MEMS three-axis gyroscope thermal drift error calibration method based on segmented calibration

Abstract

The invention discloses an MEMS three-axis gyroscope thermal drift error calibration method based on segmented calibration, which comprises the following steps: forming an inertial system by using a three-axis accelerometer and a gyroscope, rotating an inertial unit according to a designed rotation scheme by using a low-cost indexing mechanism, and collecting data of the three-axis gyroscope and the accelerometer in a temperature rising process; and carrying out segmentation processing on rotation data in the temperature rise process, calibrating each piece of segmented data in a proposed 24-dimensional system state, and finally obtaining a zero-offset temperature drift curve of the MEMS gyroscope and a temperature drift curve of a non-orthogonal transformation matrix component. According to the method, the application precision of the MEMS three-axis gyroscope is improved, the calibration cost is reduced, and the positioning precision of intelligent mobile terminals such as an unmanned aerial vehicle and a mobile robot can be improved.

Description

Technical Field

[0001] The invention belongs to the technical field of inertial sensor calibration, and in particular relates to a thermal drift error calibration method of a MEMS three-axis gyroscope based on segmented calibration. Background Art

[0002] As a component of the micro-inertial unit, the MEMS three-axis gyroscope is the core part of the attitude estimation of the moving object. Therefore, the measurement accuracy of the MEMS three-axis gyroscope has a significant impact on the accuracy of attitude estimation and navigation positioning. The errors that affect the measurement accuracy of the MEMS three-axis gyroscope include scale factor error, zero bias, non-orthogonal error, and thermal drift error of scale factor and zero bias (measured in temperature, also known as temperature drift error). The traditional method uses a temperature-controlled rate turntable to obtain accurate temperature drift curves of the scale factor and zero bias of the MEMS three-axis gyroscope, but the cost is high. In order to reduce the calibration cost and obtain MEMS three-axis gyroscope data that meets the application, the current method is to form an inertial system with the MEMS three-axis gyroscope and the accelerometer, and then collect the rotation data of the approximate constant temperature point in the constant temperature box, and then apply the system-level calibration method to obtain the parameters at the constant temperature point. After obtaining the parameters at different constant temperature points, the interpolation or fitting method is applied to obtain the gyroscope parameters at smaller interval temperatures. The parameter accuracy at the constant temperature points obtained by these methods is reliable, but the parameter accuracy between the constant temperature points depends on the constant temperature point interval and the waveform of the parameter temperature drift error. Summary of the invention

[0003] The purpose of the present invention is to provide a method for calibrating the thermal drift error of a MEMS three-axis gyroscope based on segmented calibration, which processes the rotation data of the heating process in segments, applies a system-level calibration method based on 24-dimensional states to calibrate each segmented data, and finally obtains the zero-bias temperature drift curve of the MEMS gyroscope and the temperature drift curve of the non-orthogonal transformation matrix component. The present invention improves the application accuracy of the MEMS three-axis gyroscope and reduces the calibration cost, which is conducive to improving the positioning accuracy of intelligent mobile terminals such as drones and mobile robots.

[0004] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0005] The present invention provides a method for calibrating the thermal drift error of a MEMS three-axis gyroscope based on segmented calibration, comprising:

[0006] Collect the rotation data of the inertial unit composed of MEMS three-axis gyroscope and three-axis accelerometer during the heating process;

[0007] Performing segment processing on the collected rotation data;

[0008] Perform system-level calibration based on 24-dimensional state on each segmented data to obtain calibration parameters corresponding to each segmented data;

[0009] The calibration parameters of each segmented data are fused and smoothed to obtain a rough calibration temperature drift curve;

[0010] The bias error in the rough calibration temperature drift curve is removed to obtain the zero bias temperature drift curve of the MEMS three-axis gyroscope and the temperature drift curve of the non-orthogonal transformation matrix component.

