Hemispherical resonator gyroscope gradient magnetic field interference error compensation method

Abstract

The invention relates to the technical field of gyroscopes, in particular to a hemispherical resonator gyroscope gradient magnetic field interference error compensation method which comprises the following steps: step 1, acquiring a triaxial magnetic field intensity component of a gyroscope under a carrier coordinate system through a magnetometer; step 2, acquiring a three-axis acceleration measurement value of the gyroscope under the carrier coordinate system through an accelerometer; 3, constructing an extended Kalman filter by taking navigation information as measurement input, and performing fusion calculation on the three-axis magnetic field intensity component in the step 1 and the three-axis acceleration measurement value in the step 2 through the extended Kalman filter to obtain compensation output of the magnetometer; an inertial navigation module is in signal connection with a magnetometer and an accelerometer, a triaxial magnetic field intensity component and a triaxial acceleration measurement value are acquired, then an extended Kalman filter is used for carrying out fusion calculation on the data, and compensation output is used for correcting an attitude angle of a gyroscope, so that the influence of gradient magnetic field interference on gyroscope output is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of gyroscopes, and specifically refers to a method for compensating the interference error of the gradient magnetic field of a hemispherical resonator gyroscope. Background Art

[0002] A hemispherical resonator gyroscope (HRG) is a high-precision inertial navigation instrument that uses a resonant structure to measure angular velocity. In gyroscopes, magnetic materials are usually used to form the resonator. When a fluctuating magnetic field is introduced, it will affect the magnetization state of these magnetic materials. In addition, due to the change of the magnetic field caused by the external complex environment, it will cause a change in the magnetic moment in the resonator, and further will cause an interference error of the magnetic field.

[0003] It should be noted that due to the installation characteristics of the hemispherical resonator gyroscope itself, there must be an influence on the magnetic field around it. Therefore, it is necessary to calibrate the error and compensate for the interference of the gyroscope. In this process, a magnetometer is usually used. In addition, due to the limitation of the hard magnetic materials of the gyroscope system itself, even after the external magnetic field disappears, it can maintain a certain magnetic field strength, and the generated magnetic field strength is relatively stable and has obvious gradient characteristics, which will cause an interference error to the output of the magnetometer; and then affect the output of the hemispherical resonator gyroscope.

[0004] Therefore, there is an urgent need for a method for compensating the interference error of the gradient magnetic field of a hemispherical resonator gyroscope to compensate for the interference error of the gradient magnetic field. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for compensating the interference error of the gradient magnetic field of a hemispherical resonator gyroscope, which is used to compensate for the interference error of the gradient magnetic field.

[0006] The present invention is realized through the following technical solutions: A method for compensating the gradient magnetic field interference error of a hemispherical resonant gyroscope, including connecting an inertial navigation module with a magnetometer and an accelerometer signals, and further including the following steps: Step 1, collecting the three-axis magnetic field intensity components of the gyroscope in the carrier coordinate system through the magnetometer; Step 2, collecting the three-axis acceleration measurement values of the gyroscope in the carrier coordinate system through the accelerometer; Step 3, constructing an extended Kalman filter with the navigation information as the measurement input, and performing fusion calculation on the three-axis magnetic field intensity components in Step 1 and the three-axis acceleration measurement values in Step 2 through the extended Kalman filter to obtain the compensation output of the magnetometer; wherein, the three-axis magnetic field intensity components in Step 1 carry the carrier magnetic field interference amount; the inertial navigation module is used to collect the navigation information of the gyroscope, and the fusion calculation process of the extended Kalman filter includes a state prediction process and a measurement update process. The state prediction process is used to predict the predicted state of the gyroscope at the next moment, and the measurement update process is used to perform calculation on the magnetometer to obtain the compensation output of the magnetometer.

[0007] Compared with the prior art, the present invention has the following advantages and beneficial effects: 1. In the present invention, by connecting the inertial navigation module with the magnetometer and the accelerometer signals, collecting the three-axis magnetic field intensity components and the three-axis acceleration measurement values, and then using the extended Kalman filter to perform fusion calculation on these data to obtain the compensation output of the magnetometer, the above compensation output is used to correct the attitude angle of the gyroscope, thereby reducing the influence of the gradient magnetic field interference on the output of the gyroscope; 2. In the present invention, the nonlinear function is linearized at each time step, and the nonlinear problem is transformed into a linear problem for processing. In the state prediction step, the extended Kalman filter uses the Jacobian matrix to approximate the local linear behavior of the nonlinear function. The Jacobian matrix is a matrix that describes the change rate of the nonlinear function with respect to each component of the state vector, enabling the extended Kalman filter to effectively predict the state of the system at each time step; 3. The present invention not only considers the dynamic behaviors that are not fully captured by the model, such as external shocks, temperature changes, etc., but also by introducing process noise, the state prediction equation can better simulate the random disturbances in the actual system, improving the robustness of the system. Description of the Drawings

