MEMS IMU Research Articles

Hybrid Stacking Integration of Multi-Sensor Navigation Module with WLFO and Thick Film Process

A hybrid stacking integration of multi-sensor navigation module with WLFO and thick film process was demonstrated. With wafer level fan-out packaging and stacked LTCC substrates, the GNSS, MEMS IMU, magnetometer and altimeter were integrated in one single module, the MEMS inertial sensors and ASIC chips were overlapping integrated with WLFO process as a multilayer wafer and diced, then the diced MEMS-ASIC modules, as well as the accessory circuits were pattered and mounted on several LTCC substrates as a drawer structure to accomplish the final system. The size of inertial sensor module is 10mm×5mm, and the entire navigation module is with size of 26.7cm3 and weight of 36g, meanwhile, with a high performance, the multi-sensor module can achieve low cost and high performance simultaneously. This module can be utilized for reliable navigation and gesture perception of micro UAV/UUV, automotive and other minimized platforms.

상용 MEMS IMU와 차량 CAN 데이터를 이용한 지상차량 항법용 GPS/INS/DR 통합항법시스템 구현 및 실측결과

상용 MEMS IMU와 차량 CAN 데이터를 이용한 지상차량 항법용 GPS/INS/DR 통합항법시스템 구현 및 실측결과

Investigation of MEMS IMU Performance in Sounding Rocket

Investigation of MEMS IMU Performance in Sounding Rocket

Simulation Framework for Testing Train Navigation Algorithms Based on 9-DOF-IMU and Tachometers

The integration of candidate sensors such as a microelectromechanical system-based inertial measurement unit (MEMS IMU) for localization and navigation has been widely explored for vehicular, pedestrian, and robotics applications. They also arise as candidate sensors in the train navigation context, and their integration in safety-relevant applications has become a prominent area of interest. This requires sensor modeling in a specific context, the search of new information sources (e.g., digital maps), the development of navigation algorithms, and the testing of the system. In this context, this article proposes a one-stop simulation framework for testing train navigation algorithms that uses nine degrees of freedom (DOFs) IMU and tachometers. The simulation framework can generate synthetic signals based on the dynamic model of a train, digital maps, speed profiles, and a sensor error model. It also uses real signals recorded from experimental measuring campaigns. Train navigation algorithms are then executed. Finally, the performance of the obtained results is evaluated. The main contribution made by this framework is its flexibility when used to evaluate different sensors, train configurations, tracks, mechanization methods, navigation algorithms, and so on during different stages of the development process. Simulation framework use cases are presented in this article, where several mechanization methods and train navigation algorithms are tested. Finally, the experimental data are recorded and included in the presented simulation framework to evaluate an example of a train navigation algorithm based on a 9-DOF-IMU and tachometers. The mean differences in the synthetic and experimental IMU signals are around 3.52% in accelerometers, below 0.01% in gyroscopes, and 10.16% in magnetometers in the acceleration sections (i.e., when the train is applying torque).

Attitude Measurement for High-Spinning Projectile with a Hollow MEMS IMU Consisting of Multiple Accelerometers and Gyros.

A low cost, high precision hollow structure MEMS IMU has been developed to measure the roll angular rate of a high-spinning projectile. The hollow MEMS IMU is realized by designing the scheme of non-centroid configuration of multiple accelerometers. Two dual-axis accelerometers are respectively mounted on the pitch axis and the yaw axis away from the center of mass of the high-spinning projectile. Three single-axis gyros are mounted orthogonal to each other to measure the angular rates, respectively. The roll gyro is not only used to judge the spinning direction, but also to measure and compensate for the low rotation speed of the high-spinning projectile. In order to improve the measurement accuracy of the sensor, the sensor output error is modeled and calibrated by the least square method. By analyzing the influence of noise statistical characteristics on angular rate solution accuracy, an adaptive unscented Kalman filter (AUKF) algorithm is proposed, which has a higher estimation accuracy than UKF algorithm. The feasibility of the method is verified by numerical simulation. By using the MEMS IMU device to build a semi-physical simulation platform, the solution accuracy of the angular rate is analyzed by simulating different rotation speeds of the projectile. Finally, the flight test is carried out on the rocket projectile with the hollow MEMS IMU. The test results show that the hollow MEMS IMU is reasonable and feasible, and it can calculate the roll angular rate in real time. Therefore, the hollow MEMS IMU designed in this paper has certain engineering application value for high-spinning projectiles.

