MEMS Inertial Measurement Unit Research Articles

다중 IMU 센서와 연합형 필터 융합 기반의 GPS-free 위치 추정

Localization techniques based on the integration of MEMSs(Micro Electro Mechanical Systems) and GPS(Global Positioning System) have been widely used in various fields, including aerospace, biomedical, automotive, and robotics, among others. However, it is well known that GPS signals are weakened or blocked in tunnels, urban canyons, or indoor environments. A solution is to use multiple IMU(Inertial Measurement Unit) sensors. In this paper, we present a federated fusion algorithm for GPS-free localization using multiple MEMS IMUs. The federated filter uses a master fusion algorithm to combine the outputs of independent sensors so that the current position can be estimated. The proposed structure exploits a quaternion-based extended Kalman filter and a linear Kalman filter for attitude estimations and velocity estimations, respectively. The effectiveness of the proposed algorithm was demonstrated with MATLAB simulation.

Design and Configuration of Folded Platonic Strapdowns of Biaxial MEMS Accelerometers

Abstract The authors report on the design, configure, and test of isotropic accelerometer strapdowns for high-precision inertial measurement unit (IMU) and folded MEMS configuration. The biaxial low-g MEMS accelerometers are based on the Platonic solids. A Platonic strapdown is integrated into an embedded system for acceleration-array signal acquisition targeting rigid-body pose-and-twist estimation. The electromechanical properties for dynamic sensitivity are tested on a haptic manipulator, which shows that the position estimation matches reasonably well the encoder readouts. The Platonic strapdown is promising in folded MEMS IMU with chip-level miniaturization and high estimation precision.

Foot-Mounted Pedestrian Navigation Method by Comparing ADR and Modified ZUPT Based on MEMS IMU Array.

Using an MEMS Inertial Measurement Unit (MEMS IMU) array mounted on foot is a feasible approach to improve the pedestrian tracking accuracy for the pedestrian navigation system (PNS). Based on the in-house developed IMU array, the paper proposes a new integrated framework that combines adaptive deck reckoning (ADR) with the modified zero velocity update (ZUPT). In the proposed ADR algorithm, the IMUs with large drift errors on the array are selected and removed according to the step length and the track angle computed by each IMU on the array. Then, by using the step length and the track angle of each step computed by remaining IMUs, the foot position extracted from the traditional ZUPT model is estimated on the basis of least squares (LS) so as to improve the traveled distance calculation accuracy. Compared with the traditional IMU array fusion method based on a maximum likelihood estimator (MLE) when it is used in the PNS, which is approximately taking the mean value of array readings, the proposed method is equivalent to adaptively fusing the array readings and thus improves the pedestrian tracking accuracy. To compare the proposed method with MLE, two different types of walking tracks are designed. The 161 m straight line experiments show that the end position by ADR/modified ZUPT method is much closer to the one of the reference trajectory compared with the MLE in repeated walks, and the closed-loop tracks about 300 m show that the positioning error with respect to the total traveled distance is less than 0.6% (1σ), which is higher than 1% (1σ) of MLE.

Application of Adaptive Cubature Kalman Filter to In-Pipe Survey System for 3D Small-Diameter Pipeline Mapping

A modified cubature Kalman filter (CKF) algorithm is proposed for an in-pipe survey system. This survey system can provide accurate three-dimensional (3D) location information for underground pipelines without external auxiliary location measurements. To move through a small-diameter pipe, the large size of the tactical grade inertial sensor is replaced with a low-cost micro-electromechanical system (MEMS) inertial measurement unit (IMU). Because the decrease in sensor accuracy increases the positioning error, it is necessary to improve the original filtering algorithm. The following improvements were made in relation to previous studies: geomagnetic and gravity sensors were introduced to obtain the observed vectors of the filter. The CKF was used to solve the nonlinearity of the filter's attitude errors. Furthermore, the CKF process noise matrix can be adaptively adjusted using the raw gyroscope measurements (ACKF) to maintain filter stability. Finally, the experimental results for the extended Kalman filter (EKF), unscented Kalman filter (UKF), CKF, and ACKF were compared to verify the effectiveness of ACKF.