[0011] Furthermore, the acquisition of rotation data of the inertial unit composed of the MEMS three-axis gyroscope and the three-axis accelerometer during the heating process includes:

[0012] An inertial unit consisting of a MEMS three-axis gyroscope and a three-axis accelerometer is fixed on a dual-axis rotating tool and placed in a temperature chamber;

[0013] A preconfigured inertial unit rotation scheme is used to control the dual-axis rotation tool to drive the inertial unit to rotate, so that the X, Y, and Z axes of the inertial unit rotate around the horizontal direction respectively until the rotation data collection is completed; during the rotation of the inertial unit, the temperature in the temperature box is controlled to rise at a uniform speed, and the rotation data of the heating process is collected.

[0014] Furthermore, during the heating process, the temperature variation range between the lowest temperature and the highest temperature is greater than 20°C.

[0015] Furthermore, during the heating process, the temperature rising rate is in the range of 0.5°C to 3°C per minute.

[0016] Furthermore, the pre-configured inertial unit rotation scheme is:

[0017] The rotation is performed for one cycle as follows:

[0018] A. Initially, the Z axis is facing upwards, and the position is static for 5 seconds. Then the position is rotated 90 degrees counterclockwise around the X axis, and the position is static for another 5 seconds.

[0019] B. Rotate 180 degrees counterclockwise around the X axis, rotate 180 degrees clockwise, and then rotate 180 degrees counterclockwise, and then rest for 5 seconds;

[0020] C. Rotate 90 degrees counterclockwise around the X axis and keep still for 5 seconds;

[0021] D. Rotate 90 degrees counterclockwise around the Y axis and keep still for 5 seconds;

[0022] E. Rotate 180 degrees clockwise around the Y axis, 180 degrees counterclockwise, and then 180 degrees clockwise, and then rest for 5 seconds;

[0023] F. Rotate 90 degrees counterclockwise around the Y axis and rest for 5 seconds; rotate 90 degrees clockwise around the X axis and rest for 5 seconds; rotate 90 degrees counterclockwise around the Z axis and rest for 5 seconds;

[0024] G. Rotate 180 degrees clockwise around the Z axis, 180 degrees counterclockwise, and then 180 degrees clockwise, and then rest for 5 seconds;

[0025] H. Rotate 90 degrees counterclockwise around the Z axis and keep still for 5 seconds; rotate 90 degrees counterclockwise around the X axis, return to the initial position, and keep still for 5 seconds;

[0026] During the rotation process, it is set that the rotation of 90 degrees takes 3 seconds, and the rotation of 180 degrees takes 9 seconds.

[0027] Furthermore, the rotation data collected during the heating process is processed in sections by using an interval sliding method;

[0028] In the segmentation, the segment length is set to 300 seconds, and the interval time between the starting points of adjacent data segments is set to 20 seconds.

[0029] Furthermore, the system-level calibration based on the 24-dimensional state is performed on each segmented data to obtain the calibration parameters corresponding to each segmented data, including:

[0030] Establishing the temperature drift model of triaxial gyroscope data V g (T) and error compensation conversion model as follows:

[0031]

[0032]

[0033] Among them, at temperature T, is the scaling factor matrix, is the sensor coordinate system projection matrix, is the theoretical inertial unit angular velocity, ε g (T) is the zero bias in the sensor coordinate system, and is the zero bias obtained by the six-position parameter correction method. is the gyro bias in the target orthogonal system, is the estimated non-orthogonal transformation matrix, w g is a random noise with approximately zero mean, the superscript g is the sensor coordinate system, and b is the orthogonal coordinate system.

[0034] Based on the three-axis gyroscope data temperature drift model and error compensation conversion model, the measurement error of the gyroscope is obtained:

[0035]

[0036]

[0037] in, for The error matrix δK g The weight of I is the identity matrix, where i is the row number, j is the column number, and 0 is the 1×3 is a vector with 1 row and 3 columns, all of which are 0. b To load the system gyro zero bias;

[0038] The 24-dimensional state is obtained by using the system-level calibration method in, It is a 15-dimensional state vector composed of attitude error, velocity error, position error, gyro bias, and accelerometer bias.

[0039] Furthermore, the calibration parameters of each segmented data are fused and smoothed to obtain a rough calibration temperature drift curve, including:

[0040] The fusion operation is as follows:

[0041]

[0042] in, represents the calibration parameter sequence of the jth segment data, for The corresponding temperature series, The temperature series A sequence of non-repeating temperature values ​​in ;

[0043] The smoothing process is: using a wavelet filtering algorithm to smooth the fused calibration parameter curve to obtain a rough calibration temperature drift curve T k is the temperature corresponding to the kth segment data.