Self‐alignment and calibration of a MEMS IMU: Improving observability and estimability by rotary motions of IMU

AbstractThe stationary self‐alignment and calibration (SSAC) for a low‐cost MEMS IMU is quite challenging due to the poor observability of an inertial system under static condition and the significant sensor errors of MEMS inertial sensors. This research proposes to employ IMU rotations to improve the system observability and estimability regarding the SSAC of a low‐cost MEMS IMU. IMU rotations about the X, Y, and Z axes are employed in this paper. The analytic estimation algorithm for each error state is derived and the observability of the system with IMU rotation is analyzed. As the observability analysis will not provide clues about how well an error state can be estimated, the estimability analysis is also conducted based on the eigenvalues and eigenvectors from the covariance matrix in the Kalman filter. Tests are conducted with a tri‐axial turntable to verify the improvements on system observability and estimability brought by IMU rotations. Of both theoretical analysis and results indicated with proper IMU rotations, only azimuth error still remains unobservable, and the IMU rotation also significantly improves the estimability of all error states, including the unobservable azimuth.

허리장착 IMU 센서 시스템 기반의 소방관 위치 추정을 위한 자이로 드리프트 제거 알고리즘

허리장착 IMU 센서 시스템 기반의 소방관 위치 추정을 위한 자이로 드리프트 제거 알고리즘

Position Tracking System Based on UWB and MEMS IMU

Position Tracking System Based on UWB and MEMS IMU

The Study on Performance of MEMS IMU for Launch Vehicle under High Vibration Environment

The Study on Performance of MEMS IMU for Launch Vehicle under High Vibration Environment

Indoor vehicle tracking with a smart MEMS sensor

Indoor navigation and vehicle tracking require special measurement techniques. The reference points and routes used by classic AGV (Automated Guided Vehicle) systems are usually buried under floor surface or painted directly on the floor, thus limiting the set of possible transportation paths. However, the indoor environment of an industrial warehouse is dynamic, the number and location of objects inside are subject to frequent changes and these changes might not be reflected in the map of the area. In such conditions, navigation according to the on-board instruments (dead-reckoning) could provide valuable information about the position and orientation of the vehicle. This paper reports test results from a smart sensor using a 6-axis MEMS IMU unit and a self-calibrating procedure for indoor vehicle orientation tracking. The smart sensor, integrated with information from wheel encoders can produce 2D position coordinates suitable for navigation. Original data processing algorithm, applied in the sensor, was developed by the authors as a part of the research project on mobile robotics.

Maneuver Classification of a Moving Vehicle with Six Degrees of Freedom Using Logistic Regression Technique

This paper presents a novel on line algorithm for maneuver classification of a moving vehicle with six degrees of freedom, using on-board MEMS IMU’s data (three accelerometers and three rate gyros). The classification is either discrete (i.e. high, low or no maneuver), or continuous (a value that reflects the intensity of the maneuver). It should be mentioned that there is no explicit solution for this problem in any research paper previously published, due to the inability to find a direct mathematical model capable of characterizing this problem, despite its importance and its impact in improving the functioning of navigation systems. The proposed algorithm is based on a machine learning technique called logistic regression, which is a discriminative probabilistic classification model. Computer simulations, using MEMS IMU’s data taken from real experiments of an UAV, showed the effectiveness of the proposed algorithm, taking into account the sampling time, and the suitability for a wide spectrum of applications.

