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

Farhad, Ghanipoor,; Hashemi, Mojtaba; Salarieh, Hassan

doi:10.1109/jsen.2019.2963538

Cited by 31 publications

(10 citation statements)

References 23 publications

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“…Similarly, Ref. [75] introduced an angular acceleration estimator to improve the additional error produced by the calibration of the turntable. He proposed a nonlinear model, considering that the output of the accelerometer is affected by the arm effect, installation error and the distance from the center of the turntable, and estimated the model parameters through the use of a transformed unscented Kalman filter (TUKF).…”

Section: Nonautonomous Calibrationmentioning

confidence: 99%

MEMS Inertial Sensor Calibration Technology: Current Status and Future Trends

Shang

et al. 2022

Micromachines

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A review of various calibration techniques of MEMS inertial sensors is presented in this paper. MEMS inertial sensors are subject to various sources of error, so it is essential to correct these errors through calibration techniques to improve the accuracy and reliability of these sensors. In this paper, we first briefly describe the main characteristics of MEMS inertial sensors and then discuss some common error sources and the establishment of error models. A systematic review of calibration methods for inertial sensors, including gyroscopes and accelerometers, is conducted. We summarize the calibration schemes into two general categories: autonomous and nonautonomous calibration. A comprehensive overview of the latest progress made in MEMS inertial sensor calibration technology is presented, and the current state of the art and development prospects of MEMS inertial sensor calibration are analyzed with the aim of providing a reference for the future development of calibration technology.

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Section: Nonautonomous Calibrationmentioning

confidence: 99%

MEMS Inertial Sensor Calibration Technology: Current Status and Future Trends

Shang

et al. 2022

Micromachines

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“…MEMS IMUs offer significant size and power savings over mechanical and optical variants at the expense of measurement accuracy [ 20 ]; however, this can be improved by calibrating the sensor packages within the IMU on a chip-by-chip basis. This was achieved by subjecting the trackers to a series of controlled static and dynamic tests while measuring the raw sensor outputs to calculate the calibration factors.…”

Section: System Designmentioning

confidence: 99%

Inertial Tracking System for Monitoring Dual Mobility Hip Implants In Vitro

Shuttleworth

Vickers

Smeeton

et al. 2023

Sensors

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Dual mobility (DM) implants are being increasingly used for total hip arthroplasties due to the additional range of motion and joint stability they afford over more traditional implant types. Currently, there are no reported methods for monitoring their motions under realistic operating conditions while in vitro and, therefore, it is challenging to predict how they will function under clinically relevant conditions and what failure modes may exist. This study reports the development, calibration, and validation of a novel inertial tracking system that directly mounts to the mobile liner of DM implants. The tracker was custom built and based on a miniaturized, off-the-shelf inertial measurement unit (IMU) and employed a gradient-decent sensor fusion algorithm for amalgamating nine degree-of-freedom IMU readings into three-axis orientation estimates. Additionally, a novel approach to magnetic interference mitigation using a fixed solenoid and magnetic field simulation was evaluated. The system produced orientation measurements to within 1.0° of the true value under ideal conditions and 3.9° with a negligible drift while in vitro, submerged in lubricant, and without a line of sight.

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“…At present, INS error estimation and compensation methods are divided into two aspects: one is modeling the errors of MEMS-IMU from a mechanism level [ 2 , 3 , 4 , 5 , 6 , 7 ]; the other is establishing the error propagation model based on the principle of navigation. In terms of IMU error modeling methods, especially MEMS-IMU, some studies divide IMU errors into two types, which are deterministic errors, such as scale factor, bias and misalignment, and stochastic errors such as bias instability and scale factor instability [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Performance Enhancement of INS and UWB Fusion Positioning Method Based on Two-Level Error Model

Zhang

Shi

et al. 2023

Sensors

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In GNSS-denied environments, especially when losing measurement sensor data, inertial navigation system (INS) accuracy is critical to the precise positioning of vehicles, and an accurate INS error compensation model is the most effective way to improve INS accuracy. To this end, a two-level error model is proposed, which comprehensively utilizes the mechanism error model and propagation error model. Based on this model, the INS and ultra-wideband (UWB) fusion positioning method is derived relying on the extended Kalman filter (EKF) method. To further improve accuracy, the data prefiltering algorithm of the wavelet shrinkage method based on Stein’s unbiased risk estimate–Shrink (SURE-Shrink) threshold is summarized for raw inertial measurement unit (IMU) data. The experimental results show that by employing the SURE-Shrink wavelet denoising method, positioning accuracy is improved by 76.6%; by applying the two-level error model, the accuracy is further improved by 84.3%. More importantly, at the point when the vehicle motion state changes, adopting the two-level error model can provide higher computational stability and less fluctuation in trajectory curves.

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

Cited by 31 publications

References 23 publications

MEMS Inertial Sensor Calibration Technology: Current Status and Future Trends

MEMS Inertial Sensor Calibration Technology: Current Status and Future Trends

Inertial Tracking System for Monitoring Dual Mobility Hip Implants In Vitro

Performance Enhancement of INS and UWB Fusion Positioning Method Based on Two-Level Error Model

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