[0044] Further, removing the bias error in the rough calibration temperature drift curve includes:

[0045] Control the thermostat at a constant temperature T c , based on the pre-configured inertial unit rotation scheme, the dual-axis rotation tool is controlled to rotate the inertial unit so that the X, Y, and Z axes of the inertial unit rotate around the horizontal direction respectively, and the rotation data is collected;

[0046] The collected rotation data is calibrated at the system level based on the 24-dimensional state to obtain the calibration parameters at constant temperature.

[0047] Based on the calibration parameters Remove the offset error from the coarse calibration temperature drift curve as follows:

[0048]

[0049] in, For any temperature T k The debiased state of the MEMS three-axis gyroscope calibration system. For any temperature T k Rough calibration temperature drift value under T c Coarse calibration drift value at temperature.

[0050] Further, based on the debiased state, the zero-bias temperature drift curve of the MEMS three-axis gyroscope and the temperature drift curve of the non-orthogonal transformation matrix component are obtained as follows:

[0051] The initial zero bias temperature drift curve in the orthogonal system is:

[0052] Based on the initial zero bias temperature drift curve in the orthogonal system and the zero bias temperature drift curve obtained by the six-position parameter correction method, the zero bias temperature drift curve of the MEMS three-axis gyroscope is obtained.

[0053]

[0054] in, is the zero bias obtained by the six-position parameter correction method;

[0055] Non-orthogonal transformation matrix The temperature drift curve of the main diagonal elements is:

[0056]

[0057] Non-orthogonal transformation matrix The temperature drift curve of non-diagonal elements is:

[0058]

[0059]

[0060] in, Both The elements in .

[0061] The beneficial effects of the present invention are:

[0062] The present invention provides a thermal drift error calibration method for a MEMS three-axis gyroscope based on segmented calibration, which processes the rotation data of the heating process in segments, applies a system-level calibration method based on 24-dimensional states to calibrate each segmented data, and finally obtains the zero-bias temperature drift curve of the MEMS gyroscope and the temperature drift curve of the non-orthogonal transformation matrix component. The present invention gets rid of the characteristic that the traditional calibration method relies on expensive temperature control equipment, obtains available parameters, and reduces the calibration cost of the gyroscope, which is conducive to improving the accuracy of MEMS three-axis gyroscope application scenarios such as navigation positioning and attitude estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a flow chart of the thermal drift error calibration method of the MEMS three-axis gyroscope provided by the present invention;

[0064] Figure 2 It is the rotation scheme of the inertial unit when collecting data configured in the present invention;

[0065] Figure 3 It is a schematic diagram of the installation of the inertial unit and the rotating tool in the temperature chamber when collecting data;

[0066] Figure 4 It is a segmented schematic diagram of collecting data during the heating process in the present invention. DETAILED DESCRIPTION

[0067] The present invention is further described below. The following examples are only used to more clearly illustrate the technical solution of the present invention, and are not intended to limit the protection scope of the present invention.

[0068] The present invention provides a method for calibrating the thermal drift error of a MEMS three-axis gyroscope suitable for low-cost tools, which can obtain the gyroscope zero bias temperature drift curve (3 curves), the non-orthogonal transformation matrix K g Component temperature drift curves (9), a total of 12 parameter temperature drift curves, are beneficial to improving the application accuracy of MEMS three-axis gyroscopes.

[0069] The present invention provides a method for calibrating the thermal drift error of a MEMS three-axis gyroscope based on segmented calibration. Figure 1 , as follows:

[0070] S1: Fix the inertial unit composed of the MEMS triaxial gyroscope and the triaxial accelerometer on the cubic frame, and use the six-position parameter correction method to obtain the temperature drift curve of the accelerometer parameters. Then, when collecting the rotation data during the heating process, the temperature drift curve of the accelerometer parameters is applied to convert the accelerometer data in the inertial unit, and the converted accelerometer data is used for system-level calibration of the gyroscope parameters. It should be noted that the six-position parameter correction method is a calibration technology recently disclosed by the inventors, see Tongxu Xu, Xiang Xu, Dacheng Xu, and Heming Zhao. A Novel Calibration Method Using Six Positions for MEMS Triaxial Accelerometer [J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-11, Art. No: 1002211. It will not be repeated here.

