Systematic calibration method based on acceleration and angular rate measurements for fiber-optic gyro SINS

Wang, Pingping; Lu, Baofeng; Yang, Pengxiang; Chen, Feng

doi:10.1063/5.0023674

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Camberlein et al [21] designed an 18-position calibration path, which could calibrate the laser gyro within 20 min and meet the accuracy requirements of the navigation level accuracy. Wang et al [22] proposed a 19-position scheme calibration method for fiber optic gyroscope SINS based on observability analysis and considered the effect of gravity anomalies. Wang et al [23] proposed a 36-state EKF for the Fiber-optic gyroscope SINS, based on 24-position, and an iterative calibration method has been suggested to suppress calibration residuals.…”

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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“…However, in the deterministic error model, the installation error number of the gyroscope and accelerometer are all simplified to three. In [ 20 , 21 , 22 , 23 , 24 ], the installation error of ISA is also simplified. In commercial grade or tactical grade SINS, simplifying the IMU installation error can meet the requirement of system accuracy.…”

Section: Introductionmentioning

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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Distributed Cooperative Navigation for Unmanned Aerial System Based on Dynamic Priority

Liu

Zhou

2022

2022 29th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS)

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Systematic calibration method based on acceleration and angular rate measurements for fiber-optic gyro SINS

Cited by 3 publications

References 16 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

Research on IMU Calibration Model Based on Polar Decomposition

Distributed Cooperative Navigation for Unmanned Aerial System Based on Dynamic Priority

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