Micro Inertial Measurement Unit Research Articles

Improved Pedestrian Location Method for the Indoor Environment Based on MIMU and sEMG Sensors

In order to guarantee the pedestrian location accuracy for the indoor environment, an improved pedestrian location method based on the improved Mahony complementary filter algorithm is proposed by the data fusion of microinertial measurement unit (MIMU) and sEMG sensors, which are installed on the lower limbs. First, the pedestrian location method for the indoor environment is introduced by the data fusion of MIMU and surface electromyography; and then, BP neural network is applied to adjust the parameters in real time for PI controller of Mahony complementary filter algorithm, which can obtain the current heading angle for the location. Finally, the indoor environment experiments for pedestrian location are carried out. The heading angle estimating experiment result shows that the indoor heading angle for the pedestrian can be exactly calculated by the improved Mahony complementary filter algorithm. The indoor location experiment shows that the average relative location error is 2.34 m, and the average relative location error rate is 2.27%.

MIMU Optimal Redundant Structure and Signal Fusion Algorithm Based on a Non-Orthogonal MEMS Inertial Sensor Array.

A micro-inertial measurement unit (MIMU) is usually used to sense the angular rate and acceleration of the flight carrier. In this study, multiple MEMS gyroscopes were used to form a spatial non-orthogonal array to construct a redundant MIMU system, and an optimal Kalman filter (KF) algorithm was established by a steady-state KF gain to combine array signals to improve the MIMU's accuracy. The noise correlation was used to optimize the geometric layout of the non-orthogonal array and reveal the mechanisms of influence of correlation and geometric layout on MIMU's performance improvement. Additionally, two different conical configuration structures of a non-orthogonal array for 4,5,6,8-gyro were designed and analyzed. Finally, a redundant 4-MIMU system was designed to verify the proposed structure and KF algorithm. The results demonstrate that the input signal rate can be accurately estimated and that the gyro's error can also be effectively reduced through fusion of non-orthogonal array. The results for the 4-MIMU system illustrate that the gyro's ARW and RRW noise can be decreased by factors of about 3.5 and 2.5, respectively. In particular, the estimated errors (1σ) on the axes of Xb, Yb and Zb were 4.9, 4.6 and 2.9 times lower than that of the single gyroscope.

A Single Smart Ring for Monitoring 20 Kinds of Multi‐Intensity Daily Activities––From Kitchen Work to Fierce Exercises

Statistical analysis of human daily activities contributes to time planning and health management. It also helps people make healthcare judgments and disease predictions when combined with big data technology. However, traditional methods for recognition of human daily activities based on vision and multisensors have limitations due to poor portability and weak capability in multiple activities detection. Herein, the development of a wearable human activity recognition (HAR) smart ring capable of recognizing at least 20 multi‐intensity activities, i.e., motions ranging from clean‐the‐desk to playing basketball, based on a microinertial measurement unit and a hierarchical decision algorithm is proposed. Users only need to wear the smart ring on the index finger of the right hand to accurately identify 20 common activities that are classified as light, vigorous, and fierce. A novel hierarchical decision algorithm using 70 features is proposed to improve the accuracy and speed of recognizing common human activities and provides a final recognition accuracy of 98.10% for 20 types of activities. This extremely portable, reliable, and high‐accuracy HAR solution is a significant advancement in providing real‐time quantitative data for personal lifestyle supervision, time planning, and healthcare management.

Wireless AI-Powered IoT Sensors for Laboratory Mice Behavior Recognition

More than 100 million animals are used in research, education, and testing per year, and 95% of them are mice and rats. We have developed wireless artificial intelligent (AI)-powered Internet of Things (IoT) sensors (AIIS) for laboratory mice motion recognition utilizing embedded microinertial measurement units (uIMUs)—a new sensing platform fills an important research gap of monitoring behaviors of many laboratory mice in parallel. We have demonstrated a wireless IoT sensor that could be attached and carried by mice (i.e., animals that typically weigh only 20 g and with body length of ~10 cm) and used the collected motion data to recognize five common mice behaviors (e.g., sleeping, walking, rearing, digging, and shaking) in cages with an accuracy of ~76%. For comparison, current commercial video-based tracking systems that track animal behaviors in real time can reach only 70% accuracy and with limited number of parallelly tracked animals. Furthermore, several machine learning algorithms were explored to solve the imbalanced sample data problem, which allowed the accuracy of mice motion recognition to improve from ~48% to ~76% (if shaking is removed from classification, an average accuracy of 86.46% could be achieved). Less frequent mice behaviors, such as rearing, digging, grooming, drinking, and scratching, could also be recognized at an average accuracy of 96.35%. We believe this work has the potential to revolutionize animal behavioral tracking methodology by offering a solution for large batches of simultaneous small animal motion tracking and AI-based behavior recognition.