Attitude dynamics aiding for three-dimensional passive target tracking of strap-down seeker based on instrumental variable Kalman filter

The accuracy of three-dimensional (3D) passive target tracking under strap-down system is usually affected by the measurement accuracy of attitude angular rate and attitude angle. In order to save the problem, a novel 3D passive target tracking method based on instrumental variable Kalman filter (IVKF) aided by the attitude dynamic is proposed. At first, the maneuvering target motion model is established based on the “current” statistical model and the filtering equation of MEMS inertial measurement unit (IMU) is also set up. Then, linearize the nonlinear state equations and replace the nonlinear measurement equations with pseudolinear equations. The 3D pseudolinear Kalman filter (PLKF) algorithm is derived according to the linear Kalman filter (KF). To counter the severe bias problems with PLKF, bias compensation and recursive instrumental variable (IV) methods are considered. In order to enhance observability of the system, a 3D motion tracking sliding-mode guidance law is deduced. Finally, some mathematical simulations were made to verify the effectiveness of the proposed method. The simulation results show the effect of the measurement accuracy and complexity of the algorithm are reduced, which proves the validity of the method.

Angular velocity fusion of the microelectromechanical system inertial measurement unit array based on extended Kalman filter with correlated system noises

The paper proposes an angular velocity fusion method of the microelectromechanical system inertial measurement unit array based on the extended Kalman filter with correlated system noises. In the proposed method, an adaptive model of the angular velocity is built according to the motion characteristics of the vehicles and it is regarded as the state equation to estimate the angular velocity. The signal model of gyroscopes and accelerometers in the microelectromechanical system inertial measurement unit array is used as the measurement equation to fuse and estimate the angular velocity. Due to the correlation of the state and measurement noises in the presented fusion model, the traditional extended Kalman filter equations are optimized, so as to accurately and reliably estimate the angular velocity. By simulating angular rates in different motion modes, such as constant and change-in-time angular rates, it is verified that the proposed method can reliably estimate angular rates, even when the angular rate has been out of the microelectromechanical system gyroscope measurement range. And results show that, compared with the traditional angular rate fusion method of microelectromechanical system inertial measurement unit array, it can estimate angular rates more accurately. Moreover, in the kinematic vehicle experiments, the performance advantage of the proposed method is also verified and the angular rate estimation accuracy can be increased by about 1.5 times compared to the traditional method.

An inertial measurement unit tracking system for body movement in comparison with optical tracking.

The recent advancement of motion tracking technology offers better treatment tools for conditions, such as movement disorders, as the outcome of the rehabilitation could be quantitatively defined. The accurate and fast angular information output of the inertial measurement unit tracking systems enables the collection of accurate kinematic data for clinical assessment. This article presents a study of a low-cost microelectromechanical system inertial measurement unit-based tracking system in comparison with the conventional optical tracking system. The system consists of seven microelectromechanical system inertial measurement units, which could be mounted on the lower limbs of the subjects. For the feasibility test, 10 human participants were instructed to perform three different motions: walking, running, and fencing lunges when wearing specially designed sleeves. The subjects' lower body movements were tracked using our inertial measurement unit-based system and compared with the gold standard-the NDI Polaris Vega optical tracking system. The results of the angular comparison between the inertial measurement unit and the NDI Polaris Vega optical tracking system were as follows: the average cross-correlation value was 0.85, the mean difference of joint angles was 2.00°, and the standard deviation of joint angles was ± 2.65°. The developed microelectromechanical system-based tracking system provides an alternative low-cost solution to track joint movement. Moreover, it is able to operate on an Android platform and could potentially be used to assist outdoor or home-based rehabilitation.

