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WO2022160391A1 - Procédé d'étalonnage de gyroscope mems assisté par des informations de magnétomètre et système d'étalonnage - Google Patents

Procédé d'étalonnage de gyroscope mems assisté par des informations de magnétomètre et système d'étalonnage Download PDF

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Publication number
WO2022160391A1
WO2022160391A1 PCT/CN2021/077089 CN2021077089W WO2022160391A1 WO 2022160391 A1 WO2022160391 A1 WO 2022160391A1 CN 2021077089 W CN2021077089 W CN 2021077089W WO 2022160391 A1 WO2022160391 A1 WO 2022160391A1
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magnetometer
mems
data
gyroscope
error
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Chinese (zh)
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徐祥
孙逸帆
李凤
陈洋豪
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Suzhou University
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Suzhou University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
    • G01C25/005—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass initial alignment, calibration or starting-up of inertial devices

Definitions

  • the invention relates to the technical field of inertial navigation systems, in particular to a MEMS gyroscope calibration method and a calibration system aided by magnetometer information.
  • the MEMS inertial device In order to ensure the accuracy of the inertial navigation system, the MEMS inertial device must be calibrated when it is used.
  • the traditional method usually uses a high-precision turntable to calibrate the gyroscope.
  • the use of a high-precision turntable to calibrate the MEMS gyroscope has problems such as high cost.
  • the output data of the MEMS accelerometer includes part of the external acceleration, and the external acceleration interferes with the calibration, which reduces the MEMS gyroscope. Calibration accuracy.
  • the technical problem to be solved by the present invention is to provide a magnetometer information-assisted MEMS gyroscope calibration method and calibration system, which utilizes the calibrated MEMS magnetometer data to calibrate the MEMS gyroscope and realizes low-cost MEMS gyroscope calibration.
  • the present invention provides a magnetometer information-assisted MEMS gyroscope calibration method, comprising the following steps:
  • the sensor real-time data includes real-time gyroscope data and magnetometer data
  • the real-time gyroscope data includes motion gyroscope data
  • the real-time gyroscope data also includes static gyroscope data; the interval between S1 and S3 also includes:
  • the noise covariance matrix in the initial conditions of the recursive least squares method is set according to the variance of the random noise of the MEMS gyroscope.
  • the interval between S1 and S3 also includes:
  • the ideal value of the MEMS gyroscope error parameter is set according to the MEMS gyroscope error parameter characteristic.
  • the interval between S1 and S3 also includes:
  • the recursive least squares method is used to iteratively estimate the parameters until the data is terminated, and the estimated value of the error parameter of the MEMS gyroscope is obtained, which specifically includes:
  • the ideal values of noise covariance matrix, error covariance matrix and MEMS gyroscope error estimation parameters are used as the iterative initial values of the recursive least squares equation, and the recursive least squares method is used to estimate the error parameters of the MEMS gyroscope until the data is terminated , to obtain an estimate of the error parameter of the MEMS gyroscope.
  • the S2 specifically includes;
  • b represents the carrier coordinate system, which represents the three-axis orthogonal coordinate system of the strapdown inertial navigation system, and its x-axis, y-axis and z-axis point to the right-front-upper of the carrier respectively;
  • s represents a non-orthogonal coordinate system, which represents a three-axis non-orthogonal coordinate system of the MEMS magnetometer, indicating that its x-axis, y-axis and z-axis have a certain angular deviation from the x-axis, y-axis and z-axis under the carrier coordinate system ;
  • n represents the navigation coordinate system, which represents the geographic coordinate system of the location of the carrier, and its three axes point to the local east, north and sky directions respectively;
  • the output model of the MEMS magnetometer in the non-orthogonal coordinate system is obtained:
  • m s represents the vector output of the MEMS magnetometer in a non-orthogonal system
  • S represents the scale factor error matrix of the MEMS magnetometer
  • C no represents the non-orthogonal error matrix of the MEMS magnetometer
  • m n represents the projection of the geomagnetic vector in the navigation coordinate system;
  • b represents the zero bias error vector of the MEMS magnetometer;
  • ⁇ represents the random noise vector of the MEMS magnetometer;
  • the maximum likelihood function F of the MEMS magnetometer is constructed under the optimal estimation model:
  • k represents the label of the measurement data, representing the kth measurement;