A Lie Group Manifold-Based Nonlinear Estimation Algorithm and Its Application to Low-Accuracy SINS/GNSS Integrated Navigation

In this article, in order to improve the performance of the micro inertial measurement unit (MIMU) based on low-accuracy navigation system under the condition of initial large misalignment angle, a nonlinear strapdown inertial navigation system (SINS)/global navigation satellite system (GNSS) integrated navigation estimation algorithm based on the Lie group manifold space is proposed. The proposed nonlinear algorithm is based on unscented Kalman filter (UKF), and its core is to realize the propagation of Lie algebra state error variable sigma points between Lie group space and Lie algebra space through the retraction operation and inverse retraction operation. Meanwhile, the sensor bias state error variable sigma points are always propagated through linear operations in the Euclidean space. Finally, the covariance of the two forms of state error variable is calculated in the Euclidean space, from which the lie algebra state error variable and sensor bias state error variable are estimated through the measurement updating process. The simulation and experimental results show that the proposed algorithm has higher accuracy and faster convergence speed compared with the existing state-of-the-art integrated navigation algorithms, and it has good estimation consistency.

Time–Frequency Extraction Model Based on Variational Mode Decomposition and Hilbert–Huang Transform for Offshore Oil Platforms Using MIMU Data

Time–frequency extraction is a key issue to understand structural symmetry of dynamic responses of offshore oil platforms for early warning during drilling operations. Current popular methods for signal characteristics extraction can only obtain the attributes with a single dimension or poor precision. To solve this, a combined Hilbert–Huang transform (HHT) and variational mode decomposition (VMD) method is proposed to extract multidimensional dynamic response characteristics of time, frequency, and energy of offshore oil platforms. Based on the extracted time–frequency–energy information, the frequency-domain integration approach (FDIA) can be applied to calculate the displacement using accelerometer in the micro inertial measurement unit (MIMU). A complementary filtering algorithm was designed to measure the torsion angle of platforms using six degrees of freedom data from the MIMU to obtain the torsion angle information. The performance of the proposed method was validated using a series of simulation shaking-table tests and a field test conducted on an offshore oil platform at Dongying City, Shandong Province, China. During the field test, seven out of eight collisions were detected in the frequency range 5 Hz to 12 Hz. The intensity of the fifth collision was the highest, and the maximum displacement obtained by the accelerometer was 6 mm. In addition, the results show a correlation between the axes of the accelerometer and gyroscope, and their combination can measure a torsion angle up to 1.1°.

Low-cost MIMU based AMS of highly dynamic fixed-wing UAV by maneuvering acceleration compensation and AMCF

Attitude Measurement System (AMS) that constructed by the low-cost Micro-Inertial Measurement Unit (MIMU) is usually adopted as the backup equipment for the fixed-wing Unmanned Aerial Vehicle (UAV) to enhance its safety performance during flight. However, both the large-maneuvering acceleration and the high-frequency propeller dithering are major reasons that decreasing its real-time measurement precision. In addition, there are only tri-axial gyroscope and tri-axial accelerometer measurements could be utilized to compensate the measurement errors in the autonomous MIMU based AMS. In this paper, an Adaptive Mahony Complementary Filter (AMCF) with dynamic tuning of the complementary filter Proportion and Integral (PI) parameters is used to estimate the real-time attitude of the highly dynamic aviation UAV with low-cost MIMU in high-frequency propeller dithering. Meanwhile, the maneuvering acceleration compensation technology is also integrated with the AMCF to eliminate the UAV highly dynamic maneuvering acceleration. Moreover, the attitude of AMS is updated by the quaternion updating algorithm to improve its real-time performance and reliability. Finally, both UAV ground high-speed taxiing experiment and UAV flight experiment are conducted to verify the measurement accuracy of low-cost AMS. The real-time flight experimental results demonstrated that the Rooted Mean Square Error (RMSE) of AMS based on the low-cost MIMU do not exceed 0.882° in terms of pitch angle and 0.864° in roll angle under various maneuvering conditions. These results provide powerful supports for both the UAV developers and the low-cost MIMU based AMS.