[0071] S2: In the six-position parameter correction method, the gyroscope data at six positions can be preliminarily processed to obtain six zero-bias temperature drift curves for each axis. (ie, the zero-bias temperature drift curve of the sensor coordinate system), wherein k = 1, 2, 3 ..., 6. Each zero-bias temperature drift curve is obtained by averaging sensor data with the same temperature value.

[0072] S3: Fix the inertial unit on the dual-axis rotating tool and place the dual-axis rotating tool in a low-cost temperature box, such as Figure 3 As shown in the figure, the inertial unit 3 is placed on a dual-axis rotating tool 4 and placed in an incubator 1. The front of the incubator is a transparent side door 2 for easy observation of the experimental process.

[0073] A pre-configured inertial unit rotation scheme is used to control the dual-axis rotation tool to rotate the inertial unit so that the X, Y, and Z axes of the inertial unit rotate around the horizontal direction respectively, and this rotation scheme is repeated until the temperature reaches the set maximum temperature value.

[0074] It should be noted that during the rotation of the inertial unit, the temperature in the low-cost temperature box is controlled to rise from a low temperature to a high temperature at an approximately uniform speed as required, and the rotation data of the heating process is collected.

[0075] Preferably, the temperature changes in the range of T1 to T2, usually by more than 20°C.

[0076] As a preferred implementation, in one embodiment of the present invention, the temperature variation range is -10°C to 40°C.

[0077] Preferably, the temperature rise rate is in the range of 0.5°C to 3°C per minute. If it is too small, temperature control is difficult to achieve; if it is too large, the amount of data for smaller temperature intervals is insufficient.

[0078] It should be noted that the pre-configured inertial unit rotation scheme can be found in Figure 2 , Figure 2 Shown is a cycle of rotation, specifically:

[0079] (1) Initially, the Z axis is facing upward, and the position is static for 5 seconds. Then, the position is rotated 90 degrees counterclockwise around the X axis, and the position is static for another 5 seconds.

[0080] (2) Rotate 180 degrees counterclockwise around the X-axis, rotate 180 degrees clockwise, and then rotate 180 degrees counterclockwise, and then remain still for 5 seconds;

[0081] (3) Rotate 90 degrees counterclockwise around the X-axis and remain stationary for 5 seconds;

[0082] (4) Rotate 90 degrees counterclockwise around the Y axis and remain stationary for 5 seconds;

[0083] (5) Rotate 180 degrees clockwise around the Y axis, 180 degrees counterclockwise, and then 180 degrees clockwise, and then remain still for 5 seconds;

[0084] (6) Rotate 90 degrees counterclockwise around the Y axis and rest for 5 seconds; rotate 90 degrees clockwise around the X axis and rest for 5 seconds; rotate 90 degrees counterclockwise around the Z axis and rest for 5 seconds;

[0085] (7) Rotate 180 degrees clockwise around the Z axis, 180 degrees counterclockwise, and then 180 degrees clockwise, and then remain still for 5 seconds;

[0086] (8) Rotate 90 degrees counterclockwise around the Z axis and rest for 5 seconds; rotate 90 degrees counterclockwise around the X axis, return to the initial position, and then rest for 5 seconds.

[0087] In the next cycle, continue from step (1).

[0088] In the rotation plan, a 90-degree rotation is designed to take about 3 seconds, a 180-degree rotation is designed to take about 9 seconds, and each rotation ends with a 5-second rest.

[0089] S4: Segment the rotation data collected during the heating process.

[0090] In the present invention, data segmentation is performed in an interval sliding manner, such as Figure 4 As shown, the specific implementation process is as follows:

[0091] Assume that the relationship between the temperature and time during the heating process is approximately T(t) (approximate straight line), and after discrete sampling, T(t k ), where k = 1, 2,…, N.