  • Re represents the kth vector output of the MEMS magnetometer in the non-orthogonal coordinate system;
  • S represents the scale factor error matrix of the MEMS magnetometer;
  • C no represents the non-orthogonal error matrix of the MEMS magnetometer;
  • b represents the zero bias error vector of the MEMS magnetometer;
  • ⁇ k represents the Lagrangian number;
  • the maximum likelihood function F of the MEMS magnetometer is estimated by Newton's method:
  • i represents the number of iterations of Newton's method, representing the ith iteration;
  • X (i+1) represents the estimated value of the MEMS magnetometer error parameter in the ith+1 iteration;
  • X (i) represents the ith iteration in Estimated values of MEMS magnetometer error parameters;
  • the error parameter value of the MEMS magnetometer is calculated by the Newton method, and the calibration model of the MEMS magnetometer is used for calibration:
  • m b represents the projection of the output of the MEMS magnetometer in the carrier coordinate system
  • R m represents the error compensation of the coupling term of the scale factor and the non-orthogonal error estimated by the maximum likelihood estimation method of the MEMS magnetometer matrix
  • b represents the compensation vector of the zero bias error estimated by the maximum likelihood estimation method.
  • the S3 specifically includes:
  • m b represents the projection of the geomagnetic vector in the carrier coordinate system;
  • ⁇ represents the vector product operation between vectors;
  • a column vector representing the component of the angular velocity vector of the carrier coordinate system relative to the local navigation coordinate system in the axial direction of the carrier system;
  • the recursive least squares method estimates MEMS gyroscope data errors, including:
  • K k P k
  • the S4 specifically includes:
  • R w represents the compensation matrix of the MEMS gyroscope scale factor and the non-orthogonal error coupling error;
  • d w represents the product of the MEMS gyroscope scale factor and the non-orthogonal error coupling error compensation matrix and the zero bias error vector, and ws represents the output of the MEMS gyroscope.
  • the invention discloses a MEMS gyroscope calibration system assisted by magnetometer information, comprising:
  • the data acquisition module acquires real-time sensor data
  • the sensor real-time data includes real-time gyroscope data and magnetometer data
  • the real-time gyroscope data includes motion gyroscope data
  • a magnetometer calibration module which performs calibration processing on the magnetometer data to obtain calibrated magnetometer data
  • the error parameter estimation module constructs an equation from the calibrated magnetometer data and the moving gyroscope data, uses the recursive least squares method to iteratively estimate the parameters until the data is terminated, and obtains the error parameters of the MEMS gyroscope estimated value;
  • a gyroscope calibration module performs calibration processing on the MEMS gyroscope according to the estimated value of the error parameter of the MEMS gyroscope.
  • the invention discloses a computer device, comprising a memory, a processor and a computer program stored in the memory and running on the processor, characterized in that the processor implements the steps of the above method when executing the program.
  • the present invention proposes a method for calibrating a MEMS gyroscope using a MEMS magnetometer as auxiliary information without the assistance of high-precision external equipment.
  • the present invention calibrates the MEMS magnetometer data and uses the calibrated MEMS
  • the magnetometer data is used to calibrate the MEMS gyroscope, which realizes the calibration of the low-cost MEMS gyroscope.
  • the present invention uses the full error estimation model based on the maximum likelihood method to calibrate the MEMS magnetometer, which improves the calibration accuracy of the MEMS magnetometer and further improves the calibration accuracy of the MEMS gyroscope;
  • the present invention uses the recursive least squares method to estimate the MEMS gyroscope, with high calculation accuracy and high calculation efficiency;
  • the present invention uses no high-precision external auxiliary equipment to calibrate the MEMS gyroscope, which saves the calibration cost and improves the practicability of the MEMS gyroscope.
  • Fig. 1 is the flow chart of the MEMS gyroscope calibration method aided by magnetometer information
  • Fig. 2 is the calibration bias error diagram of MEMS gyroscope
  • Fig. 3 is the first row vector error diagram of MEMS gyroscope calibration scale factor and non-orthogonal error coupling matrix
  • Fig. 4 is the error diagram of the second row vector of the MEMS gyroscope calibration scale factor and the non-orthogonal error coupling matrix
  • Fig. 5 is the third row vector error diagram of the MEMS gyroscope calibration scale factor and the non-orthogonal error coupling matrix.
  • the present invention discloses a magnetometer information-assisted MEMS gyroscope calibration method, including the following steps:
  • Step 1 Acquiring real-time sensor data, the sensor real-time data includes real-time gyroscope data and magnetometer data, and the real-time gyroscope data includes moving gyroscope data and stationary gyroscope data.
  • Step 2 Perform calibration processing on the magnetometer data to obtain the calibrated magnetometer data, which specifically includes:
  • b represents the carrier coordinate system, which represents the three-axis orthogonal coordinate system of the strapdown inertial navigation system, and its x-axis, y-axis and z-axis point to the right-front-upper of the carrier respectively;
  • s represents a non-orthogonal coordinate system, which represents a three-axis non-orthogonal coordinate system of the MEMS magnetometer, indicating that its x-axis, y-axis and z-axis have a certain angular deviation from the x-axis, y-axis and z-axis under the carrier coordinate system ;
  • n represents the navigation coordinate system, which represents the geographic coordinate system of the location of the carrier, and its three axes point to the local east, north and sky directions respectively;
  • the output model of the MEMS magnetometer in the non-orthogonal coordinate system is obtained:
  • m s represents the vector output of the MEMS magnetometer in a non-orthogonal system
  • S represents the scale factor error matrix of the MEMS magnetometer
  • C no represents the non-orthogonal error matrix of the MEMS magnetometer
  • m n represents the projection of the geomagnetic vector in the navigation coordinate system;
  • b represents the zero bias error vector of the MEMS magnetometer;
  • ⁇ represents the random noise vector of the MEMS magnetometer;
  • the maximum likelihood function F of the MEMS magnetometer is constructed under the optimal estimation model:
  • k represents the label of the measurement data, representing the kth measurement;
  • Re represents the kth vector output of the MEMS magnetometer in the non-orthogonal coordinate system;
  • S represents the scale factor error matrix of the MEMS magnetometer;
  • C no represents the non-orthogonal error matrix of the MEMS magnetometer;
  • b represents the zero bias error vector of the MEMS magnetometer;
  • ⁇ k represents the Lagrangian number;
  • the maximum likelihood function F of the MEMS magnetometer is estimated by Newton's method:
  • i represents the number of iterations of Newton's method, representing the ith iteration;
  • X (i+1) represents the estimated value of the MEMS magnetometer error parameter in the ith+1 iteration;
  • X (i) represents the ith iteration in Estimated values of MEMS magnetometer error parameters;
  • the error parameter value of the MEMS magnetometer is calculated by the Newton method, and the calibration model of the MEMS magnetometer is used for calibration:
  • m b represents the projection of the output of the MEMS magnetometer in the carrier coordinate system
  • R m represents the error compensation of the coupling term of the scale factor and the non-orthogonal error estimated by the maximum likelihood estimation method of the MEMS magnetometer matrix
  • b represents the compensation vector of the zero bias error estimated by the maximum likelihood estimation method.
  • the variance of the random noise of the MEMS gyroscope is obtained, and the noise covariance matrix in the initial condition of the recursive least squares method is set according to the variance of the random noise of the MEMS gyroscope; according to the MEMS gyroscope error parameter
  • the characteristic sets the ideal value of the MEMS gyroscope error parameter; the error covariance matrix is set according to the empirical value.
  • Step 3 the magnetometer data after calibration and the gyroscope data of motion construct equation, utilize the recursive least squares method to iteratively estimate the parameters until the data terminates, obtain the estimated value of the error parameter of the MEMS gyroscope, specifically include:
  • m b represents the projection of the geomagnetic vector in the carrier coordinate system;
  • ⁇ represents the vector product operation between vectors;
  • a column vector representing the component of the angular velocity vector of the carrier coordinate system relative to the local navigation coordinate system in the axial direction of the carrier system;
  • the recursive least squares method estimates MEMS gyroscope data errors, including:
  • K k P k
  • Step 4 Perform calibration processing on the MEMS gyroscope according to the estimated value of the error parameter of the MEMS gyroscope.
  • the recursive least squares method is used to iteratively estimate the parameters until the data is terminated, and the estimated value of the error parameter of the MEMS gyroscope is obtained, which specifically includes:
  • the ideal values of noise covariance matrix, error covariance matrix and MEMS gyroscope error estimation parameters are used as the iterative initial values of the recursive least squares equation, and the recursive least squares method is used to estimate the error parameters of the MEMS gyroscope until the data is terminated , to obtain the estimated value of the error parameter of the MEMS gyroscope, specifically including: MEMS gyroscope error calibration expression:
  • R w represents the compensation matrix of the MEMS gyroscope scale factor and the non-orthogonal error coupling error;
  • d w represents the product of the MEMS gyroscope scale factor and the non-orthogonal error coupling error compensation matrix and the zero-bias error vector;
  • ws represents the output of the MEMS gyroscope.
  • the invention discloses a MEMS gyroscope calibration system assisted by magnetometer information, which comprises a data acquisition module, a magnetometer calibration module, an error parameter estimation module and a gyroscope calibration module.
  • the data acquisition module acquires real-time sensor data, the sensor real-time data includes real-time gyroscope data and magnetometer data, and the real-time gyroscope data includes motion gyroscope data.
  • the magnetometer calibration module performs calibration processing on the magnetometer data to obtain calibrated magnetometer data.
  • the error parameter estimation module constructs an equation from the calibrated magnetometer data and the moving gyroscope data, uses the recursive least squares method to iteratively estimate the parameters until the data is terminated, and obtains the estimated value of the error parameter of the MEMS gyroscope.
  • the gyroscope calibration module performs calibration processing on the MEMS gyroscope according to the estimated value of the error parameter of the MEMS gyroscope.
  • the estimated MEMS gyroscope error parameters include two items, one is the zero bias error, and the other is the coupling term between the scale factor error and the non-orthogonal error.
  • Figure 2 shows the change of the difference between the estimated value of the zero bias error and the real value (the ideal result difference is 0) with the data iteration in the error parameters of the MEMS gyroscope, which is a 3x1 column vector (respectively x-axis, y-axis axis, z-axis direction), in the figure (a), (b), (c) respectively represent the difference between the estimated value of the zero bias error and the true value. It iterates with the data on the three axes x-axis, y-axis, and z-axis The change.
  • Figure 3 is a vector error diagram of the first row of the MEMS gyroscope calibration scale factor and the non-orthogonal error coupling matrix, wherein Figure 3 (a) shows the first row of the coupling term matrix between the scale factor and the non-orthogonal error. , the difference between the elements in the first column and the real value (simulation setting) changes with the data iteration; Figure 3(b) shows the coupling term matrix representing the scale factor and the non-orthogonal error. The first row, the difference between the elements in the second column and the real value (simulation setting) changes with the data iteration; Figure 3(c) shows the coupling term matrix representing the scale factor and the non-orthogonal error. One row, the difference between the element in the third column and the true value (simulation setting) as a function of data iteration.
  • Fig. 4 is a vector error diagram of the second row of the MEMS gyroscope calibration scale factor and the non-orthogonal error coupling matrix, in which Fig. 4(a) shows the second row of the coupling term matrix between the scale factor and the non-orthogonal error. , the difference between the elements in the first column and the real value (simulation setting) changes with the data iteration; Figure 4(b) shows the coupling term matrix representing the scale factor and the non-orthogonal error. The second row, the difference between the element in the second column and the real value (simulation setting) changes with the data iteration; Figure 4(c) shows the coupling term matrix representing the scale factor and the non-orthogonal error. The difference between the elements in the second row and the third column and the true value (simulation setting) varies with data iterations.
  • Figure 5 is a vector error diagram of the third row of the MEMS gyroscope calibration scale factor and the non-orthogonal error coupling matrix, wherein, 5(a) represents the third row of the coupling term matrix between the scale factor and the non-orthogonal error, The difference between the elements in the first column and the real value (simulation setting) changes with the data iteration; Figure 5(b) shows the coupling term matrix representing the scale factor and the non-orthogonal error. The third row , the difference between the elements in the second column and the real value (simulation setting) changes with the data iteration; Figure 5(c) shows the coupling term matrix representing the scale factor and the non-orthogonal error. The third row, the difference between the element in the third column and the true value (simulation setting) as a function of data iteration.
  • the difference between the MEMS gyroscope scale factor and the non-orthogonal error coupling term matrix obtained by the recursive least squares method and the real value (simulation setting) is minus 3 of 10 To the negative 4th power of 10, it shows that the error parameter estimation value obtained by the recursive least square method has low error and high accuracy.