High-speed train positioning method based on motion constraints suppressing INS error in tunnel

When the high-speed train is running in tunnel, the global navigation satellite system (GNSS) signal is completely lost, and only relying on the inertial navigation system (INS) composed of micro electro mechanical system (MEMS) devices makes large navigation error. To solve this problem, a method considering the installation angles of micro inertial measurement unit (MIMU) relative to the train body for motion constraints aided inertial navigation system (CIAMC-INS) is proposed, which does not need extra sensors. This method first establishes motion constraints model based on the installation angles of MIMU; secondly, the effect of turning on the motion constraints model is analyzed, and the use condition of motion constraints is obtained; then, the installation angles of MIMU are estimated when GNSS signal is good and the use condition of motion constraints is met; finally, the estimated installation angles are applied to the motion constraints to suppress the error of pure inertial navigation system (P-INS) to improve the navigation accuracy in tunnel. Based on this method, high-speed train navigation tests are carried out both in the real tunnel environment and in the case of artificially disconnected GNSS signal. The experimental results show that the navigation accuracy of the train in the tunnel is significantly improved, which verifies the effectiveness of the method.

Integrated Bionic Polarized Vision/VINS for Goal-Directed Navigation and Homing in Unmanned Ground Vehicle

In this paper we present a bionic multi-sensor anavigation and control system for unmanned ground vehicles to complete the homing task. The system consists of the pixelated polarized sensor, a Micro Inertial Measurement Unit (MIMU), and a monocular camera. To compensate for the installation error, we provide a joint calibration method for multiple sensors. Utilizing the measurements of pixelated polarized vision sensor, we firstly propose an adaptive integrated method with the Visual-Inertial System. The integrated algorithm can not only solve the ambiguity problem of polarized orientation and reduce the cumulative error of the system, but also increase the navigation output rate and enhance the robustness of the system. We present a homevector-based strategy for the goal-directed navigation and control of the unmanned ground vehicles. When the external data link is interrupted, the unmanned vehicles can return to the starting point autonomously, which benefits for improving the survivability of the system. Finally, we design various experiments to verify the algorithm proposed in this paper. The experimental results of the calibration demonstrate that the RMSE of the orientation after calibration is only 0.014°. In the navigation and homing experiment, the RMSE of the position error is 0.64m, and the minimum homing error is only 0.49m (1.09% of the travelled distance). Finally, we discuss interesting insights gained with respect to future work in multi-sensor integration and robot control strategies.

High-Speed Train Tunnel Navigation Method Based on Integrated MIMU/ODO/MC Navigation

When a high-speed train is running in a tunnel, the global navigation satellite system (GNSS) signal is completely lost. Relying only on the inertial navigation system (INS) composed of Micro-electromechanical Systems (MEMS) devices leads to large navigation errors. To solve this problem, an integrated micro inertial measurement unit (MIMU), odometer (ODO), and motion constraint (MC) tunnel navigation method is proposed. This method first establishes a motion constraint model based on the installation angles of MIMU; secondly, the effect of turning on the motion constraint model and the odometer is analyzed and the use condition of the motion constraints is obtained; the installation angles of MIMU are then estimated when GNSS signal is good and the use condition of the motion constraints is met; finally, the forward speed measured by the odometer and the motion constraints are applied to suppress the error of the INS and improve the navigation accuracy in the tunnel. Based on this method, high-speed train navigation tests were carried out both in areal tunnel environment and in a case study with an artificially disconnected GNSS signal. The experimental results showed that the navigation accuracy of the train in the tunnel was significantly improved. Seamless navigation was achieved inside and outside the tunnel, which verified the effectiveness of the method.

