Calibration method, device and equipment of dual-axis MEMS accelerometer and storage medium

Abstract

The present disclosure provides a method, device, equipment and storage medium for calibrating a dual-axis MEMS accelerometer, the method comprising: obtaining original acceleration data output by the dual-axis MEMS accelerometer when an included angle between an x-axis and a y-axis is within a range of ±15°; correcting the original acceleration data by using a pre-constructed parameter correction matrix to obtain ideal corrected acceleration data, and outputting the ideal corrected acceleration data as a calibration result; wherein at least one of a scale factor, a non-orthogonal cross term, and a zero offset corresponding to the x-axis and the y-axis is corrected. Since the non-orthogonal cross term of the x-axis to the y-axis and the non-orthogonal cross term of the y-axis to the x-axis are introduced in the parameter correction matrix, the coupling error introduced by the non-orthogonality in the original acceleration data can be compressed to within 0.005° through the two non-orthogonal cross terms, thereby further improving the accuracy of the output ideal acceleration data and realizing accurate calibration of the dual-axis MEMS accelerometer within the range of ±15°.

Description

Technical Field

[0001] This disclosure relates to the field of metrology instrument calibration technology, and in particular to a calibration method, apparatus, device and storage medium for a dual-axis MEMS accelerometer. Background Technology

[0002] When calibrating biaxial MEMS accelerometers, most existing technologies employ simple calibration algorithms such as the single-axis two-point method and the multi-position linear least squares method. These simplified calibration algorithms only correct for bias and scale factor, neglecting the minute angular deviations (non-orthogonality) between the X and Y sensitive axes caused by manufacturing processes. This deviation can cause the acceleration signal from one axis to "leak" to the other, creating cross-coupling errors. Within a ±15° range, even a non-orthogonal angle of only 0.1° can introduce an angular error exceeding 0.02°, far exceeding the target accuracy of 0.005°. Therefore, improving the calibration accuracy of biaxial MEMS accelerometers within a ±15° range has become a pressing technical problem for those skilled in the art. Summary of the Invention

[0003] In view of this, this disclosure proposes a calibration method, apparatus, device and storage medium for a dual-axis MEMS accelerometer, which can improve the calibration accuracy of the dual-axis MEMS accelerometer within a range of ±15°.

[0004] According to a first aspect of this disclosure, a calibration method for a dual-axis MEMS accelerometer is provided, comprising: Acquire raw acceleration data output by a dual-axis MEMS accelerometer when the angle between the x-axis and y-axis is within ±15°; The original acceleration data is corrected using a pre-constructed parameter correction matrix to obtain corrected ideal acceleration data, and the ideal acceleration data is output as the calibration result. The parameter correction moment includes at least one of the following correction parameters: x-axis scale factor, x-axis non-orthogonal cross term with respect to y-axis, x-axis zero bias, y-axis scale factor, y-axis non-orthogonal cross term with respect to x-axis, and y-axis zero bias.

[0005] In one possible implementation, constructing the parameter correction matrix includes: Based on the ideal acceleration data and raw acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are ±90°, a first-order parameter matrix is ​​calculated, wherein the first-order parameter matrix includes the first-order parameter values ​​of each of the correction parameters in the parameter correction moment; Based on the ideal acceleration data and raw acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are ±15° apart, a quadratic parameter matrix is ​​calculated, which includes the quadratic parameter values ​​of each correction parameter in the parameter correction moment; The parameter correction matrix is ​​constructed based on the first-order parameter matrix and the second-order parameter matrix.

[0006] In one possible implementation, when calculating the first-order parameter matrix based on the ideal acceleration data and raw acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are at ±90°, the calculation includes: Based on the ideal acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are ±90°, construct the target output matrix corresponding to the ±90° angle. Based on the raw acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are ±90°, construct the raw output matrix corresponding to the ±90° angle; The linear parameter matrix is ​​calculated based on the target output matrix and the original output matrix corresponding to ±90°.

[0007] In one possible implementation, when calculating the quadratic parameter matrix based on the ideal acceleration data and raw acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are at ±15°, the calculation includes: Based on the ideal acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are ±15° apart, construct the target output matrix corresponding to the ±15°. Based on the raw acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are at ±15°, construct the raw output matrix corresponding to the ±15°. The quadratic parameter matrix is ​​calculated based on the target output matrix and the original output matrix corresponding to ±15°.

