An Improved System-Level Calibration Method of Strapdown Inertial Navigation System Based on Matrix Factorization

Zhao, Guiling; Tan, Maolin; Guo, Quanrong; Cai, Wenli

doi:10.1109/jsen.2022.3182316

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…Scale factor error is the difference between the scale factors of accelerometer or gyro in actual operation and their factory calibration factors. Installation error is produced by misalignment of the sensitive axes of the accelerometer and gyro with those of the carrier coordinate system (as shown in Figure 2) caused by deviations from ideal installation [30]. In Figure 2 The main deterministic errors of inertial sensors are bias, scale factor error, and installation error.…”

Section: Sensor Error Modelmentioning

confidence: 99%

Modeling and Compensation of Inertial Sensor Errors in Measurement Systems

Zheng

et al. 2023

Electronics

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In the field of surveying and mapping, inertial sensor deterministic errors are poorly understood, and error calibration and compensation are not carried out. Thus, in this study, the effects of three types of deterministic errors (i.e., bias, scale factor error, and installation error) in a conventional inertial measurement unit (IMU) error model on a navigation system are theoretically deduced and verified by simulation. Subsequently, navigation experiments are carried out to investigate the effects of the three deterministic errors on the navigation system. The experimental results show that the gyro bias has the strongest influence on the navigation and positioning accuracy of the system. Consequently, we designed a two-position continuous calibration scheme to calibrate the IMU. The calibration scheme can simultaneously calibrate the bias error of the gyroscope and the accelerometer. When calibrating the bias error of the 0.005°/h order of magnitude, the maximum relative error is 13.16%, and the rest of the calibration relative errors are less than 10%, which verifies the effectiveness of the calibration path designed in this paper. The system is compensated by using the IMU bias calibration results, and the navigation experiment results show that the position accuracy and heading accuracy are improved by 72.68% and 79.65%, respectively, through the calibration and compensation of IMU bias error. Therefore, the position and heading accuracy of the system will be greatly improved by calibrating and compensating the bias error through the two-position calibration path before the IMU is used.

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Section: Sensor Error Modelmentioning

confidence: 99%

Modeling and Compensation of Inertial Sensor Errors in Measurement Systems

Zheng

et al. 2023

Electronics

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“…As the error propagation law in the navigation system is already in place, we can reverse the error equation to deduce the device error. System-level calibration can be summarized into two schemes: (1) Kalman filter-based system-level calibration [4,5]; (2) least-squares fitting-based system-level calibration [6,7]. The Kalman filter-based system-level calibration method requires establishing the error equation of the MEMS device and then compensating for the error.…”

Section: Introductionmentioning

confidence: 99%

Research on a Distributed Calibration Method Based on Specific Force Measurement

et al. 2022

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Micro-electro-mechanical system (MEMS) inertial devices are small volume, lightweight, low cost, and have mass-production characteristics. The development trend of inertial modules is to reduce cost and improve accuracy, and batch calibration of MEMS devices is one of the most feasible solutions to reduce cost. In this paper, we propose a distributed calibration method based on system-level, discrete calibration. The distributed calibration method requires only one or a few rotations of the combined and arranged devices to excite the individual error parameters of the inertial instruments. In this study, the relationship between the error parameters and the navigation error was rewritten using equivalence transformation, and the 24 error parameters of the device were identified by the distributed least-squares estimation using the velocity error as the observed quantity. In simulation experiments, this method could calibrate more MEMS devices simultaneously than the traditional calibration method with the exact accuracy requirement.

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