A new dynamic calibration method for IMU deterministic errors of the INS on the Hypersonic Cruise Vehicles

Peng, Hui; Xiong, Zhi; Wang, Rong; Liu, Jianye; Zhang, Cheng

doi:10.1016/j.ast.2013.11.005

Cited by 28 publications

(28 citation statements)

References 11 publications

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“…The gyroscope bias, scale factor error and misalignment error angle of MEMS IMU-L are modeled as the random constant [20,21].…”

Section: Angular Rate Measurement Performance Enhancement Methodsmentioning

confidence: 99%

“…In the simulation verification, the gyroscope outputs of MEMS IMU-L and IMU-H are simulated according to their error models. As the gyroscope of MEMS IMU-H has high stability and high accuracy, especially after calibration, the scale factor error and misalignment error are almost zero, it is simulated using a model with ideal output plus white noise [21]. The gyroscope of MEMS IMU-L simulation model includes bias, scale factor error, misalignment error and white noise, and the first three errors are added in the form of random constants.…”

Section: Validation and Analysismentioning

confidence: 99%

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Performance Enhancement Method for Angular Rate Measurement Based on Redundant MEMS IMUs

Qian

et al. 2019

Micromachines

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Aiming at the low-cost, wide-range, and accurate measurement requirement for Microelectromechanical System (MEMS) Inertial Measurement Unit (IMU) on a multi-rotor Unmanned Aerial Vehicle (UAV), the paper designs a heterogeneous parallel redundancy configuration scheme. In redundant MEMS IMUs, a high-cost and small-range MEMS gyroscope is combined with low-cost and large-range MEMS gyroscopes. Then, an adaptive data fusion method of redundant MEMS gyroscopes is proposed. By the designed experiments based on the simulation data and the sensor measurement data, the proposed method has been proved that it can effectively improve the angular rate measurement performance of the multi-rotor UAV and broaden the angular rate measurement range on the basis of saving the configuration cost and volume of the micro IMU.

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“…The gyroscope bias, scale factor error and misalignment error angle of MEMS IMU-L are modeled as the random constant [20,21].…”

Section: Angular Rate Measurement Performance Enhancement Methodsmentioning

confidence: 99%

Section: Validation and Analysismentioning

confidence: 99%

Performance Enhancement Method for Angular Rate Measurement Based on Redundant MEMS IMUs

Qian

et al. 2019

Micromachines

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show abstract

“…With the advancement of performance in flight distance, speed and maneuvering penetration ability, hypersonic vehicle puts forward more stringent requirements for the reliability, adaptability and accuracy of inertial navigation system (INS) [1], [2]. Due to the drift in accelerometer and gyroscope, the navigation error of INS will accumulate with time, which will seriously affect the navigation accuracy of hypersonic vehicle.…”

Section: Introductionmentioning

confidence: 99%

Identification Method of SINS Error Parameters Utilizing Artificial Neural Network for Hypersonic Vehicle

Guo

Zhang

et al. 2019

IEEE Access

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To improve the navigation accuracy of hypersonic vehicle, an error parameter identification method of strap-down inertial navigation system (SINS) based on artificial neural network is proposed. Firstly, the inertial measurement unit (IMU) error model and the SINS navigation calculation model are established, which can provide an accurate model basis for the error parameters identification. Then, four kinds of neural network structures with different inputs are constructed and optimized by the numerical simulation. To further improve the identification accuracy of the error parameters, the neural network method is improved by adjusting the inputs of the network structure and optimizing the initial network values based on gradient particle swarm optimization. The improved neural network method is eventually used to identify the SINS error parameters of hypersonic vehicle. The simulation results show that the neural network with position deviation, velocity deviation and attitude deviation as the inputs has the optimal network structure. The improved artificial neural network method has the highest identification accuracy for the error parameters compared with other methods, and the relative errors of the error parameters are less than 34%. Meanwhile, the navigation errors of the proposed method are equivalent to the magnitude of Kalman filter algorithm, which demonstrates the effectiveness of our method to identify the high-precision SINS error parameters and improve the navigation accuracy for hypersonic vehicle.

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“…For strap-down Inertial Navigation System (SINS), an inertial measurement unit (IMU) is directly installed in the vehicle. Thus, the navigation precision of SINS depends on the measuring accuracy of IMU [ 1 , 2 , 3 , 4 , 5 , 6 ]. In practical application, installation of accelerometer and gyro is not orthogonal completely, and the installation error will arise.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Calibration of Strap-Down Tri-Axis Accelerometer Unit

Zhang

Qin

et al. 2017

Micromachines

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This paper proposes a comprehensive calibration method to improve the precision of a strap-down tri-axis accelerometer unit, in which parameters are divided into static and dynamic ones. The contribution of the manuscript is that it solves the problem of inappropriate installation and the size effect error for tri-axis accelerometer unit at high speed by using static and dynamic calibration method, respectively. The experiment results show the measuring accuracy of accelerometers is increased by more than one order of magnitude, and the navigation precision is increased by more than two orders of magnitude.

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A new dynamic calibration method for IMU deterministic errors of the INS on the Hypersonic Cruise Vehicles

Cited by 28 publications

References 11 publications

Performance Enhancement Method for Angular Rate Measurement Based on Redundant MEMS IMUs

Performance Enhancement Method for Angular Rate Measurement Based on Redundant MEMS IMUs

Identification Method of SINS Error Parameters Utilizing Artificial Neural Network for Hypersonic Vehicle

Comprehensive Calibration of Strap-Down Tri-Axis Accelerometer Unit

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