The Improved 3D Pedestrian Positioning System Based on Foot-Mounted Inertial Sensor

The foot-mounted navigation system based on Micro Inertial Measurement Unit (MIMU) can estimate the position of pedestrians. Zero Velocity Update (ZUPT) provides a solution for reducing cumulative positioning errors caused by the integral calculation of the inertial sensor. However, the low accuracy and high noise of the MIMU will greatly affect the measurement accuracy of zero velocity detector, which will lead to poor 3D pedestrian positioning accuracy. An improved generalized likelihood ratio test (GLRT) method is proposed by establishing adaptive thresholds at different gait frequency. The core is to dynamically adjust the detection threshold according to different motion states, so as to determine the constraint relation for detection. In addition, the drift of sensors and the accumulated error of position integration affect the accuracy of attitude and position. Therefore, an improved extended Kalman particle filter (EKPF) is proposed. The improved EKPF constructs the position constraint model in altitude direction by introducing barometer information. The new particle filter distribution function is constructed by constrained multi-source observation information, which makes the posterior probability distribution closer to the real distribution. In order to verify the feasibility and effectiveness of this method, the inertial pedestrian navigation system is established. The experimental results demonstrate that the improved GLRT method performs well in detecting the zero velocity point of the foot, and improved EKPF method reduces the positioning error by 69.25% and 53.57% compared with the EKF and PF, respectively. Therefore, the proposed method can improved the 3D pedestrian positioning accuracy.

New solution to the motion acceleration disturbance in MEMS-based attitude and heading reference system

The attitude and heading reference system based on microinertial measurement unit is becoming increasingly important in the fields of vehicle navigation, autonomous underwater vehicles, and unmanned aircraft systems. However, motion acceleration disturbance always affects attitude fusion, thereby making the study of compensation method for attitude fusion in motion important. In this work, two independent extended Kalman filters with clear structure and easy control are used to calculate attitude fusion. The linear process of the measurement equations in this structure is avoided and is more intuitive than the Euler-angle or Quaternion method. Subsequently, this work describes the motion convergence model for suppressing motion acceleration disturbance. This model is designed on the basis of gyro compensation and motion statistical analysis. First, the concrete structure of the model is introduced, and the feasibility of the scheme is analyzed. Second, the influence of small gradient acceleration on the model is elucidated. Finally, vehicle experimental results show that the model can effectively reduce motion acceleration disturbance and reflect the real attitude changes inside the vehicle body.

A Comprehensive Review of Micro-Inertial Measurement Unit Based Intelligent PIG Multi-Sensor Fusion Technologies for Small-Diameter Pipeline Surveying.

It is of great importance for pipeline systems to be is efficient, cost-effective and safe during the transportation of the liquids and gases. However, underground pipelines often experience leaks due to corrosion, human destruction or theft, long-term Earth movement, natural disasters and so on. Leakage or explosion of the operating pipeline usually cause great economical loss, environmental pollution or even a threat to citizens, especially when these accidents occur in human-concentrated urban areas. Therefore, the surveying of the routed pipeline is of vital importance for the Pipeline Integrated Management (PIM). In this paper, a comprehensive review of the Micro-Inertial Measurement Unit (MIMU)-based intelligent Pipeline Inspection Gauge (PIG) multi-sensor fusion technologies for the transport of liquids and gases purposed for small-diameter pipeline (D < 30 cm) surveying is demonstrated. Firstly, four types of typical small-diameter intelligent PIGs and their corresponding pipeline-defects inspection technologies and defects-positioning technologies are investigated according to the various pipeline defects inspection and localization principles. Secondly, the multi-sensor fused pipeline surveying technologies are classified into two main categories, the non-inertial-based and the MIMU-based intelligent PIG surveying technology. Moreover, five schematic diagrams of the MIMU fused intelligent PIG fusion technology is also surveyed and analyzed with details. Thirdly, the potential research directions and challenges of the popular intelligent PIG surveying techniques by multi-sensor fusion system are further presented with details. Finally, the review is comprehensively concluded and demonstrated.