[0092] Set the segment length to 300 seconds, and the corresponding data length is N t300 Set the interval between the start points of adjacent data segments to 20 seconds, and the corresponding data volume is N t20 Then, the index set corresponding to the segmented calibration data sequence is:

[0093] Id j ={j s ,j s +1,…,j s +N t300 -1};

[0094] Among them, Id j is the jth segment data, j is the segment data segment index, and j = 1, ..., M, M is composed of N, N t300 and N t20 Determine, is less than (NN t300 ) / N t20 The largest integer.

[0095] S5. Execute the system-level calibration method based on 24 states for each segment data to obtain the calibration parameters corresponding to each segment data. And the temperature T corresponding to the segmented data k ;

[0096] It should be noted that during the calibration process,

[0097] Establishing the temperature drift model of triaxial gyroscope data V g (T) and error compensation conversion model as follows:

[0098]

[0099]

[0100] Among them, at temperature T, is the scaling factor matrix, is the sensor coordinate system projection matrix, is the theoretical inertial unit angular velocity, ε g (T) is the zero bias in the sensor coordinate system, is the gyro bias in the target orthogonal system, is the estimated non-orthogonal transformation matrix, w g is a random noise with an approximate zero mean. In the present invention, the superscript g is the sensor coordinate system, and b is the orthogonal coordinate system.

[0101] Based on the above three-axis gyroscope data temperature drift model and error compensation conversion model, the measurement error of the gyroscope is obtained:

[0102]

[0103]

[0104] in, for The error matrix δK g The weight of I is the identity matrix, where i is the row number, j is the column number, and 0 is the 1×3 is a vector with 1 row and 3 columns, all of which are 0. b To load the system gyro zero bias;

[0105] The 24-dimensional state in system-level calibration is: in, is a 15-dimensional state vector consisting of attitude error, velocity error, position error, gyro bias, and accelerometer bias;

[0106] S6: The calibration parameters of each segmented data are integrated and smoothed to obtain a rough calibration temperature drift curve.

[0107] In the present invention, the calibration parameter fusion operation is as follows:

[0108]

[0109] in, represents the calibration parameter sequence of the jth segment data, for The corresponding temperature series, The temperature series A sequence of non-repeating temperature values.

[0110] It should be noted that in actual processing, It can be a temperature sequence with an interval of 1°C (or other temperature intervals). If you want to get a parameter sequence with a smaller temperature resolution (for example, an interval of 0.1°C), It can be obtained by interpolation method.

[0111] In the present invention, a wavelet filtering algorithm is used to smooth the fused calibration parameter curve to obtain a rough calibration temperature drift curve.

[0112] S7: Remove the rough calibration temperature drift curve The bias error in the MEMS three-axis gyroscope is used to obtain a more accurate state vector Temperature drift curve.

[0113] In the present invention, the rough calibration temperature drift curve is removed The specific implementation process is:

[0114] Control the thermostat at a constant temperature T c Based on the pre-configured inertial unit rotation scheme, the dual-axis rotation tool is controlled to rotate the inertial unit, so that the X, Y, and Z axes of the inertial unit rotate around the horizontal direction respectively, and the rotation data is collected. The data length is about 800s. The system-level calibration method based on 24 states is used to obtain the calibration parameters under constant temperature.

[0115] Based on calibration parameters at approximately constant temperature Remove the rough calibration temperature drift curve The bias error in is as follows:

[0116]

[0117] in, For any temperature T k The debiased state of the MEMS three-axis gyroscope calibration system. For any temperature T k Rough calibration temperature drift value under T c Coarse calibration parameters at temperature.

[0118] Based on the state vector The initial zero bias temperature drift curve and non-orthogonal transformation matrix of the MEMS three-axis gyroscope are obtained by the following formula: Temperature drift curve of component:

[0119] Initial zero bias temperature drift curve in orthogonal system:

[0120] Non-orthogonal transformation matrix Temperature drift curve of main diagonal elements:

[0121]

[0122] Non-orthogonal transformation matrix Non-diagonal element temperature drift curve:

[0123]

[0124]

[0125] in, Both The elements in .

