Novel Self-Calibration Method for IMU Using Distributed Inertial Sensors

Guner, Ufuk; Daşdemir, Janset

doi:10.1109/jsen.2022.3227341

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…Reference 28 introduces a novel self-calibration method for inertial measurement units (IMUs) utilizing distributed inertial sensors. The authors propose an innovative approach that leverages multiple sensors to calibrate IMUs autonomously.…”

Section: The Proposed Algorithm Formulationmentioning

confidence: 99%

A fast strapdown gyrocompassing algorithm based on INS differential errors

Atashgah,

Mohammadkarimi,

Ebrahimi

2023

Sci Rep

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This paper presents an enhanced algorithm for inertial gyrocompassing using strapdown sensors, which performs faster than the other available ones. The proposed algorithm is based on differential errors in an inertial navigation system and requires only the output of the inertial measurement unit while extracting and compensating for the inertial sensor errors. After eliminating the error of the inertial sensors, which is accomplished swiftly, the coarse alignment algorithm performs with error-compensated sensors, and the true north is extracted accurately. The number of non-observable parameters of the algorithm is equal to that of the fine alignment algorithm; therefore, its accuracy is the same as that of a well-tuned fine alignment. Numerical simulations and lab experiments demonstrate that the proposed method performs heading estimation in the time required to perform the coarse alignment, which is faster than the existing fine alignment algorithms.

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Section: The Proposed Algorithm Formulationmentioning

confidence: 99%

A fast strapdown gyrocompassing algorithm based on INS differential errors

Atashgah,

Mohammadkarimi,

Ebrahimi

2023

Sci Rep

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“…Positioning technology greatly facilitates many aspects of our lives. Among these, inertial technology holds a significant position in the field of positioning due to its high real-time performance, full autonomy, and immunity to external environmental interference [1,2]. Inertial navigation systems can be classified into two types based on their structure: Strapdown Inertial Navigation Systems (SINS) and Platform Inertial Navigation Systems (PINS).…”

Section: Introductionmentioning

confidence: 99%

Calibration method of strapdown inertial measurement unit based on two-axis turntable

Liu,

Gao,

Zhan

et al. 2024

J. Phys.: Conf. Ser.

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This paper analyzes the causes and effects of error in the Strapdown Micro-Electro-Mechanical System Inertial Measurement Unit (MEMS-IMU), utilizing a sensor calibration method based on a two-axis turntable. By considering deterministic errors such as sensor bias, scale factor, and non-orthogonality of the sensitive axis, an error model was established to assess their impact on measurement accuracy. An expression was provided to represent the relationship between sensor data collection and external input stimuli, storing the error parameters of the accelerometer and the gyroscope separately in parameter matrices. The error parameter matrix between input and output quantities was fitted using the least squares method, and error compensation was completed in conjunction with the error model. By comparing the original data with the error-compensated data, the calibration results were obtained. This improved the consistency of the measurements from the sensitive axes to the stimulus source, reduced the zero bias error of each sensitive axis, and enhanced the positioning accuracy of the Strapdown MEMS-IMU.

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A model-based and efficient parameters correction method for low-cost MEMS IMU

Xu,

et al. 2024

Meas. Sci. Technol.

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With the wide use of intelligent mobile terminals, inertial measurement unit (IMU) made by micro-electromechanical system (MEMS) has become an important part of their positioning systems. MEMS IMU has advantages of low cost, low power consumption and small size. However, the scale factor error, nonorthogonality error, and bias affect the measurement accuracy of MEMS IMU. In this paper, the output data of MEMS IMU at the six-position were analysed and modelled. A parameter correction method based on the model was proposed for obtaining more accurate scale factor, nonorthogonality parameters and bias. The proposed method has low-cost and the obtained parameters’ accuracy are consistency. Ten groups of two MEMS IMUs parameter correction experiments show that the parameters obtained by the proposed method have small fluctuation and high consistency. The navigation test shows that the navigation errors of the corrected data of one MEMS IMU reduce to 15.2 %, 9.3 % and 5.1 % in the east, north and up directions compared with the position errors of the original data, which proves the effectiveness of the proposed method.

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Novel Self-Calibration Method for IMU Using Distributed Inertial Sensors

Cited by 7 publications

References 23 publications

A fast strapdown gyrocompassing algorithm based on INS differential errors

A fast strapdown gyrocompassing algorithm based on INS differential errors

Calibration method of strapdown inertial measurement unit based on two-axis turntable

A model-based and efficient parameters correction method for low-cost MEMS IMU

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