[0008] In one possible implementation, constructing the parameter correction matrix based on the first-order parameter matrix and the second-order parameter matrix includes: Based on the first-order parameter matrix and the second-order parameter matrix, calculate the initial parameter correction matrix; Based on the initial parameter correction matrix and the original output matrix corresponding to ±15°, calculate the initial target output matrix corresponding to ±15°. Calculate the error between the target output matrix and the initial target output matrix at ±15° to obtain the error matrix at ±15°; Based on the error matrix corresponding to ±15°, the x-axis zero bias and y-axis zero bias in the initial parameter correction matrix are corrected to obtain the parameter correction matrix.

[0009] In one possible implementation, after constructing the parameter correction matrix, the following is also included: Based on the parameter correction matrix, verify whether the true error matrix corresponding to ±15° meets the preset accuracy requirements.

[0010] According to a second aspect of this disclosure, a dual-axis MEMS accelerometer calibration device is provided, comprising: The data acquisition module is used to acquire the raw acceleration data output by the dual-axis MEMS accelerometer when the angle between the x-axis and y-axis is within ±15°. The output data calibration module is used to correct the original acceleration data using a pre-constructed parameter correction matrix to obtain corrected ideal acceleration data, and output the ideal acceleration data as the calibration result. The parameter correction moment includes at least one of the following correction parameters: x-axis scale factor, x-axis non-orthogonal cross term with respect to y-axis, x-axis zero bias, y-axis scale factor, y-axis non-orthogonal cross term with respect to x-axis, and y-axis zero bias.

[0011] In one possible implementation, the apparatus further includes a parameter correction matrix construction module, which, when constructing the parameter correction matrix, is specifically used for: Based on the ideal acceleration data and raw acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are ±90°, a first-order parameter matrix is ​​calculated, wherein the first-order parameter matrix includes the first-order parameter values ​​of each of the correction parameters in the parameter correction moment; Based on the ideal acceleration data and raw acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are ±15° apart, a quadratic parameter matrix is ​​calculated, which includes the quadratic parameter values ​​of each correction parameter in the parameter correction moment; The parameter correction matrix is ​​constructed based on the first-order parameter matrix and the second-order parameter matrix.

[0012] According to a third aspect of this disclosure, a dual-axis MEMS accelerometer calibration device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the method described in the first aspect of this disclosure.

[0013] According to a fourth aspect of this disclosure, a non-volatile computer-readable storage medium is provided that stores computer program instructions thereon, wherein the computer program instructions, when executed by a processor, implement the method described in the first aspect of this disclosure.

[0014] This disclosure provides a calibration method, apparatus, device, and storage medium for a dual-axis MEMS accelerometer. The method includes: acquiring raw acceleration data output by the dual-axis MEMS accelerometer when the angle between the x-axis and y-axis is within ±15°; correcting the raw acceleration data using a pre-constructed parameter correction matrix to obtain corrected ideal acceleration data, and outputting the ideal acceleration data as the calibration result; wherein the parameter correction matrix includes at least one correction parameter selected from x-axis scale factor, x-axis non-orthogonal cross term with respect to y-axis, x-axis zero bias, y-axis scale factor, y-axis non-orthogonal cross term with respect to x-axis, and y-axis zero bias. Because the non-orthogonal cross term with respect to y-axis and y-axis is introduced into the parameter correction matrix, the coupling error introduced by non-orthogonality in the raw acceleration data can be compressed to within 0.005° through these two non-orthogonal cross terms, thereby further improving the accuracy of the output ideal acceleration data and achieving accurate calibration of the dual-axis MEMS accelerometer within the ±15° range.

[0015] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0016] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this disclosure together with the specification and serve to explain the principles of this disclosure.