An Effective In-Field Calibration Method for Triaxial Magnetometers Based on Local Magnetic Inclination

This article presents an innovative in-field calibration method for portable triaxial magnetometers (TMs). For in-field calibration of portable sensors, it is difficult to provide accurately measured inputs to each sensitivity axis due to the unavailability of high-precision turntables. Because of this limitation, most existing in-field calibration methods often require nonlinear optimization to identify model parameters. According to the fact that the angle between the local gravity and magnetic field is invariant, a new in-field calibration approach called inclination-based calibration (I-Calibration) was proposed. The I-Calibration could reliably estimate model parameters of the magnetometers by simply using a linear least square estimator. A 12-observation icosahedron experimental scheme was performed for a microinertial measurement unit, which contains both triaxial accelerometers (TAs) and TMs. The effectiveness, as well as the robustness with the misalignments of the TAs and TMs, of the proposed calibration method, was first verified by using numerical simulations. Thereafter, in real data experiments, the proposed method was compared with an existing calibration approach, namely, magnitude-based calibration (M-Calibration), which is based on the fact that the magnitude of the local magnetic field is invariant. The calibrated results together with the comparison between the two methods demonstrated the effectiveness of the proposed method.

Design of a Projectile-Borne Data Recorder Triggered by Overload

The projectile-borne data recorder is used to measure and record the position and attitude data of exterior ballistic flights, which experience high overload during the launching process. The navigation algorithm can be optimized by analyzing the data stored in the recorder. As the primary means of acquiring the navigation data, the micro inertial measurement unit (MIMU) is an inevitable part of the projectile equipment. However, its mechanical structure could hardly bear the high overload during operation. In view of the above problems, a novel projectile-borne data recorder triggered by overload is designed in this paper. The recorder could have the navigation system powered when the projectile leaves the barrel so as to activate the data recording of the high overload. Furthermore, the viability of the MIMU of the high overload is guaranteed through a specific way of system encapsulation. In the proposed design, the overload switch redundancy and the power supply redundancy were adopted to improve reliability. The proposed design is tested with practical experiments, and the results show that the proposed recorder could be effectively triggered by overloading, and the supply voltage is stable, which helps record reliable data for the projectile.

Research on practical calibration compensation method of MIMU

The Micro-inertial measurement unit (MIMU) is affected by the processing, installation and environment during its working process, which will produce large errors, resulting in low measurement precision, therefore, error calibration compensation is necessary. In this paper, a practical calibration compensation method of MIMU based on the error model of gyroscope and accelerometer was studied. The practical method uses a three-axis multi-functional turntable, which can provide high-precision angular rate and spatial position benchmark, to design the angular rate and six-position calibration scheme, and computes twenty-four error parameters of the MIMU, including zero bias, scale factor and installation error coefficient. The analysis of the experimental result shows that the precision of MIMU after calibration compensation can be improved by 1-2 orders of magnitude, which proves the effectiveness of the calibration compensation method.

MIMU/Odometer Fusion with State Constraints for Vehicle Positioning during BeiDou Signal Outage: Testing and Results.

With the rapid development of autonomous vehicles, the demand for reliable positioning results is urgent. Currently, the ground vehicles heavily depend on the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) providing reliable and continuous navigation solutions. In dense urban areas, especially narrow streets with tall buildings, the GNSS signals are possibly blocked by the surrounding tall buildings, and under this condition, the geometry distribution of the in-view satellites is very poor, and the None-Line-Of-Sight (NLOS) and Multipath (MP) heavily affects the positioning accuracy. Further, the INS positioning errors will quickly diverge over time without the GNSS correction. Aiming at improving the position accuracy under signal challenging environment, in this paper, we developed an MIMU(Micro Inertial Measurement Unit)/Odometer integration system with vehicle state constraints (MO-C) for improving the vehicle positioning accuracy without GNSS. MIMU/Odometer integration model and the constrained measurements are given in detail. Several field tests were carried out for evaluating and assessing the MO-C system. The experiments were divided into two parts, firstly, field testing with data post-processing and real-time processing was carried out for fully assessing the performance of the MO-C system. Secondly, the MO-C was implemented in the BeiDou Satellite Navigation System (BDS)/integrated navigation system (INS) for evaluating the MO-C performance during the BDS signal outage. The MIMU standalone positioning accuracy was compared with that from the MIMU/Odometer integration (MO), MO-C and MIMU with constraints (M-C) for assessing the Odometer, and the influence of the constraint on the positioning errors reduction. The results showed that the latitude and longitude errors could be suppressed with Odometer assisting, and the height errors were suppressed while the state constraints were included.

