An All-Parameter System-Level Calibration for Stellar-Inertial Navigation System on Ground

Lu, Jiazhen; Lei, Chaohua; Liang, Shufang; Yang, Yanqiang

doi:10.1109/tim.2017.2674758

Cited by 44 publications

(14 citation statements)

References 12 publications

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“…At present, the research of this method mainly focuses on the design of Kalman filter and the arrangement of calibration path. Aiming at the complex error characteristics of IMU, some researchers have established different error models and used Kalman filters with different dimensions to estimate them [12], [13], [14], [15], [16]. Wang et al [17] regarded navigation error as observable error and proposed using extended Kalman filter to estimate error parameters.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…In order to increase accuracy and obtaining more error coefficients like nonlinear scale factor, other equipment like a centrifuge and mechanical arm have been used [13][14][15]. Along with these equipment, other methods like Kalman Filter, nonlinear least squares and other optimization methods are used to increase the accuracy of obtaining error coefficients [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Simplification of calibration of low-cost MEMS accelerometer and its temperature compensation without accurate laboratory equipment

Khankalantary

Ranjbaran

Ebadollahi

2021

Meas. Sci. Technol.

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A nonlinear cost function is defined for field calibration of the accelerometer, using the rule that the norm of the measured vector in a static state is equal to the magnitude of the gravity vector. To solve this cost function, various optimization methods like Newton and Levenberg–Marquardt have been presented in different references. However, these methods are complicated, time-consuming, and require an initial value. This study presents a method that simplifies the cost function and obtains the error coefficients, including bias, scale factor, and non-orthogonality using the linear least-squares method which is simpler and faster than other optimization methods and does not need initial values. Also, the output of the low-cost MEMS accelerometer depends on temperature due to its silicon property. Thus, by finding the dependency of the error coefficients on temperature, they can be compensated. This paper models dependency of error coefficients on temperature using cubic spline interpolation and minimizes the temperature effect. Simulation results of MATLAB and the proposed field calibration method and temperature compensation on the low-cost MPU6050 sensor show its good performance.

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“…Currently, to improve the geometric accuracy, a high-resolution optical remote sensing satellite is usually equipped with star sensors as the attitude determination system (ADS), owing to its advantages in anti-jamming, resistance to drift with time, and higher measurement accuracy [6][7][8]. The star sensor utilizes a light detector to gather lines of sight from two or more stars in space and calculates the transformation matrix from the ADS coordinate system to the J2000 celestial coordinate system [9].…”

Section: Introductionmentioning

confidence: 99%

On-Orbit Calibration of Installation Parameter of Multiple Star Sensors System for Optical Remote Sensing Satellite with Ground Control Points

Wang

Zhu

2020

Remote Sensing

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Owing to the vibrations and thermal shocks that arise during the launch and orbit penetration process, the on-orbit installation parameters of multiple star sensors are different from the on-ground measured parameters, causing inconsistencies in the attitude determinations from different combination modes and seriously affecting the geometric accuracy of high-resolution optical remote sensing images. This study presents an on-orbit calibration approach for the installation parameters of a multiple star sensors system using ground control points (GCPs). Based on the on-ground installation parameters of the optical axes of conventional star sensors, a fiducial coordinate system is proposed as the calibration coordinate system. The installation parameters of the conventional star sensors are calibrated using the statistical characteristics of angles between axes of the star sensor and three fiducial vectors in the J2000 celestial coordinate system. Based on the GCPs, the relative fiducial parameters are calculated, and the installation parameter of unconventional star sensor is then calibrated with the relative fiducial parameters and statistical characteristics of angles. It can be used for high-resolution optical remote sensing satellite measuring with only two star sensors to unify the fiducial coordinate system. The proposed method is tested using simulated data and on-orbit measurement data. The results demonstrate that the proposed method can calibrate the optical axis of the star sensor without the restriction of the accuracy of horizontal axis. Moreover, the star sensor with a large installation angle error can be calibrated well using the proposed approach. The results of attitude determinations from different star sensor combination modes are consistent, and the geometric accuracy of the remote sensing images is significantly improved.

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An All-Parameter System-Level Calibration for Stellar-Inertial Navigation System on Ground

Cited by 44 publications

References 12 publications

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

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

Simplification of calibration of low-cost MEMS accelerometer and its temperature compensation without accurate laboratory equipment

On-Orbit Calibration of Installation Parameter of Multiple Star Sensors System for Optical Remote Sensing Satellite with Ground Control Points

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