Velocity-Based Optimization-Based Alignment (VBOBA) of Low-End MEMS IMU/GNSS for Low Dynamic Applications

To solve the low accuracy and poor reliability of the alignment of low-end micro-electro-mechanical systems (MEMS) inertial measurement units (IMU) in low dynamic applications such as precision agriculture, this paper proposes a velocity-based optimization-based alignment (VBOBA) method using the assistance of GNSS velocity. Essentially, the proposed VBOBA method is a kind of attitude determination algorithm based on multiple velocity vector observations. Furthermore, the global perspective of observability analysis is utilized to investigate the influence of vehicle maneuvers on the estimation of the heading angle, which reveals that the proposed VBOBA method only requires velocity vectors, while the existing acceleration-based optimization-based alignment (ABOBA) method requires acceleration changes to estimate the heading angle. The results of simulation and tractor tests show that the alignment error of the heading angle provided by the proposed VBOBA method can be reduced to 4° in 60s under low dynamic conditions with about 1 m/s for the industrial grade MEMS IMU (e.g., ADIS16460). The proposed VBOBA method can serve as an effective in-motion alignment for low-end and low dynamic applications.

Data Fusion Method of Measurement Lag Compensation for Multirate MIMU/FOG/GNSS Compound Navigation

In the future, smart intelligent ammunition will be widely applied in the military field. Small volume microelectromechanical system inertial measurement units (MIMUs) have become the mainstream solution for a smart intelligent ammunition navigation system; however, the accuracy of the IMU is the main factor limiting the accuracy of the strikes. To improve the navigation accuracy of the MIMU, various integrated methods have been proposed, for example, the integration of a global navigation satellite system (GNSS) and a magnetometer. In this study, a compound navigation system (CPNS) is designed to improve the navigation accuracy of the MIMU. The CPNS consists of an oblique single-axis fiber optic gyroscope (FOG), a MIMU and a GNSS. The proposed method solves two problems: the calibration of the MEMS gyro and GPS measurement lag. With a FOG as the measurement, MEMS gyro errors are calibrated online by the designed Kalman filter. Thus, the accuracy of the CPNS-IMU can be improved. When GPS measurement lag occurs, the accuracy of the traditional Kalman filter will deteriorate. To reduce the data fusion error of the CPNS under measurement lag, a distributed optimal fusion estimation method is proposed based on the multi-scale theory. The hardware-in-the-loop simulation results demonstrate that the constant bias, scale factor errors, and misalignment errors of the MEMS gyro are effectively suppressed by configuring the FOG to the MIMU. The navigation error of the CPNS-IMU is reduced by more than 50% as compared with the navigation error of the MIMU. In the CPNS with the measurement lag, the fusion error is reduced by approximately 33% in the proposed method as compared with the existing methods.

Toward Calibration of Low-Precision MEMS IMU Using a Nonlinear Model and TUKF

MEMS-IMUs have an extensive application in multifarious studies, as well as industrial and commercial areas. It is crucial to diminish their intrinsic errors in a suitable calibration procedure. In this paper, a novel calibration procedure was proposed for Inertial Measurement Units (IMUs) on a turntable. A general nonlinear model of the IMU output including the effects of bias, scale factor, misalignment, and lever arm was derived. Transformed Unscented Kalman Filter (TUKF) was utilized to perform the estimation of error parameters for gyroscopes and accelerometers. The calibration maneuvers were applied using a tri-axis turntable to create input signals. In addition, assuming the sensors not placing in the center of the table makes angular acceleration become another variable which affects the estimation of the error parameters. Therefore, a suitable angular acceleration estimator was designed utilizing the angular velocity output. According to experimental results, applying the proposed method caused 66% and 63% increase in the accuracy of gyroscopes and accelerometers outputs, respectively, compared with the calibrated signals based on the least square method.

Development the method of pipeline bending strain measurement based on microelectromechanical systems inertial measurement unit.

