Calibration of the Thermal Model of an Inertial Measurement Unit with Three Angular Rate Sensors

Micro inertial measurement unit integration storage test system is a typical multi-sensor information fusion system consists of microsensors. The Federated Kalman filter is applied to micro inertial measurement unit integration storage test system. The general structure and characteristics of Federated Kalman filter is expounded. The four-order Runge-Kutta method based on quaternion differential equation was used to dispose the output angular rate data from gyroscope, and the recurrence expressions was established too. The control system based ARM Cortex-M4 master-slave structure is adopted in this paper. The result shown that the dimensionality reduced algorithm significantly reduces implementation complexity of the method and the amount computation. The filtering effect and real-time performance have much increased than traditionally method.

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

Measurement and Control ◽

10.1177/0020294020917702 ◽

2020 ◽

pp. 002029402091770

Author(s):

Li Xing ◽

Xiaowei Tu ◽

Weixing Qian ◽

Yang Jin ◽

Pei Qi

Keyword(s):

Kalman Filter ◽

Angular Velocity ◽

Extended Kalman Filter ◽

Inertial Measurement Unit ◽

Angular Rate ◽

The State ◽

Measurement Unit ◽

Fusion Method ◽

Microelectromechanical System ◽

Inertial Measurement

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.

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An Implementation of Error Minimization Position Estimate in Wireless Inertial Measurement Unit using Modification ZUPT

EMITTER International Journal of Engineering Technology ◽

10.24003/emitter.v4i2.156 ◽

2016 ◽

Vol 4 (2) ◽

Author(s):

Adytia Darmawan ◽

Sanggar Dewanto ◽

Dadet Pramadihanto

Keyword(s):

Inertial Measurement Unit ◽

Robot Navigation ◽

Angular Rate ◽

Position Estimation ◽

Experimental Result ◽

Measurement Unit ◽

Legged Robot ◽

Positioning Systems ◽

Inertial Measurement ◽

Position Estimate

Position estimation using WIMU (Wireless Inertial Measurement Unit) is one of emerging technology in the field of indoor positioning systems. WIMU can detect movement and does not depend on GPS signals. The position is then estimated using a modified ZUPT (Zero Velocity Update) method that was using Filter Magnitude Acceleration (FMA), Variance Magnitude Acceleration (VMA) and Angular Rate (AR) estimation. Performance of this method was justified on a six-legged robot navigation system. Experimental result shows that the combination of VMA-AR gives the best position estimation.

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A Sporting Chance

Mechanical Engineering ◽

10.1115/1.2011-jul-3 ◽

2011 ◽

Vol 133 (07) ◽

pp. 40-45

Author(s):

Noel C. Perkins ◽

Kevin King ◽

Ryan McGinnis ◽

Jessandra Hough

Keyword(s):

Inertial Measurement Unit ◽

Wireless Sensors ◽

Microelectromechanical Systems ◽

Angular Rate ◽

Rigid Body Dynamics ◽

The Body ◽

Measurement Unit ◽

Golf Club ◽

Sports Training ◽

Inertial Measurement

This article discusses using wireless sensors to improve sports training. One example of wireless sensors is inertial sensors that were first developed for automotive and military applications. They are tiny accelerometers and angular rate gyros that can be combined to form a complete inertial measurement unit. An inertial measurement unit (IMU) detects the three-dimensional motion of a body in space by sensing the acceleration of one point on the body as well as the angular velocity of the body. When this small, but rugged device is mounted on or embedded within sports gear, such as the shaft of a golf club, the IMU provides the essential data needed to resolve the motion of that equipment. This technology—and sound use of the theory of rigid body dynamics—is now being developed and commercialized as the ingredients in new sports training systems. It won’t be too long before microelectromechanical systems based hardware and sophisticated software combine to enable athletes at any level to get world-class training.

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Network dynamic field calibration for micro inertial measurement unit

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331218811465 ◽

2018 ◽

Vol 41 (10) ◽

pp. 2826-2837

Author(s):

Xu Yun ◽

Su Yan ◽

Zhu Xinhua ◽

Luo Zhihang

Keyword(s):