[0017] Figure 1 A flowchart illustrating a calibration method for a dual-axis MEMS accelerometer according to an embodiment of the present disclosure is shown. Figure 2 A schematic block diagram of a dual-axis MEMS accelerometer calibration apparatus according to an embodiment of the present disclosure is shown. Figure 3 A schematic block diagram of a dual-axis MEMS accelerometer calibration apparatus according to an embodiment of the present disclosure is shown. Detailed Implementation

[0018] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0019] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0020] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0021] <Method Implementation> It should be noted that before implementing the method of this disclosure, a parameter correction matrix needs to be constructed. This parameter correction matrix can correct the raw acceleration data output by the dual-axis MEMS accelerometer to ideal acceleration data after removing zero bias, scale factor error and non-orthogonality error between the X and Y axes. This parameter correction matrix is ​​pre-stored in the system that implements the method of this disclosure. In this way, the system can read the parameter correction matrix during real-time correction and perform fast and accurate correction on the raw acceleration data output by the dual-axis MEMS accelerometer.

[0022] In one possible implementation, the parameter correction matrix includes an x-axis scale factor. Non-orthogonal cross term of x-axis with respect to y-axis x-axis zero bias y-axis scale factor Non-orthogonal cross term of the y-axis with respect to the x-axis and y-axis zero bias At least one of the correction parameters.

[0023] In an embodiment where the parameter correction matrix includes the above six correction parameters, the parameter correction matrix is ​​denoted as: P3 2 = In one possible implementation, a parameter correction matrix P3 is constructed that includes the aforementioned six correction parameters. In step 2, the following steps may be included: Step 1: Based on the ideal acceleration data and raw acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are at ±90°, calculate the first-order parameter matrix. The first-order parameter matrix includes the first-order parameter values ​​of each correction parameter in the parameter correction moments. Specifically, calculating this parameter matrix may include the following steps: First, based on the ideal acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are at ±90°, construct the target output matrix corresponding to ±90°. Here, the ideal acceleration data in this disclosure refers to the component on other axes that should be 0mg when gravity is fully applied to a certain axis, while the component on this axis should be 1000mg. This is the ideal acceleration data.

[0024] Specifically, the dual-axis MEMS accelerometer has four cases when the x-axis and y-axis are ±90°: 1) x-axis 90°, y-axis 0°; 2) x-axis -90°, y-axis 0°; 3) x-axis 0°, y-axis 90°; 4) x-axis 0°, y-axis -90°. Ideal acceleration data (including ideal x-axis acceleration and ideal y-axis acceleration) is obtained for each of these four cases. Specifically, when x-axis 90°, y-axis 0°, the output ideal acceleration data is (1000, 0); when x-axis -90°, y-axis 0°, the output ideal acceleration data is (-1000, 0); when x-axis 0°, y-axis 90°, the output ideal acceleration data is (0, 1000); and when x-axis 0°, y-axis -90°, the output ideal acceleration data is (0, -1000). Then, the ideal acceleration data output in the four cases are combined in top-to-bottom order to construct the target output matrix corresponding to ±90°, which is denoted as: D

[90] 4 2= Second, based on the raw acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are at ±90°, a raw output matrix corresponding to ±90° is constructed. Here, the raw acceleration data in this disclosure refers to the actual measured output value of the dual-axis MEMS accelerometer.

[0025] Specifically, for four cases where the x-axis and y-axis are ±90°, the raw acceleration data (including raw x-axis acceleration and raw y-axis acceleration) output by the dual-axis MEMS accelerometer is acquired for each of these four cases. Specifically, when the x-axis is 90° and the y-axis is 0°, the raw acceleration data output is (984.88, 4.98); when the x-axis is -90° and the y-axis is 0°, the raw acceleration data output is (-1011.92, -3.07); when the x-axis is 0° and the y-axis is 90°, the raw acceleration data output is (-17.07, 1002.75); and when the x-axis is 0° and the y-axis is -90°, the raw acceleration data output is (-8.82, -999.5). Then, the raw acceleration data output in the four cases are combined in the same top-to-bottom order to construct the raw output matrix corresponding to ±90°, which is denoted as: Third, calculate the parameter matrix based on the target output matrix and the original output matrix corresponding to ±90°.

[0026] Specifically, the original output matrix corresponding to ±90° is extended by one column to obtain the extended matrix M.

[90] 4 3: M

[90] 4 3 = The other parameter matrix is ​​K

[90] 3 2: K

[90] 3 2 = in, , , , , as well as These are the first-order parameter values ​​corresponding to each correction parameter in the parameter correction matrix.