[0126] It should be noted that the selected constant temperature point of the approximately constant temperature must be within the temperature rise range. For example, according to the above temperature change range, the constant temperature point is selected to be 25°C.

[0127] S8. Process the initial zero bias temperature drift curve obtained in the orthogonal system and the zero bias temperature drift curve obtained in S2 according to the following formula to obtain the zero bias temperature drift curve of the MEMS three-axis gyroscope:

[0128]

[0129] Based on the zero bias temperature drift curve of the MEMS three-axis gyroscope obtained above, a more accurate three-axis gyroscope measurement value can be obtained at any temperature T within the available temperature range:

[0130]

[0131] in, It is the average value (dynamic zero bias) of the gyroscope data in the sensor coordinate system within 1-2 seconds of initial stillness after the sensor is powered on. T0 is the average value of the temperature data within this 1-2 seconds.

[0132] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0133] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0134] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0135] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for calibrating the thermal drift error of a MEMS three-axis gyroscope based on segmented calibration, characterized in that: include: Collect the rotation data of the inertial unit composed of MEMS three-axis gyroscope and three-axis accelerometer during the heating process; Performing segment processing on the collected rotation data; Perform system-level calibration based on 24-dimensional state on each segmented data to obtain calibration parameters corresponding to each segmented data; The calibration parameters of each segmented data are fused and smoothed to obtain a rough calibration temperature drift curve; The bias error in the rough calibration temperature drift curve is removed to obtain the zero bias temperature drift curve of the MEMS three-axis gyroscope and the temperature drift curve of the non-orthogonal transformation matrix component.

2. A method for calibrating the thermal drift error of a MEMS three-axis gyroscope based on segmented calibration according to claim 1, characterized in that: The collecting of the rotation data of the inertial unit composed of the MEMS three-axis gyroscope and the three-axis accelerometer during the heating process includes: An inertial unit consisting of a MEMS three-axis gyroscope and a three-axis accelerometer is fixed on a dual-axis rotating tool and placed in a temperature chamber; A preconfigured inertial unit rotation scheme is used to control the dual-axis rotation tool to drive the inertial unit to rotate, so that the X, Y, and Z axes of the inertial unit rotate around the horizontal direction respectively until the rotation data collection is completed; during the rotation of the inertial unit, the temperature in the temperature box is controlled to rise at a uniform speed, and the rotation data of the heating process is collected.

3. The method for calibrating the thermal drift error of a MEMS three-axis gyroscope based on segmented calibration according to claim 2, characterized in that: During the heating process, the temperature variation range between the lowest temperature and the highest temperature is greater than 20°C.

4. The method for calibrating the thermal drift error of a MEMS three-axis gyroscope based on segmented calibration according to claim 2, characterized in that: During the heating process, the temperature rising rate is in the range of 0.5°C to 3°C per minute.

5. The method for calibrating the thermal drift error of a MEMS three-axis gyroscope based on segmented calibration according to claim 2, characterized in that: The pre-configured inertial unit rotation scheme is: The rotation is performed for one cycle as follows: A. Initially, the Z axis is facing upwards, and the position is static for 5 seconds. Then the position is rotated 90 degrees counterclockwise around the X axis, and the position is static for another 5 seconds. B. Rotate 180 degrees counterclockwise around the X axis, rotate 180 degrees clockwise, and then rotate 180 degrees counterclockwise, and then rest for 5 seconds; C. Rotate 90 degrees counterclockwise around the X axis and keep still for 5 seconds; D. Rotate 90 degrees counterclockwise around the Y axis and keep still for 5 seconds; E. Rotate 180 degrees clockwise around the Y axis, 180 degrees counterclockwise, and then 180 degrees clockwise, and then rest for 5 seconds; F. Rotate 90 degrees counterclockwise around the Y axis and keep still for 5 seconds; Rotate 90 degrees clockwise around the X axis and keep still for 5 seconds; rotate 90 degrees counterclockwise around the Z axis and keep still for 5 seconds; G. Rotate 180 degrees clockwise around the Z axis, 180 degrees counterclockwise, and then 180 degrees clockwise, and then rest for 5 seconds; H. Rotate 90 degrees counterclockwise around the Z axis and keep still for 5 seconds; rotate 90 degrees counterclockwise around the X axis, return to the initial position, and keep still for 5 seconds; During the rotation process, it is set that the rotation of 90 degrees takes 3 seconds, and the rotation of 180 degrees takes 9 seconds.