Pedestrian Navigation Method Based on Machine Learning and Gait Feature Assistance.

In recent years, as the mechanical structure of humanoid robots increasingly resembles the human form, research on pedestrian navigation technology has become of great significance for the development of humanoid robot navigation systems. To solve the problem that the wearable inertial navigation system based on micro-inertial measurement units (MIMUs) installed on feet cannot effectively realize its positioning function when the body movement is too drastic to be measured correctly by commercial grade inertial sensors, a pedestrian navigation method based on construction of a virtual inertial measurement unit (VIMU) and gait feature assistance is proposed. The inertial data from different positions of pedestrians’ lower limbs are collected synchronously via actual IMUs as training samples. The nonlinear mapping relationship between inertial information from the human foot and leg is established by a visual geometry group-long short term memory (VGG-LSTM) neural network model, based on which the foot VIMU and virtual inertial navigation system (VINS) are constructed. The VINS experimental results show that, combined with zero-velocity update (ZUPT), the integrated method of error modification proposed in this paper can effectively reduce the accumulation of positioning errors in situations where the gait type exceeds the measurement range of the inertial sensors. The positioning performance of the proposed method is more accurate and stable in complex gait types than that merely using ZUPT.

A Wearable Flow-MIMU Device for Monitoring Human Dynamic Motion.

For personalized rehabilitation or sports training, it is necessary to monitor dynamic motions and track postures of human body and limbs. Traditional methods using micro inertial sensors usually suffer from drift problems in tracking dynamic motions. In this paper, a wearable flow-MIMU human motion capture device is proposed by incorporating micro flow sensor with micro inertial measurement unit (MIMU). Motion velocity is detected by a micro flow sensor and utilized to figure out the motion acceleration. The gravity accelerations are extracted by eliminating the motion accelerations from the accelerometer outputs. Finally, posture estimation is implemented by using a tailor-designed Kalman-based data fusion of the gyroscope outputs and the extracted gravity accelerations. The flow-MIMU device with wireless communication is designed like a watch to be wearable. Experimental results validate that the motion velocity, acceleration and posture of human limb are determined accurately and free of accumulative error in monitoring of dynamic motions using the proposed method. The wearable flow-MIMU device provides an advantageous monitoring approach for applications of personalized rehabilitation, sports training, intelligent prosthetics, etc.

Improved Pedestrian Positioning with Inertial Sensor Based on Adaptive Gradient Descent and Double-Constrained Extended Kalman Filter

The Foot-mounted Inertial Pedestrian-Positioning System (FIPPS) based on the Micro-Inertial Measurement Unit (MIMU) is a good choice for the forest fire fighters when the Global Navigation Satellite System is unavailable. Zero Velocity Update (ZUPT) provides a solution for reducing cumulative positioning errors caused by the integral calculation of the inertial navigation. However, the performance of ZUPT is highly affected by the low accuracy and high noise of the MIMU. The accuracy of conventional ZUPT for attitude alignment is reduced by the zero offset of acceleration and the drift of a gyroscope during the standing phase. An initial alignment algorithm based on Adaptive Gradient Descent Algorithm (AGDA) is proposed. In the stepping phase, the extended Kalman filter (EKF) is often used to correct attitude and position in track estimation. However, the measurement noise of the EKF is influenced by the high-frequency acceleration and angular velocity. Thus, the accuracy of the attitude and position will decrease. A double-constrained extended Kalman filtering (DEKF) is proposed. An adaptive parameter positively correlated with the acceleration and angular velocity is set, and the measurement noise in the DEKF is adaptively adjusted. The performance of the proposed method is verified by implementing the pedestrian test trajectory using MPU-9150 MIMU manufactured by InvenSense. The results show that the attitude error of the AGDA is 33.82% less than that of the conventional GDA. The attitude error of DEKF is 21.70% less than that of the conventional EKF. The experimental results verify the effectiveness and applicability of the proposed method.