[0027] The target output moment D corresponding to ±90°

[90] 4 2 and extended matrix M

[90] 4 3 satisfies the following calculation formula: D

[90] 4 2 = M

[90] 4 3 K

[90] 3 2 The target output moment D corresponding to ±90°

[90] 4 2 and extended matrix M

[90] 4 Given 3, the linear parameter matrix K can be directly solved.

[90] 3 2.

[0028] Step 2: Based on the ideal acceleration data and raw acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are at ±15°, calculate the quadratic parameter matrix. The quadratic parameter matrix includes the quadratic parameter values ​​of each correction parameter in the parameter correction moments. Specifically, the calculation of this quadratic parameter matrix may include the following steps: First, based on the ideal acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are at ±15°, the target output matrix corresponding to ±15° is constructed.

[0029] Specifically, the dual-axis MEMS accelerometer has four cases when the x-axis and y-axis are ±15° apart: 1) x-axis 15°, y-axis 0°; 2) x-axis -15°, y-axis 0°; 3) x-axis 0°, y-axis 15°; 4) x-axis 0°, y-axis -15°. Ideal acceleration data (including ideal x-axis and ideal y-axis acceleration) is obtained for each of these four cases. Specifically, when x-axis 15°, y-axis 0°, the output ideal acceleration data is (258.819, 0); when x-axis -90°, y-axis 0°, the output ideal acceleration data is (--258.819, 0); when x-axis 0°, y-axis 15°, the output ideal acceleration data is (0, 258.819); and when x-axis 0°, y-axis -15°, the output ideal acceleration data is (0, -258.819). Then, the ideal acceleration data output in the four cases are combined in top-to-bottom order to construct the target output matrix corresponding to ±15°, which is denoted as: D

[15] 4 2= Second, based on the raw acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are at ±15°, the raw output matrix corresponding to ±15° is constructed.

[0030] Specifically, for four cases where the x-axis and y-axis are ±15° apart, the raw acceleration data (including raw x-axis acceleration and raw y-axis acceleration) output by the dual-axis MEMS accelerometer is acquired for each of these four cases. Specifically, when the x-axis is 15° and the y-axis is 0°, the raw acceleration data output is (236.46, 7.53); when the x-axis is -15° and the y-axis is 0°, the raw acceleration data output is (-280.13, -5.49); when the x-axis is 0° and the y-axis is 15°, the raw acceleration data output is (-22.76, 265.6); and when the x-axis is 0° and the y-axis is -15°, the raw acceleration data output is (-20.67, -252.39). Then, the raw acceleration data output for each of the four cases is combined in the same top-to-bottom order to construct the raw output matrix corresponding to ±15°, which is denoted as: Third, based on the target output matrix and the original output matrix corresponding to ±15°, calculate the quadratic parameter matrix.

[0031] Specifically, the original output matrix corresponding to ±15° is column-extended to obtain the extended matrix M.

[15] 4 3: M

[15] 4 3 = Based on the first-order parameter matrix calculated above, K

[90] 3 2 pairs of extended matrices M

[15] 4 3. Perform correction to obtain the correction matrix D'

[15] 4 2, where the correction matrix D'

[15] 4 The formula for calculating 2 is shown below: D'

[15] 4 2 = M

[15] 4 3 K

[90] 3 2 For the correction matrix D'

[15] 4 2. Perform a one-column extension to obtain the extended matrix D'

[15] 4 3: The other quadratic parameter matrix is ​​K

[15] 3 2: K

[15] 3 2 = in, 、 、 、 、 as well as These are the quadratic parameter values ​​corresponding to each correction parameter in the parameter correction matrix.

[0032] The target output moment D corresponding to ±15° is then...

[15] 4 2 and extended matrix D'

[15] 4 3 satisfies the following calculation formula: D

[15] 4 2= ​​D'

[15] 4 3 K

[15] 3 2 The target output moment D corresponding to ±15°

[15] 4 2 and extended matrix D'

[15] 4 Given that 3, the quadratic parameter matrix K can be directly solved.

[15] 3 2.

[0033] Step 3: Construct the parameter correction matrix based on the first-order and second-order parameter matrices. The specific calculation steps are as follows: First, based on the first-order and second-order parameter matrices, calculate the initial parameter correction matrix. Specifically, first, let the first-order parameter matrix be K.