6. The method for calibrating the thermal drift error of a MEMS three-axis gyroscope based on segmented calibration according to claim 1, characterized in that: The rotation data collected during the heating process is processed in sections by using an interval sliding method; In the segmentation, the segment length is set to 300 seconds, and the interval time between the starting points of adjacent data segments is set to 20 seconds.

7. The method for calibrating the thermal drift error of a MEMS three-axis gyroscope based on segmented calibration according to claim 1, characterized in that: The system-level calibration based on the 24-dimensional state is performed on each segmented data to obtain the calibration parameters corresponding to each segmented data, including: Establishing the temperature drift model of triaxial gyroscope data V g (T) and error compensation conversion model as follows: Among them, at temperature T, is the scaling factor matrix, is the sensor coordinate system projection matrix, is the theoretical inertial unit angular velocity, ε g (T) is the zero bias in the sensor coordinate system, and is the zero bias obtained by the six-position parameter correction method. is the gyro bias in the target orthogonal system, is the estimated non-orthogonal transformation matrix, w g is a random noise with approximately zero mean, the superscript g is the sensor coordinate system, and b is the orthogonal coordinate system. Based on the three-axis gyroscope data temperature drift model and error compensation conversion model, the measurement error of the gyroscope is obtained: in, for The error matrix δK g The weight of I is the identity matrix, where i is the row number, j is the column number, and 0 is the 1×3 is a vector with 1 row and 3 columns, all of which are 0. b To load the system gyro zero bias; The 24-dimensional state is obtained by using the system-level calibration method in, It is a 15-dimensional state vector composed of attitude error, velocity error, position error, gyro bias, and accelerometer bias.

8. The method for calibrating the thermal drift error of a MEMS three-axis gyroscope based on segmented calibration according to claim 7, characterized in that: The obtained calibration parameters of each segmented data are integrated and smoothed to obtain a rough calibration temperature drift curve, including: The fusion operation is as follows: in, represents the calibration parameter sequence of the jth segment data, for The corresponding temperature series, The temperature series A sequence of non-repeating temperature values ​​in ; The smoothing process is: using a wavelet filtering algorithm to smooth the fused calibration parameter curve to obtain a rough calibration temperature drift curve T k is the temperature corresponding to the kth segment data.

9. The method for calibrating the thermal drift error of a MEMS three-axis gyroscope based on segmented calibration according to claim 8, characterized in that: The removing the bias error in the rough calibration temperature drift curve comprises: Control the thermostat at a constant temperature T c , based on the pre-configured inertial unit rotation scheme, the dual-axis rotation tool is controlled to rotate the inertial unit so that the X, Y, and Z axes of the inertial unit rotate around the horizontal direction respectively, and the rotation data is collected; The collected rotation data is calibrated at the system level based on the 24-dimensional state to obtain the calibration parameters at constant temperature. Based on the calibration parameters Remove the offset error from the coarse calibration temperature drift curve as follows: in, For any temperature T k The debiased state of the MEMS three-axis gyroscope calibration system. For any temperature T k Rough calibration temperature drift value under T c Coarse calibration drift value at temperature.

10. The method for calibrating the thermal drift error of a MEMS three-axis gyroscope based on segmented calibration according to claim 9, characterized in that: Based on the debiased state, the zero-bias temperature drift curve of the MEMS three-axis gyroscope and the temperature drift curve of the non-orthogonal transformation matrix component are obtained as follows: The initial zero bias temperature drift curve in the orthogonal system is: Based on the initial zero bias temperature drift curve in the orthogonal system and the zero bias temperature drift curve obtained by the six-position parameter correction method, the zero bias temperature drift curve of the MEMS three-axis gyroscope is obtained. in, is the zero bias obtained by the six-position parameter correction method; Non-orthogonal transformation matrix The temperature drift curve of the main diagonal elements is: Non-orthogonal transformation matrix The temperature drift curve of non-diagonal elements is: in, Both The elements in .

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