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  • Manufacturing & Machinery (AREA)
  • Physics & Mathematics (AREA)
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  • Radar, Positioning & Navigation (AREA)
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  • Gyroscopes (AREA)

Abstract

L'invention concerne un procédé d'étalonnage de gyroscope MEMS assisté par des informations de magnétomètre et un système d'étalonnage. Le procédé comprend les étapes consistant à : obtenir des données en temps réel de capteur, les données en temps réel de capteur comprenant des données de gyroscope en temps réel et des données de magnétomètre, et les données de gyroscope en temps réel comprenant des données de gyroscope de mouvement ; effectuer un traitement d'étalonnage sur les données de magnétomètre pour obtenir des données de magnétomètre étalonnées ; établir une équation à l'aide des données de magnétomètre étalonnées et des données de gyroscope de mouvement, et effectuer une estimation itérative sur des paramètres en utilisant une méthode des moindres carrés récursive jusqu'à ce que les données soient terminées, de manière à obtenir une valeur estimée d'un paramètre d'erreur d'un gyroscope MEMS ; et effectuer un traitement d'étalonnage sur le gyroscope MEMS en fonction de la valeur estimée du paramètre d'erreur du gyroscope MEMS. Le gyroscope MEMS est étalonné à l'aide des données de magnétomètre MEMS étalonnées, ce qui permet d'obtenir un étalonnage de gyroscope MEMS à faible coût.
PCT/CN2021/077089 2021-01-27 2021-02-20 Procédé d'étalonnage de gyroscope mems assisté par des informations de magnétomètre et système d'étalonnage Ceased WO2022160391A1 (fr)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115406467A (zh) * 2022-11-01 2022-11-29 北京开拓航宇导控科技有限公司 一种mems陀螺自动标定方法
CN115574839A (zh) * 2022-10-08 2023-01-06 桂林电子科技大学 一种mpu9250姿态传感器误差的标定方法
CN115900770A (zh) * 2023-02-14 2023-04-04 北京理工大学前沿技术研究院 一种机载环境下磁传感器的在线校正方法和系统
CN115979311A (zh) * 2023-03-20 2023-04-18 伸瑞科技（北京）有限公司 Piga交叉二次项系数标定方法、系统、设备及介质
CN116659547A (zh) * 2023-04-27 2023-08-29 杭州华橙软件技术有限公司 陀螺仪标定方法、陀螺仪标定装置及计算机可读存储介质
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* Cited by examiner, † Cited by third party
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CN120651245B (zh) * 2025-08-12 2025-11-28 太原融盛科技有限公司 一种航行器导航中姿态监测方法及系统

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110167893A1 (en) * 2008-09-17 2011-07-14 Bae Systems Information And Electronic Systems Integration Inc. Method and apparatus for in-flight calibration of gyroscope using magnetometer reference
US20140202229A1 (en) * 2013-01-23 2014-07-24 Michael E. Stanley Systems and method for gyroscope calibration
US20150285835A1 (en) * 2013-12-23 2015-10-08 InvenSense, Incorporated Systems and methods for sensor calibration
CN107389092A (zh) * 2017-06-27 2017-11-24 上海交通大学 一种基于磁传感器辅助的陀螺标定方法
CN109696183A (zh) * 2019-01-28 2019-04-30 北京华捷艾米科技有限公司 惯性测量单元的标定方法及装置
CN110177998A (zh) * 2017-01-06 2019-08-27 Idhl控股公司 用于陀螺仪标定校准的方法、装置和系统
CN111121824A (zh) * 2020-01-03 2020-05-08 西北工业大学 一种mems传感器的标定方法

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103221788B (zh) * 2010-11-08 2016-03-09 阿尔派回放股份有限公司 陀螺仪传感器的标定设备和方法
CN104880201B (zh) * 2015-03-26 2016-01-13 武汉大学 Mems陀螺自动标定方法
CN105547326B (zh) * 2015-12-08 2018-04-06 上海交通大学 陀螺与磁传感器联合标定方法
CN109631939B (zh) * 2018-11-08 2020-07-28 湖北三江航天红峰控制有限公司 一种基于磁强计和加速度计的快速对准方法

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110167893A1 (en) * 2008-09-17 2011-07-14 Bae Systems Information And Electronic Systems Integration Inc. Method and apparatus for in-flight calibration of gyroscope using magnetometer reference
US20140202229A1 (en) * 2013-01-23 2014-07-24 Michael E. Stanley Systems and method for gyroscope calibration
US20150285835A1 (en) * 2013-12-23 2015-10-08 InvenSense, Incorporated Systems and methods for sensor calibration
CN110177998A (zh) * 2017-01-06 2019-08-27 Idhl控股公司 用于陀螺仪标定校准的方法、装置和系统
CN107389092A (zh) * 2017-06-27 2017-11-24 上海交通大学 一种基于磁传感器辅助的陀螺标定方法
CN109696183A (zh) * 2019-01-28 2019-04-30 北京华捷艾米科技有限公司 惯性测量单元的标定方法及装置
CN111121824A (zh) * 2020-01-03 2020-05-08 西北工业大学 一种mems传感器的标定方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
LI YUPING, QI MIN-HUI;WU LIAN-SHUN;XIANG KUI: "Implementation of an Orientation Estimation Algorithm Based on Complementary Kalman Filter", MEASUREMENT & CONTROL TECHNOLOGY, BEIJING CHANGCHENG HANGKONG CEKONG JISHU YANJIUSUO, CN, 18 March 2017 (2017-03-18), CN , XP055954213, ISSN: 1000-8829, DOI: 10.19708/j.ckjs.2017.03.013 *

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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