[90] 3 2 extended to K

[90] 3 3: K

[90] 3 3 = Then, based on K

[90] 3 3 and the quadratic parameter matrix K

[15] 3 2. Calculate the initial parameter correction matrix P3 2, where the initial parameter correction matrix P3 The formula for calculating 2 is shown below: P3 2 = K

[90] 3 3 K

[15] 3 2 Among them, the initial parameter correction matrix P3 2 can be represented as: P3 2= in, 、 、 、 、 as well as The initial parameter correction matrix P3 is shown below. The parameter values ​​of each of the above correction parameters corresponding to 2.

[0034] Second, based on the initial parameter correction matrix P3 Calculate the original output matrix corresponding to 2° and ±15°, and calculate the initial target output matrix corresponding to ±15°.

[0035] Specifically, the original output matrix corresponding to ±15° is column-extended to obtain the extended matrix M.

[15] 4 3: M

[15] 4 3 = Correct matrix P3 using initial parameters 2 pairs of extended matrices M

[15] 4 3. Perform correction to obtain the initial target output matrix D''

[15] 4 2, where the initial target output matrix D''

[15] 4 2. The calculation formula is as follows: D''

[15] 4 2 = M

[15] 4 3 P3 2 Third, calculate the target output matrix D

[15] 4 corresponding to ±15°. 2 and the initial target output matrix D''

[15] 4 The error is 2, and the error matrix δ corresponding to ±15° is obtained.

[15] 4 2, where δ

[15] 4 The formula for calculating 2 is shown below: δ

[15] 4 2 = D

[15] 4 2- D''

[15] 4 2 Fourth, based on the error matrix δ corresponding to ±15°.

[15] 4 2. Correct the initial parameter matrix P3 The x-axis and y-axis zero biases in step 2 are corrected to obtain the final parameter correction matrix.

[0036] Specifically, based on the error matrix δ

[15] 4 2. Calculate the fine adjustment amount δx for the x-axis zero bias, where the formula for calculating the fine adjustment amount δx for the x-axis zero bias is as follows: δx = (δ4 2(1,1) + δ4 2(2,1) ) / 2 In the formula, δ4 2(1,1) represents the error matrix δ

[15] 4 Data in row 1 and column 1 of column 2, δ4 2(2,1) represents the error matrix δ

[15] 4 The data in row 2 and column 1 of column 2.

[0037] Based on the error matrix δ

[15] 4 2. Calculate the fine adjustment amount for the y-axis zero bias, where the formula for calculating the fine adjustment amount δy for the y-axis zero bias is as follows: δy = (δ4 2(3,2) + δ4 2(4,2) ) / 2 In the formula, δ4 2(3,2) represents the error matrix δ

[15] 4 The data in row 3 and column 2, δ4 2(4,2) represents the error matrix δ

[15] 4 The data in row 4 and column 2 of section 2.

[0038] The initial parameter correction matrix P3 is based on the fine-tuning amount δx with zero x-axis bias. Zero x-axis bias in 2 Adjustments were made to obtain the adjusted zero x-axis offset. Among them, the adjusted x-axis zero offset The calculation formula is as follows: = +δx The initial parameter correction matrix P3 is based on the fine adjustment δy of the zero y-axis bias. y-axis zero bias in 2 Adjustments were made to obtain the adjusted y-axis zero offset. Among them, the adjusted y-axis zero offset The calculation formula is as follows: = +δy Then the initial parameter correction matrix P3 The x-axis and y-axis zero biases in step 2 are corrected to obtain the final parameter correction matrix as shown below: P'3 2 = In one possible implementation, after constructing the above-mentioned parameter correction matrix P'3 Following 2, it also includes: based on the parameter correction matrix P'3 2. Verify whether the actual error matrix corresponding to ±15° meets the preset accuracy requirements.

[0039] Specifically, the true error matrix δ' corresponding to ±15°

[15] 4 The formula for calculating 2 is shown below: δ'

[15] 4 2 = D

[15] 4 2-M

[15] 4 3 P'3 2 Determine δ'

[15] 4 2. If the target accuracy is less than 0.005°, and the actual error is less than this target accuracy, then the calculated parameter correction matrix P'3 is used to determine if the actual error meets the preset accuracy requirement. 2. Stored in the system executing the method of this disclosure for convenient use in subsequent real-time correction processes.