[0008] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, and constitute a part of the present invention, but do not constitute a limitation to the embodiments of the present invention. In the drawings: Figure 1 is a schematic flow chart of the method of the present invention. Detailed Embodiments

[0009] To make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to embodiments and the accompanying drawings. The illustrative embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention. It should be noted that the present invention has been in the actual R & D and use stage.

[0010] Embodiment 1: As shown in the Figure 1 accompanying drawings, a method for compensating the gradient magnetic field interference error of a hemispherical resonant gyroscope includes signal - connecting an inertial navigation module to a magnetometer and an accelerometer, and further includes the following steps: Step 1: Collect the three - axis magnetic field intensity components of the gyroscope in the carrier coordinate system through the magnetometer; Step 2: Collect the three - axis acceleration measurement values of the gyroscope in the carrier coordinate system through the accelerometer; Step 3: Use the navigation information as the measurement input to construct an extended Kalman filter, and through the extended Kalman filter, fuse and calculate the three - axis magnetic field intensity components in Step 1 and the three - axis acceleration measurement values in Step 2 to obtain the compensated output of the magnetometer; Among them, the three - axis magnetic field intensity components in Step 1 carry the carrier magnetic field interference amount; The inertial navigation module is used to collect the navigation information of the gyroscope. The fusion and calculation process of the extended Kalman filter includes a state prediction process and a measurement update process. The state prediction process is used to predict the predicted state of the gyroscope at the next moment, and the measurement update process is used to calculate the magnetometer to obtain the compensated output of the magnetometer.

[0011] The state prediction process includes: Select the deviation of the magnetometer caused by the zero - bias of the magnetometer itself and the carrier magnetic field interference amount as the state vector to construct a state prediction equation, and predict the predicted state of the gyroscope at the next moment through the state prediction equation; The measurement update process includes: Use the navigation information and the heading difference calculated by the magnetometer as the observation quantity to construct an observation equation, and introduce the components of the deviation of the magnetometer in its three axes into the observation equation, and then calculate the magnetometer to obtain the compensated output of the magnetometer. The compensated output of the magnetometer is used to correct the attitude angle of the gyroscope.

[0012] It should be noted that the carrier coordinate system refers to the own coordinate system of the carrier (platform, aircraft, UAV, etc.) equipped with the hemispherical resonant gyroscope, that is, the body coordinate system. In the inertial navigation system, all sensor data (gyroscope, accelerometer, magnetometer) are first collected and preliminarily fused in this coordinate system. Therefore, the three - axis magnetic field intensity components and acceleration measurement values in the carrier coordinate system both refer to the measurement values in the three directions in this body coordinate system.

[0013] It should be noted that due to the limitation of the hard magnetic material of the gyroscope system itself, it can maintain a certain magnetic field intensity even after the external magnetic field disappears. The generated magnetic field intensity is relatively stable and has obvious gradient characteristics, which will cause interference errors to the output of the magnetometer, and further affect the output of the hemispherical resonator gyroscope. Based on the above problems, the applicant proposes a method for compensating the gradient magnetic field interference error of a hemispherical resonator gyroscope. By connecting the inertial navigation module with the magnetometer and accelerometer signals, the three-axis magnetic field intensity components and the three-axis acceleration measurement values are collected, and then the extended Kalman filter is used to fuse and calculate these data to obtain the compensated output of the magnetometer, which is used to correct the attitude angle of the gyroscope, thereby reducing the influence of the gradient magnetic field interference on the gyroscope output.

[0014] More specifically, since the hemispherical resonator gyroscope is sensitive to magnetic field changes, the changes in the magnetic field (including the carrier magnetic field interference of the gyroscope itself and the external gradient magnetic field interference) will affect the measured angular velocity, and thus introduce errors. The three-axis magnetic field intensity components measured by the magnetometer include the magnetic field intensities of the X, Y, and Z axes. In the direct measurement values of the magnetometer, the self-deviation is already included. When the magnetometer has magnetic field interference errors, the output of the gyroscope will inevitably be affected accordingly. Therefore, on this basis, it is necessary to compensate for the interference errors of the gyroscope.

