-filter outputs from inner loop. Each tacking channel has two filters of cascaded structure, one is the traditional pre-filter which is used to achieve GNSS signal tracking errors, and the second one is an extractor which is constructed to extract navigation solution derivation errors from the designed NCO controls and the signal tracking errors. For the ultra-tight integration navigation filter, the receiver clock residual error model after compensating the receiver clock drift is derived in detail. Finally, semi-physical simulation environment of the proposed ultra-tight integration scheme is built and closed-loop experiment under high dynamics scenario is put forward. The simulation results show that the proposed integration scheme can stably and robustly track the GNSS signals, and can achieve accurate navigation solutions even in harsh cases.