[0040] It should be noted here that, since the raw acceleration data and ideal acceleration data of the MEMS accelerometer follow a linear output law, the parameter correction matrix P'3 constructed through the above process... 2. It can be used for accurate correction of various original acceleration data within a range of ±15°.

[0041] Before practicing, construct the modified parameter correction matrix P'3. After step 2, the calibration method of the biaxial MEMS accelerometer disclosed herein can be executed. Figure 1 A flowchart illustrating a calibration method for a biaxial MEMS accelerometer according to an embodiment of this disclosure is shown. Figure 1 As shown, the method includes steps S1100-S1200.

[0042] S1100: Acquire the raw acceleration data output by the dual-axis MEMS accelerometer when the angle between the x-axis and y-axis is within ±15°. S1200: Correct the raw acceleration data using a pre-built parameter correction matrix to obtain the corrected ideal acceleration data, and output the ideal acceleration data as the calibration result.

[0043] Specifically, the raw acceleration data accx0 and accy0 of the MEMS accelerometer output obtained at any angle. The calibration parameter matrix is ​​also known to be P'3. 2. The calibrated ideal acceleration data are: accx1 = accx0+ accy0+ accy1= accx0+ accy0+ This disclosure provides a calibration method for a dual-axis MEMS accelerometer, comprising: acquiring the raw acceleration data output by the dual-axis MEMS accelerometer when the angle between the x-axis and y-axis is within ±15°; correcting the raw acceleration data using a pre-constructed parameter correction matrix to obtain corrected ideal acceleration data, and outputting the ideal acceleration data as the calibration result; wherein, the parameter correction matrix includes at least one correction parameter selected from x-axis scale factor, x-axis non-orthogonal cross term, x-axis zero bias, y-axis scale factor, y-axis non-orthogonal cross term, and y-axis zero bias. Because the x-axis non-orthogonal cross term and y-axis non-orthogonal cross term are introduced into the parameter correction matrix, the coupling error introduced by non-orthogonality in the raw acceleration data can be compressed to within 0.005°, thereby further improving the accuracy of the output ideal acceleration data and achieving accurate calibration of the dual-axis MEMS accelerometer within the ±15° range.

[0044] Traditional methods typically optimize in the acceleration domain, that is, minimize the difference between the corrected acceleration and the theoretical gravity projection (g). The difference between sin(θ) and sin(θ) is significant. This method is less sensitive to non-orthogonal terms, especially at small angles where sin(θ) is approximately linear, making it difficult to effectively separate cross-coupling errors.

[0045] The method provided in this disclosure has the following advantages: 1. Define residuals directly in the angle domain: The optimization objective is no longer the matching degree of acceleration, but the difference between the final calculated tilt angle and the true tilt angle. This makes the optimization process directly serve the final application objective—angle accuracy.

[0046] 2. Joint processing of x / y axis data: The x-axis rotation data and y-axis rotation data are merged into a large dataset for joint optimization. In this way, ay_raw in the x-axis data (theoretically should be 0, but not 0 due to non-orthogonality) becomes the constraint y→x interaction term (…). kxy The key information is as follows: Similarly, the ax_raw constraint in the y-axis data defines the x→y cross term (...). kyx ).

[0047] This method significantly improves the identifiability and estimation accuracy of nonorthogonal parameters. By directly minimizing the angular error, it ensures that the coupling error introduced by nonorthogonality is compressed to within 0.005° within a working range of ±15°, which is difficult to guarantee in acceleration domain optimization. <Device Embodiment> Figure 2 A schematic block diagram of a dual-axis MEMS accelerometer calibration apparatus according to an embodiment of the present disclosure is shown. Figure 2 As shown, the device 100 includes: The data acquisition module 110 is used to acquire the raw acceleration data output by the dual-axis MEMS accelerometer when the angle between the x-axis and y-axis is within ±15°. The output data calibration module 120 is used to correct the original acceleration data using a pre-built parameter correction matrix to obtain the corrected ideal acceleration data, and output the ideal acceleration data as the calibration result. The parameter correction moment includes at least one of the following correction parameters: x-axis scale factor, x-axis non-orthogonal cross term with respect to y-axis, x-axis zero bias, y-axis scale factor, y-axis non-orthogonal cross term with respect to x-axis, and y-axis zero bias.