Attitude Determination Algorithm based on Relative Quaternion Geometry of Velocity Incremental Vectors for Cost Efficient AHRS Design

A novel attitude determination method is investigated that is computationally efficient and implementable in low cost sensor and embedded platform. Recent result on attitude reference system design is adapted to further develop a three-dimensional attitude determination algorithm through the relative velocity incremental measurements. For this, velocity incremental vectors, computed respectively from INS and GPS with different update rate, are compared to generate filter measurement for attitude estimation. In the quaternion-based Kalman filter configuration, an Euler-like attitude perturbation angle is uniquely introduced for reducing filter states and simplifying propagation processes. Furthermore, assuming a small angle approximation between attitude update periods, it is shown that the reduced order filter greatly simplifies the propagation processes. For performance verification, both simulation and experimental studies are completed. A low cost MEMS IMU and GPS receiver are employed for system integration, and comparison with the true trajectory or a high-grade navigation system demonstrates the performance of the proposed algorithm.

A hybrid error modeling for MEMS IMU in integrated GPS/INS navigation system

Continuity of accurate navigational data for intelligent transportation applications has been widely provided by utilizing low-cost navigation systems through integrating GPS with micro-electro-mechanical-system (MEMS) inertial sensors. To achieve the required accuracy, augmentation of Kalman filter (KF) with nonlinear error modeling techniques such as fast orthogonal search (FOS) was introduced to enhance the navigational solution by estimating and eliminating a great part of both linear and nonlinear errors of azimuth angle sensed by MEMS gyro. Although this augmented approach enhanced the overall navigational accuracy to some extent, it still suffers from some drawbacks that diverge the system accuracy during GPS long outage periods. These drawbacks stem from the wide-variational behavior and high nonlinearities of the errors in MEMS gyros which make it difficult to depend on the non-adaptive linear error model provided by KF to model the two types of MEMS azimuth errors.In this paper we tried to minimize the effect of uncertainties associated with the KF azimuth prediction during the absence of GPS by introducing a hybrid error model which employs support vector machine (SVM) to model the KF output and FOS, based on autoregressive (AR) concept, to model the nonlinear azimuth errors. The performance of the proposed hybrid SVM-FOS approach is evaluated for GPS/ RISS (Reduced inertial sensor system integrated system) and the results were compared with the conventional KF and augmented KF-FOS approaches.

Sensor Fusion Algorithm by Complementary Filter for Attitude Estimation of Quadrotor with Low-Cost IMU

This paper proposes a sensor fusion algorithm by complementary filter technique for attitude estimation of quadrotor UAV using low-cost MEMS IMU. Angular rate from gyroscope tend to drift over a time while accelerometer data is commonly effected with environmental noise. Therefore, high frequency gyroscope signal and low frequency accelerometer signal is fused using complementary filter algorithm. The complementary filter scaling factor K1=0.98 and K2=0.02 are used to merge both gyro and accelerometer. The results show that the smooth roll, pitch and yaw attitude angle can be obtained from the low cost IMU by using proposed sensor fusion algorithm.

Accurate Positioning Technique Based on Long 3D Trajectory Using Low-cost Multi-GNSS Receiver and MEMS IMU

Accurate Positioning Technique Based on Long 3D Trajectory Using Low-cost Multi-GNSS Receiver and MEMS IMU