With the development of pipeline construction, the additional stress and strain becomes the key factor to induce the damage for oil and gas pipeline. The in-line inspection of pipeline bending strain which is based on high-end tactical-grade inertial measurement unit has become routine practice for the oil and gas pipelines over recent years. However, these accurate inertial measurement units are large size and high cost limit to use in small diameter pipelines of bending strain inspection. Microelectromechanical systems-based inertial navigation has been applied to mapping the centerline of the small size pipeline, and the accurate trajectory and attitude information become key factors to calculate the bending strain of pipelines. This article proposed a method not only to calculate the pipeline bending strain but also to improve the accuracy for the bending strain based on the wavelet analysis. Tests show that this method can be effectively used in the calculation and optimization of the bending strain, and it will increase the accuracy to within 19.1% of the actual bending strain.

Correlating the passive response of eye and brain to head impact using MEMS IMUs on 3D-printed human head phantom

Objective:The eye may act as a surrogate for the brain in response to head acceleration during an impact. This paper reports the latest demonstration of the correlation between the passive human eye accelerations and the brain accelerations under head impacts using a model eye/brain/skull phantom.Approach:Experiments investigating rotational and linear accelerations were performed on a 3D-printed human head phantom, along with rotational experiments on a human volunteer. Data acquired during the passive eye response (PER) periods were processed and analysed using statistical methods including one-way normal analysis of variance, linear regression fit, and Pearson R correlation.Main results:The results have shown that, at least on the 3D-printed human head phantom, strong correlations can be found between the eye and the brain during the PER, which indicates a potential use of microelectromechanical systems inertial measurement units in real-time on-field monitoring and diagnosis of concussions or traumatic brain injuries.Significance:These discoveries pave the way for potential non-invasive wearable devices based on this technology to be applied in real-time on-field concussion monitoring, which is expected to provide vulnerable parties with instant alert thus timely treatment.

Adaptive Attitude Estimation for Low-Cost MEMS IMU Using Ellipsoidal Method

In this article, the attitude estimation for low-cost MEMS inertial measurement units in a smartphone is proposed using adaptive ellipsoidal methods. Accelerometer and magnetometer measurements in an attitude heading reference system (AHRS) are ideally applicable when there is no acceleration and magnetic disturbance. However, when the AHRS is applied to the smartphone, acceleration is frequently occurred by hand movement, and it causes significant attitude errors. Besides, magnetic disturbance from the surrounding environment degrades the heading estimation. In order to improve the attitude accuracy, the acceleration from movement, in addition to the magnetic disturbance is considered in adaptive logic using measurement residuals in this article. The performance of the proposed algorithm is simulated by the computer and tested using the rate table and optical motion capture system compared with the conventional algorithms.

Foot-Mounted Pedestrian Navigation Method Based on Gait Classification for Three-Dimensional Positioning

A foot-mounted pedestrian navigation system (PNS) that uses MEMS inertial measurement units (IMUs) can track the position of a person when walking normally on two-dimensional (2D) plane. However, the positioning accuracy is decreased under complex gaits. This article proposes a pedestrian navigation method based on gait classification for three-dimensional (3D) positioning. The commonly used gaits are divided into seven types and a bidirectional long short-term memory recurrent neural network (BLSTM-RNN) is constructed as gait classifier. When the detected gaits are walking sideways or running, the step length models are built to calculate single step length. Otherwise the inertial navigation algorithm with zero velocity update (ZUPT) is used to calculate travel distance. If the pedestrian is going upstairs or downstairs, a barometer together with stair height constraint is applied to obtain altitude variation. Meanwhile, the heading drift is reduced combining zero integrated heading rate (ZIHR) method and simplified heuristic drift reduction (HDR) method. The gait classification results show that the classification accuracy of designed BLSTM-RNN reaches 99.4%. The results of mixed gaits experiment and multi-floor experiment show that the positioning errors of 2D and 3D trajectories are less than 2%.