Inertial Measurement Unit ◽

Angular Rate ◽

Calibration Method ◽

Measurement Unit ◽

Network Dynamic ◽

Zero Bias ◽

Inertial Measurement ◽

Field Calibration ◽

Dynamic Field ◽

Environment Temperature

Calibration accuracy of micro inertial measurement unit (MIMU) will affect the navigation accuracy of micro strap-down inertial navigation system. Generally, when the application environment changes (i.e. environment temperature and humidity), the specific force and angular rate output by MIMU will be changed, which were influenced by the zero bias of accelerometers, the zero drift of gyroscopes and so on. Thus, it is necessary to carry out the field calibration for MIMU. Aiming at the application of multi MIMUs, the network dynamic field calibration method is proposed in this paper. According to the navigation attitude and velocity error models, the estimating model is established. Then, the observability for the parameters in the estimating model is analyzed. By fusing the output information of MIMUs and GPS, vehicle experiments are carried out with the designed maneuvers in order to estimate the parameters. The experiment result illustrated that the proposed network dynamic filed calibration can efficiently realize the calibration for the parameters in the model of several MIMUs simultaneously.

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Pedestrian navigation system based on the inertial measurement unit sensor for outdoor and indoor environments

Journal of Sensors and Sensor Systems ◽

10.5194/jsss-9-7-2020 ◽

2020 ◽

Vol 9 (1) ◽

pp. 7-13

Author(s):

Marcin Uradzinski ◽

Hang Guo

Keyword(s):

Inertial Measurement Unit ◽

Angular Rate ◽

Dead Reckoning ◽

Step Length ◽

Position Estimation ◽

Outdoor Environment ◽

Measurement Unit ◽

Indoor Environments ◽

Inertial Measurement ◽

Step Detection

Abstract. With the continuous improvement of the hardware level of the inertial measurement unit (IMU), indoor pedestrian dead reckoning (PDR) using an inertial device has been paid more and more attention. Typical PDR system position estimation is based on acceleration obtained from accelerometers to measure the step count, estimate step length and generate the position with the heading received from angular sensors (magnetometers and gyroscopes). Unfortunately, collected signals are very responsive to the alignment of sensor devices, built-in instrumental errors and distortions from the surrounding environment. In our work, a pedestrian positioning method using step detection based on a shoe-mounted inertial unit is arranged and put to the test, and the final results are analyzed. The extended Kalman filter (EKF) provides estimation of the errors which are acquired by the XSENS IMU sensor biases. The EKF is revised with acceleration and angular rate computations by the ZUPT (zero velocity update) and ZARU (zero angular rate update) algorithms. The step detection associated with these two solutions is the perfect choice to calculate the current position and distance walked and to estimate the IMU sensors' collected errors by using EKF. The test with a shoe-mounted IMU device was performed and analyzed in order to check the performance of the recommended method. The combined PDR final results were compared to GPS/Beidou postprocessing kinematic results (outdoor environment) and to a real route which was prepared and calculated for an indoor environment. After the comparison, the results show that the accuracy of the regular-speed walking under ZUPT and ZARU combination in the case of outdoor positioning did not go beyond 0.19 m (SD) and for indoor positioning accuracy did not exceed 0.22 m (SD). The authors are conscious that built-in drift errors coming from accelerometers and gyroscopes, as well as the final position obtained by XSENS IMU, are only stable for a short time period. Based on this consideration, our future work will be focused on supporting the methods presented with radio technologies (WiFi) or image-based solutions to correct all IMU imperfections.

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Embedded inertial measurement unit reveals pole lean angle for cross-country skiing

Sports Engineering ◽

10.1007/s12283-019-0316-3 ◽

2020 ◽

Vol 23 (1) ◽

Cited By ~ 1

Author(s):

John Bruzzo ◽

Noel C. Perkins ◽

Aki Mikkola

Keyword(s):

Measurement System ◽

Inertial Measurement Unit ◽

Angular Rate ◽

Measurement Unit ◽

Inertial Measurement ◽

Lean Angle ◽

Cross Country Skiing ◽

Cross Country ◽

Mean Differences ◽

Mean Square Errors

AbstractThis study introduces an inertial measurement unit-based measurement system for resolving the dynamic lean angle of a ski pole during double poling while cross-country skiing. The measurement system estimates both the pole lean angle and pole–terrain contact events. Reported are results from 20 trials providing validated estimates of ski pole lean angle and the timing of pole plant and pole lift events. The pole lean angle is estimated from a complementary filter that fuses estimates of orientation from the embedded accelerometer and angular rate gyro. Validation follows from comparison with video capture measurements. Bland–Altman analysis showed agreement between the two measurement modalities with less than 5% bias in the mean differences (relative to the lean angle range of motion). Companion correlation analysis confirms strong correlation ($$r = 0.99$$ r = 0.99 ) between the inertial measurement unit and video-estimated lean angles and with mean root-mean-square errors below 4.5$$^{\circ }$$ ∘ .

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