[0015] Secondly, the three-axis magnetic field intensity components of the gyroscope in the carrier coordinate system are collected by the magnetometer. This data reflects the magnetic field conditions of the environment where the gyroscope is located, including the magnetic fields generated by the external environment and the carrier itself. At the same time, the accelerometer is used to collect the three-axis acceleration measurement values of the gyroscope in the carrier coordinate system. Then, these collected data are used as inputs, combined with the navigation information of the gyroscope, to construct an extended Kalman filter. In the extended Kalman filter, during the state prediction process, the state transition matrix and the process noise vector are used to predict the predicted state of the gyroscope at the next moment. This process takes into account the dynamic characteristics and internal noise of the system. Subsequently, during the measurement update process, the actual observed data, such as the navigation information and the heading difference calculated by the magnetometer, are used to correct the predicted state. In this process, the observation matrix maps the state vector to the observation space, and the Kalman gain matrix determines how to combine the predicted state and the observed value to obtain the optimal state estimate, thereby realizing the precise fusion calculation of the three-axis magnetic field intensity components and the three-axis acceleration measurement values of the gyroscope.

[0016] Through the iterative process of the aforementioned state prediction and measurement update, the extended Kalman filter can continuously adjust and optimize the estimation of the gyroscope state, thereby obtaining the compensated output of the magnetometer, which can be directly used to correct the attitude angle of the gyroscope to eliminate or reduce the influence of gradient magnetic field interference. Specifically, the compensated output adjusts the attitude angle of the gyroscope through the rotation matrix and the angular velocity vector. This adjustment process takes into account the integration time interval, the attitude angle obtained by integrating the angular velocity of the gyroscope, and the compensated output of the magnetometer. In this way, the attitude angle of the gyroscope is corrected, thereby improving the accuracy and reliability of the gyroscope output.

[0017] The method involved in this embodiment can process data in real time, provide continuous attitude estimation, and meet the output requirements of real-time gyroscopes. In addition, this embodiment also has good adaptability, can adapt to dynamic magnetic field environments, and provide accurate navigation information. This adaptability enables the gyroscope to maintain high performance under different operating conditions, whether in complex scenarios such as urban canyons or in open scenarios, and can work effectively.

[0018] Embodiment 2: In this embodiment, only the parts different from Embodiment 1 are described. Specifically, in Step 1, the three-axis magnetic field intensity components of the magnetometer are measurement values and satisfy: ; where are the three-axis magnetic field intensity components of the magnetometer; is the deviation of the magnetometer; are the three-axis geomagnetic field intensities of the gyroscope in the carrier coordinate system; is the measurement noise.

[0019] It should be noted that the three-axis magnetic field intensity components of the magnetometer are expressed as measurement values, including the deviation of the magnetometer itself, the three-axis geomagnetic field intensities of the gyroscope in the carrier coordinate system, and the measurement noise. This process enables more accurate identification and quantification of various error sources in magnetometer measurements, providing a basis for subsequent error compensation. Specifically, the compensation process begins with preprocessing the data collected by the magnetometer. In this step, the deviation of the magnetometer is identified through a mathematical model to separate the magnetic field changes caused by the external environment. Then, using these data and combining with the navigation information of the gyroscope, an extended Kalman filter is constructed. The extended Kalman filter is an effective algorithm that can achieve optimal estimation of the state in a nonlinear system. In this process, the extended Kalman filter estimates and corrects the attitude angle of the gyroscope in real time through two key steps: state prediction and measurement update.

[0020] In the state prediction step, the extended Kalman filter uses the state estimate and control input at the previous moment to predict the state of the system at the current moment. Subsequently, in the measurement update step, the extended Kalman filter uses actual observation data, such as the measurement values of the magnetometer and accelerometer, to correct the predicted state. Through this iterative process, the extended Kalman filter can continuously adjust the estimate of the gyroscope state, thereby obtaining a more accurate attitude angle estimate; this estimate result, combined with the compensated output of the magnetometer, is used to correct the attitude angle of the gyroscope to eliminate or reduce the influence of gradient magnetic field interference. This correction takes into account the biases and noises in the magnetometer measurements, as well as the magnetic field interference caused by external environmental changes, thereby providing a more comprehensive and accurate compensation scheme.