A Model-Based Approach for Measurement Noise Estimation and Compensation in Feedback Control Systems

This article considers the problem of measurement noise rejection in a linear output-feedback control system. Specifically, we take into account not only the rejection of high-frequency stochastic noises but also the compensation for low-frequency measurement errors, such as bias and drift, which cannot be well-handled by the classic frequency-domain filters or Kalman filters. A novel noise estimator (NE)-based robust control solution is proposed. The NE is designed in the frequency domain by exploiting the system model and control structure information and is embedded into the controller instead of being an independent functional module in the closed-loop system. The adverse effects of model uncertainties on the performance of the NE-based solution are investigated, and an improved solution is proposed by incorporating a simple low-pass filter as the prefilter of NE. This solution is applied to the angle tracking problem of a 2-DOF experimental helicopter platform equipped with a low-cost and low-accuracy microelectromechanical system (MEMS) inertial measurement unit (IMU) (MEMS IMU) for angular position/rate measurements. Both numerical simulation and experimental comparisons with other existing approaches demonstrate: 1) constant bias and time-varying drift in the IMU measurements are systematically addressed by the solution; 2) it is accessible to improve the steady-state tracking accuracy by tuning the parameter of NE to extend its bandwidth; and 3) when model uncertainties limit the feasible bandwidth of NE, the improved solution is able to largely maintain its noise rejection performance.

Autonomous Pedestrian Altitude Estimation Inside a Multi-Story Building Assisted by Motion Recognition

Owing to autonomy and continuity, a pedestrian navigation system (PNS) has been widely deployed, which is based on the micro electro-mechanical system inertial measurement unit (MEMS-IMU) and the strapdown inertial navigation system (SINS). However, altitude information cannot be effectively obtained in this system by the double integral of vertical acceleration because of the altitude channel divergent of SINS. The study is aimed at improving the accuracy and robustness of altitude estimation through a novel method based on foot-mounted MEMS-IMU. More specifically, the proposed method exploits the adaptive network-based fuzzy inference system (ANFIS) to recognize vertical motion modes including horizontal, downstairs, and upstairs movements. Then, the pseudo height model based on both motion modes and the stair height is constructed for stair walking with different height. Finally, the pseudo measurements from the pseudo height model are integrated with the data from motion prediction through the extended Kalman filter (EKF). Experimental results show that the overall classification accuracy of ANFIS can reach up to 99.1%. Since ANFIS is utilized to assist height estimation, the cumulative height error accounts for about 1.2% over a total height of 44 m when a pedestrian walks up and down six floors without external facility and barometric pressure support. It is concluded that the ANFIS-based height estimation method can achieve better vertical positioning performance for PNS than the existing approaches in terms of accuracy and robustness.

Artificial Marker and MEMS IMU-Based Pose Estimation Method to Meet Multirotor UAV Landing Requirements.

The fully autonomous operation of multirotor unmanned air vehicles (UAVs) in many applications requires support of precision landing. Onboard camera and fiducial marker have been widely used for this critical phase due to its low cost and high effectiveness. This paper proposes a six-degrees-of-freedom (DoF) pose estimation solution for UAV landing based on an artificial marker and a micro-electromechanical system (MEMS) inertial measurement unit (IMU). The position and orientation of the landing maker are measured in advance. The absolute position and heading of the UAV are estimated by detecting the marker and extracting corner points with the onboard monocular camera. To achieve continuous and reliable positioning when the marker is occasionally shadowed, IMU data is fused by an extended Kalman filter (EKF). The error terms of the IMU sensor are modeled and estimated. Field experiments show that the positioning accuracy of the proposed system is at centimeter level, and the heading error is less than 0.1 degrees. Comparing to the marker-based approach, the roll and pitch angle errors decreased by 33% and 54% on average. Within five seconds of vision outage, the average drifts of the horizontal and vertical position were 0.41 and 0.09 m, respectively.

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.