STATISTICAL SENSOR FUSION OF A 9-DOF MEMS IMU FOR INDOOR NAVIGATION

Abstract. Sensor fusion of a MEMS IMU with a magnetometer is a popular system design, because such 9-DoF (degrees of freedom) systems are capable of achieving drift-free 3D orientation tracking. However, these systems are often vulnerable to ambient magnetic distortions and lack useful position information; in the absence of external position aiding (e.g. satellite/ultra-wideband positioning systems) the dead-reckoned position accuracy from a 9-DoF MEMS IMU deteriorates rapidly due to unmodelled errors. Positioning information is valuable in many satellite-denied geomatics applications (e.g. indoor navigation, location-based services, etc.). This paper proposes an improved 9-DoF IMU indoor pose tracking method using batch optimization. By adopting a robust in-situ user self-calibration approach to model the systematic errors of the accelerometer, gyroscope, and magnetometer simultaneously in a tightly-coupled post-processed least-squares framework, the accuracy of the estimated trajectory from a 9-DoF MEMS IMU can be improved. Through a combination of relative magnetic measurement updates and a robust weight function, the method is able to tolerate a high level of magnetic distortions. The proposed auto-calibration method was tested in-use under various heterogeneous magnetic field conditions to mimic a person walking with the sensor in their pocket, a person checking their phone, and a person walking with a smartwatch. In these experiments, the presented algorithm improved the in-situ dead-reckoning orientation accuracy by 79.8–89.5 % and the dead-reckoned positioning accuracy by 72.9–92.8 %, thus reducing the relative positioning error from metre-level to decimetre-level after ten seconds of integration, without making assumptions about the user’s dynamics.

Aircraft navigation using MEMS IMU and ground radio beacons

Navigation of a small-sized aircraft is discussed using integrated data from MEMS-based IMU and receiver of ground radio beacon signals within an integrated tightly-coupled orientation and navigation system (IONS). IONS algorithms and errors in orientation and navigation parameters are considered both during prestart IMU error calibration with external aiding over a limited time interval and during simulation of aircraft flight along a preset path. Data in IONS are integrated using the extended Kalman filter (EKF). In simulation modeling of IONS functioning algorithms in Matlab (Simulink) we used data of bench tests of MEMS sensors developed by Elektropribor.

Low-cost and high performance ultra-tightly coupled GPS/INS integrated navigation method

In this paper, a low-cost, high-performance ultra-tightly coupled GPS/INS integrated navigation method is proposed. The measurement of the integration Kalman filter is generated from a moving average filter in the proposed method. A moving average filter is inserted between the signal tracking error estimator and the integration Kalman filter to avoid other measurement noise reduction methods’ limitations and the error of the MEMS inertial sensor is modeled as bias repeatability and bias stability in the proposed method. A new integration Kalman filter is derived to compensate the time delay caused by the moving average filter. The proposed method improves signal tracking performance of the GPS receiver and resolves the problem caused by the difference between correlator output rate and integration Kalman filter update rate. In addition to these, the proposed method resolves the performance degradation problem of the MEMS inertial sensors. In order to verify the proposed method, an experimental platform is constructed, which consists of a GPS/IMU signal generator, a GPS SDR S/W module and a GPS/INS S/W module. The results in weak signal environment show that the proposed method gives better navigation performance than other methods without the moving average filter and/or the model of bias stability of MEMS IMU.

Design of AHRS using Low-Cost MEMS IMU Sensor and Multiple Filters

Design of AHRS using Low-Cost MEMS IMU Sensor and Multiple Filters

Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter.

In airborne MEMS SINS transfer alignment, the error of MEMS IMU is highly environment-dependent and the parameters of the system model are also uncertain, which may lead to large error and bad convergence of the Kalman filter. In order to solve this problem, an improved adaptive incremental Kalman filter (AIKF) algorithm is proposed. First, the model of SINS transfer alignment is defined based on the “Velocity and Attitude” matching method. Then the detailed algorithm progress of AIKF and its recurrence formulas are presented. The performance and calculation amount of AKF and AIKF are also compared. Finally, a simulation test is designed to verify the accuracy and the rapidity of the AIKF algorithm by comparing it with KF and AKF. The results show that the AIKF algorithm has better estimation accuracy and shorter convergence time, especially for the bias of the gyroscope and the accelerometer, which can meet the accuracy and rapidity requirement of transfer alignment.