[0021] Embodiment 3: In this embodiment, only the parts different from Embodiment 1 are described. Specifically, the state prediction process satisfies ; where is the state vector; are the components of the bias of the magnetometer on the X, Y, and Z axes respectively; represents the transpose of the vector.

[0022] The state prediction equation is: ; where At time step moment, based on time step the predicted state vector; is the state transition matrix; At time step moment, based on time step the estimate of the state vector; is the process noise vector.

[0023] It should be noted that in this embodiment, the core of state prediction lies in constructing an accurate state prediction equation. This equation can describe the behavior of the gyroscope under various interferences and predict its future state. The starting point of the state prediction process is the current state vector of the gyroscope, which contains all the necessary information to describe the dynamic behavior of the gyroscope, such as angular velocity, attitude angle, etc. This information is collected in real time by the gyroscope and accelerometer and input into the state prediction equation as part of the state vector. The form of the state prediction equation is usually a non - linear function. According to the dynamic model of the system, combined with the current state vector and possible control inputs, it predicts the state at the next moment. In this embodiment, this equation is further refined to include the components of the biases of the magnetometer on the X, Y, and Z axes. These biases are jointly caused by the zero - bias of the magnetometer itself and the carrier magnetic field interference. This refinement enables the state prediction equation to more accurately simulate the behavior of the gyroscope in the actual environment, thereby improving the accuracy of the prediction.

[0024] To handle the non - linear problem in the state prediction equation, the extended Kalman filter technique is adopted in this embodiment. By linearizing the non - linear function at each time step, the non - linear problem is transformed into a linear problem for processing. In the state prediction step, the extended Kalman filter uses the Jacobian matrix to approximate the local linear behavior of the non - linear function. The Jacobian matrix is a matrix that describes the rate of change of the non - linear function with respect to each component of the state vector, which enables the extended Kalman filter to effectively predict the state of the system at each time step.

[0025] Another key factor in the state prediction equation is the state transition matrix, which describes the evolution law of the system state over time. The state transition matrix takes into account the physical characteristics and working environment of the gyroscope, such as the attitude angle obtained by integrating the angular velocity, and possible process noise. Process noise is a random variable that describes the uncertainty of the system model. It not only considers the dynamic behavior not fully captured by the model, such as external shocks, temperature changes, etc. By introducing process noise, the state prediction equation can better simulate the random disturbances in the actual system and improve the robustness of the system.

[0026] In the state prediction process, the extended Kalman filter is also involved in predicting the state covariance matrix, which describes the distribution of the uncertainty of the state estimate. The prediction of the state covariance matrix is achieved by adding the product of the current state covariance matrix and the Jacobian matrix and the process noise covariance matrix. This process takes into account the uncertainty in the prediction process and provides the necessary information for the subsequent measurement update step.

[0027] Embodiment 4: In this embodiment, only the parts different from Embodiment 1 are described. Specifically, the observation equation satisfies: ; Among them, is the observable quantity, that is, the three-axis magnetic field strength components and the three-axis acceleration measurement values; is the observation matrix; is the state vector; is the observation noise.

[0028] The observation matrix satisfies ; are the row vectors of the observation matrix respectively; Among them, ; Among them, ; represents the partial derivative; are the three-axis magnetic field strength components respectively; are intermediate variables respectively. Specifically, After transforming the three-axis magnetometer observations through the attitude angles (specifically the pitch angle and roll angle), an intermediate expression for constructing the observation model is constructed. Among them, L represents the difference component after the y and z components of the magnetometer are rotated around the roll angle, that is, the lateral magnetic field information projected by the body roll angle; N represents the combined component of the three-axis components of the magnetometer in the x-axis direction of the body after being rotated around the roll and pitch angles, that is, the heading component after attitude compensation; M is used to construct a scaling factor so that the finally output angle or direction difference falls into a reasonable linear observation domain; represents the roll angle; represents the pitch angle.

[0029] The measurement update process satisfies: ; Among them, is the state after measurement update; is the Kalman filter gain matrix.

[0030] It should be noted that the actual measurement values of the magnetometer and accelerometer are used as the observation quantities, and an observation matrix H considering the magnetometer bias is constructed to achieve accurate estimation of the state (especially the magnetometer bias and attitude). Specifically, the observation quantities are the actual sensor observation values, and the observation equation describes the relationship between the state vector and the observation data. During the measurement update process, the observation matrix is a matrix that maps the state vector to the observation space and describes how the state vector affects the observation data. The observation matrix contains magnetometer biases, which are jointly caused by the zero bias of the magnetometer itself and the carrier magnetic field interference. This detailed definition enables the observation matrix H to more accurately simulate the errors in the actual observation process, thereby improving the accuracy of state estimation.

