A New Calibration Method of MEMS IMU Plus FOG IMU

Lu, Jiazhen; Ye, Lili; Zhang, Jingxian; Luo, Wei; Liu, Haiqiao

doi:10.1109/jsen.2022.3160692

Cited by 20 publications

(12 citation statements)

References 26 publications

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“…It is an adjustment between the sensor's output signal and the measured quantity to reach the closest value to the truth [75,76]. Adopting a proper calibration model can significantly affect the performance of the MEMS-based instruments [32,[76][77][78][79][80]. A MEMS pressure sensor mainly suffer from four types of error, including offset, gain, nonlinearity, and hysteresis.…”

Section: Sensor Calibrationmentioning

confidence: 99%

Development of IOT-based low-cost MEMS pressure sensor for groundwater level monitoring

Barzegar

Blanks²,

Gharehdash

et al. 2023

Meas. Sci. Technol.

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Groundwater level monitoring is critical to protect and manage of groundwater resources. A properly designed and executed instrumentation can play important role to increase the quality and reliability of collected data and reducing total monitoring costs. The efficiency of the instrumentation is mainly depending on the accuracy and reliability of the installed sensors. This study presents, the testing and application of a cost-effective pressure sensor (0-689 kPa) range) for water level monitoring based on microelectromechanical system (MEMS) technology and Internet of things (IOT) concept. The sensor performance in term of accuracy, precision, repeatability, temperature was investigated in laboratory columns (with constant water level, increasing and decreasing water levels at various rates) and in-situ conditions in an observation bore (with natural groundwater level fluctuations). The results shows that the MEMS sensor capable of providing reliable and adequate monitoring scheme with an accuracy of 0.31% full scale (FS) (2.13 kPa).

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Section: Sensor Calibrationmentioning

confidence: 99%

Development of IOT-based low-cost MEMS pressure sensor for groundwater level monitoring

Barzegar

Blanks²,

Gharehdash

et al. 2023

Meas. Sci. Technol.

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show abstract

“…The IMU comprises three gyroscopes and three accelerometers. Considering the bias error, non-orthogonal error, and scale factor error of the three-axis gyroscopes and three-axis accelerometers, the error model [32] is established as follows:…”

Section: B System-level Calibration Kalman Filtering Modelmentioning

confidence: 99%

Optimal Path Planning Method for IMU System-Level Calibration Based on Improved Dijkstra’s Algorithm

Bai

Wang

et al. 2023

IEEE Access

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The calibration path of system-level calibration directly affects the incentive effect of the error term and thus the calibration accuracy. Currently, the planning of system-level calibration paths is predominantly designed based on personal experience, resulting in insufficient incentive for error terms, low calibration accuracy, and long calibration times. Therefore, this study proposes a system-level calibration optimal path planning method based on an improved Dijkstra's algorithm. First, the system-level calibration optimal path planning problem was modeled as a multi-fork regular root tree model, and the adaptability of Dijkstra's algorithm was improved. Second, a 30-dimensional Kalman filter model was designed for system-level calibration. Then, simulation experiments were conducted, and the results demonstrated that the calibration accuracy of the error term reached 90% within 330 s. Finally, a Micro-Electro-Mechanical system (MEMS) inertial sensor, model PA-IMU488B, was used for experimental verification, and the results were compared with the discrete calibration results. The results indicate that the bias and scale factor errors of the MEMS inertial sensor reached the target accuracy within 5 min. The optimal path planning method for system-level calibration proposed in this study is not dependent on a high-precision turntable, is applicable to sensors of different accuracies, and decreases calibration time while ensuring calibration accuracy.

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“…The orthogonal small-angle transformation matrix is a skew-symmetric matrix. Therefore,

is a skew-symmetric matrix [ 29 , 30 ]. The three misalignment angles between the

-frame and the

-frame are

, and

.…”

Section: Polar Decomposition Of Installation Error Matrixmentioning

confidence: 99%

“…There are misalignment angles between the

-frame and the

-frame. The misalignment relationship can be compensated by the transformation of the orthogonal small-angle transform [ 29 , 30 ]. The principle of orthogonal small-angle transform is shown in Figure 2 b.…”

Section: Polar Decomposition Of Installation Error Matrixmentioning

confidence: 99%

Research on IMU Calibration Model Based on Polar Decomposition

et al. 2023

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As an important deterministic error of the inertial measurement unit (IMU), the installation error has a serious impact on the navigation accuracy of the strapdown inertial navigation system (SINS). The impact becomes more severe in a highly dynamic application environment. This paper proposes a new IMU calibration model based on polar decomposition. Using the new model, the installation error is decomposed into a nonorthogonal error and a misalignment error. The compensation of the IMU calibration model is decomposed into two steps. First, the nonorthogonal error is compensated, and then the misalignment error is compensated. Based on the proposed IMU calibration model, we used a three-axis turntable to calibrate three sets of strapdown inertial navigation systems (SINS). The experimental results show that the misalignment errors are larger than the nonorthogonal errors. Based on the experimental results, this paper proposes a new method to simplify the installation error. This simplified method defines the installation error matrix as an antisymmetric matrix composed of three misalignment errors. The navigation errors caused by the proposed simplified calibration model are compared with the navigation errors caused by the traditional simplified calibration model. The 48-h navigation experiment results show that the proposed simplified calibration model is superior to the traditional simplified calibration model in attitude accuracy, velocity accuracy, and position accuracy.

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A New Calibration Method of MEMS IMU Plus FOG IMU

Cited by 20 publications

References 26 publications

Development of IOT-based low-cost MEMS pressure sensor for groundwater level monitoring

Development of IOT-based low-cost MEMS pressure sensor for groundwater level monitoring

Optimal Path Planning Method for IMU System-Level Calibration Based on Improved Dijkstra’s Algorithm

Research on IMU Calibration Model Based on Polar Decomposition

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