[0031] The Kalman gain matrix is the coefficient in the extended Kalman filter used to determine how to combine the predicted state and the observation value to obtain the optimal state estimation. The calculation of the Kalman gain matrix takes into account the observation noise and the uncertainty of state prediction, and achieves the optimal state estimation by minimizing the error covariance. The calculation of the Kalman gain matrix involves the observation noise covariance matrix R and the prediction error covariance matrix, which describe the uncertainties in the observation process and the prediction process. During the measurement update step, the extended Kalman filter uses the Kalman gain matrix to adjust the predicted state to make it closer to the actual state. In this way, the extended Kalman filter can continuously adjust the estimation of the gyroscope state, thereby obtaining a more accurate attitude angle estimation. This estimation result, combined with the compensated output of the magnetometer, is used to correct the attitude angle of the gyroscope to eliminate or reduce the influence of the gradient magnetic field interference.

[0032] Embodiment 5: In this embodiment, only the parts different from Embodiment 1 are described. Specifically, the correction process of the attitude angle of the gyroscope by the compensated output of the magnetometer satisfies: ; ; where is the rotation matrix; is the angular velocity vector; is the integration time interval; is the corrected attitude angle; is the attitude angle obtained by integrating the gyroscope angular velocity; is the compensated output of the magnetometer.

[0033] It should be noted that in the state prediction step, the Extended Kalman Filter uses the state estimate and control input at the previous moment to predict the state of the system at the current moment. In the measurement update step, the Extended Kalman Filter uses the actual observed data, such as the measurement values of the magnetometer and accelerometer, to correct the predicted state. Through this iterative process, the Extended Kalman Filter can continuously adjust the estimation of the gyroscope state, thereby obtaining a more accurate attitude angle estimation. This estimation result, combined with the compensated output of the magnetometer, is used to correct the attitude angle of the gyroscope to eliminate or reduce the influence of gradient magnetic field interference. Specifically, the compensated output of the magnetometer adjusts the attitude angle of the gyroscope through the rotation matrix and angular velocity vector. This adjustment process takes into account the integration time interval, the attitude angle obtained by integrating the gyroscope angular velocity, and the compensated output of the magnetometer. In this way, the attitude angle of the gyroscope is corrected, thereby improving the accuracy and reliability of the gyroscope output.

[0034] Embodiment 6: In this embodiment, only the parts different from Embodiment 1 are described. Specifically, the difference between the observed quantity and the heading calculated from the navigation information and the magnetometer satisfies: ; Wherein, ; is the navigation information; is the heading calculated from the magnetometer.

[0035] It should be noted that by constructing a comprehensive observation model, the measurement values of the magnetometer and accelerometer are combined with the navigation information of the gyroscope to calculate the heading difference. This heading difference is the difference between the heading calculated from the magnetometer and the heading in the navigation information, which reflects the influence of magnetic field interference on the gyroscope measurement. By accurately measuring this difference, the system can identify the specific influence of magnetic field interference on the gyroscope output and make compensations accordingly.

[0036] In this process, the Extended Kalman Filter is used to handle the state estimation problem in a nonlinear system. The Extended Kalman Filter transforms the nonlinear problem into a linear problem by linearizing the nonlinear function at each time step. In the state prediction step, the Extended Kalman Filter uses the state estimate and control input at the previous moment to predict the state of the system at the current moment. In this step, the accuracy of the system dynamic model is crucial, which determines the accuracy of the prediction. Subsequently, in the measurement update step, the Extended Kalman Filter uses the actual observed data, such as the measurement values of the magnetometer and accelerometer, to correct the predicted state. In this step, the observation matrix and the Kalman gain matrix play a core role, which determine how to effectively fuse the observed data into the state estimation.

[0037] The technical solution of this embodiment further optimizes the design of the Kalman filter, especially when dealing with the heading difference between the observed quantity and the navigation information and the calculation of the magnetometer. By accurately modeling and processing these differences, the system can more accurately estimate the impact of magnetic field interference on the output of the gyroscope and compensate accordingly. This method not only improves the measurement accuracy of the gyroscope, but also improves the reliability and stability of the entire navigation system.

[0038] The specific embodiments described above have further elaborated on the purpose, technical solution and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for compensating the gradient magnetic field interference error of a hemispherical resonator gyroscope, including connecting the inertial navigation module with the signals of a magnetometer and an accelerometer, characterized in that: It further includes the following steps: Step 1: Collect the three-axis magnetic field intensity components of the gyroscope in the carrier coordinate system through a magnetometer; Step 2: Collect the three-axis acceleration measurement values of the gyroscope in the carrier coordinate system through an accelerometer; Step 3: Construct an extended Kalman filter with the navigation information as the measurement input, and perform fusion calculation on the three-axis magnetic field intensity components in Step 1 and the three-axis acceleration measurement values in Step 2 through the extended Kalman filter to obtain the compensated output of the magnetometer; Among them, the three-axis magnetic field intensity components in Step 1 carry the carrier magnetic field interference amount; The inertial navigation module is used to collect the navigation information of the gyroscope. The fusion calculation process of the extended Kalman filter includes a state prediction process and a measurement update process. The state prediction process is used to predict the predicted state of the gyroscope at the next moment, and the measurement update process is used to calculate the compensated output of the magnetometer.

2. The method for compensating the gradient magnetic field interference error of a hemispherical resonant gyroscope according to claim 1, characterized in that: The state prediction process includes: Selecting the deviation of the magnetometer caused by the zero bias of the magnetometer itself and the carrier magnetic field interference amount as the state vector to construct a state prediction equation, and predicting the predicted state of the gyroscope at the next moment through the state prediction equation; The measurement update process includes: Constructing an observation equation with the navigation information and the course difference calculated by the magnetometer, and introducing the components of the deviation of the magnetometer in its three axes into the observation equation to calculate the compensated output of the magnetometer. The compensated output of the magnetometer is used to correct the attitude angle of the gyroscope.

3. A method for compensating the gradient magnetic field interference error of a hemispherical resonant gyroscope according to claim 1, characterized in that: In Step 1, the three-axis magnetic field intensity components of the magnetometer are measurement values and satisfy: ； Among them, are the three-axis magnetic field intensity components of the magnetometer; is the deviation of the magnetometer; The three-axis geomagnetic field intensity of the gyroscope in the carrier coordinate system; For measuring noise.

4. A method for compensating the gradient magnetic field interference error of a hemispherical resonator gyroscope according to claim 1, characterized in that: The state prediction process satisfies that, ; Among them, is the state vector; They are the components of the magnetometer bias on the X, Y, and Z axes respectively; Denotes the transpose of a vector.

5. A method for compensating the gradient magnetic field interference error of a hemispherical resonant gyroscope according to claim 4, characterized in that: The state prediction equation is as follows: ; Among them, at time step at the moment, based on the time step predicted state vector; is the state transition matrix; For the estimation of the state vector at time step at time instant, based on the state vector at time step ; is the process noise vector.

6. A method for compensating the gradient magnetic field interference error of a hemispherical resonant gyroscope according to claim 2, characterized in that: The observation equation satisfies: ； Among them, is the observable quantity, that is, the three-axis magnetic field intensity component and the three-axis acceleration measurement value; is the observation matrix; is the state vector; is the observation noise.

7. The method for compensating the gradient magnetic field interference error of a hemispherical resonant gyroscope according to claim 6, characterized in that: The observation matrix satisfies ; They are respectively the row vectors of the observation matrix; Among them, ； Among them, ; denotes the partial derivative; They are the components of the magnetometer bias on the X, Y, and Z axes, respectively; They are the three-axis magnetic field strength components respectively; They are intermediate variables respectively; Indicates the roll angle; Indicates the pitch angle.

8. A method for compensating the gradient magnetic field interference error of a hemispherical resonant gyroscope according to claim 6, characterized in that: The measurement update process satisfies: ； Among them, is the state after measurement update; is the Kalman filter gain matrix.

9. A method for compensating the gradient magnetic field interference error of a hemispherical resonant gyroscope according to claim 6, characterized in that: The process of correcting the attitude angle of the gyroscope by the compensated output of the magnetometer satisfies: ； ； Among them, is a rotation matrix; is the angular velocity vector; is the integration time interval; is the corrected attitude angle; is the attitude angle obtained by integrating the angular velocity of the gyroscope; Is the compensated output of the magnetometer.

10. A method for compensating the gradient magnetic field interference error of a hemispherical resonant gyroscope according to claim 6 or 8, characterized in that: The observed quantity, the navigation information and the course difference calculated by the magnetometer satisfy: ； Among them, ; is navigation information; The heading calculated for the magnetometer.

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