[0048] In one possible implementation, the device further includes a parameter correction matrix construction module, which, when constructing the parameter correction matrix, is specifically used for: Based on the output of ideal acceleration data and raw acceleration data of the dual-axis MEMS accelerometer when the x-axis and y-axis are ±90°, the first-order parameter matrix is ​​calculated, where the first-order parameter matrix includes the first-order parameter values ​​of each correction parameter in the parameter correction moment; Based on the dual-axis MEMS accelerometer, ideal acceleration data and raw acceleration data are output when the x-axis and y-axis are ±15° apart. The quadratic parameter matrix is ​​calculated, which includes the quadratic parameter values ​​of each correction parameter in the parameter correction moment. A parameter correction matrix is ​​constructed based on the first-order and second-order parameter matrices.

[0049] <Equipment Example> Figure 3 A schematic block diagram of a dual-axis MEMS accelerometer calibration apparatus according to an embodiment of the present disclosure is shown. Figure 3 As shown, the dual-axis MEMS accelerometer calibration device 200 includes a processor 210 and a memory 220 for storing executable instructions of the processor 210. The processor 210 is configured to implement any of the aforementioned dual-axis MEMS accelerometer calibration methods when executing the executable instructions.

[0050] It should be noted here that the number of processors 210 can be one or more. Furthermore, the dual-axis MEMS accelerometer calibration device 200 of this embodiment may also include an input device 230 and an output device 240. The processors 210, memory 220, input device 230, and output device 240 can be connected via a bus or other means, without specific limitations here.

[0051] The memory 220, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and various modules, such as the program or module corresponding to the calibration method of the dual-axis MEMS accelerometer in this embodiment of the present disclosure. The processor 210 executes various functional applications and data processing of the dual-axis MEMS accelerometer calibration device 200 by running the software program or module stored in the memory 220.

[0052] Input device 230 can be used to receive input digital numbers or signals. These signals may include key signals related to user settings and function control of the device / terminal / server. Output device 240 may include a display device such as a screen.

[0053] <Storage Medium Examples> According to a fourth aspect of this disclosure, a non-volatile computer-readable storage medium is also provided, on which computer program instructions are stored, which, when executed by processor 210, implement the calibration method of any of the preceding dual-axis MEMS accelerometers.

[0054] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical applications, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A calibration method for a biaxial MEMS accelerometer, characterized in that, include: Acquire raw acceleration data output by a dual-axis MEMS accelerometer when the angle between the x-axis and y-axis is within ±15°; The original acceleration data is corrected using a pre-constructed parameter correction matrix to obtain corrected ideal acceleration data, and the ideal acceleration data is output as the calibration result. The parameter correction moment includes at least one of the following correction parameters: x-axis scale factor, x-axis non-orthogonal cross term with respect to y-axis, x-axis zero bias, y-axis scale factor, y-axis non-orthogonal cross term with respect to x-axis, and y-axis zero bias.

2. The method according to claim 1, characterized in that, Constructing the parameter correction matrix includes: Based on the ideal acceleration data and raw acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are ±90°, a first-order parameter matrix is ​​calculated, wherein the first-order parameter matrix includes the first-order parameter values ​​of each of the correction parameters in the parameter correction moment; Based on the ideal acceleration data and raw acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are ±15° apart, a quadratic parameter matrix is ​​calculated, which includes the quadratic parameter values ​​of each correction parameter in the parameter correction moment; The parameter correction matrix is ​​constructed based on the first-order parameter matrix and the second-order parameter matrix.

3. The method according to claim 2, characterized in that, When calculating the first-order parameter matrix based on the ideal acceleration data and raw acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are at ±90°, the calculation includes: Based on the ideal acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are ±90°, construct the target output matrix corresponding to the ±90° angle. Based on the raw acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are ±90°, construct the raw output matrix corresponding to the ±90° angle; The linear parameter matrix is ​​calculated based on the target output matrix and the original output matrix corresponding to ±90°.

4. The method according to claim 2, characterized in that, When calculating the quadratic parameter matrix based on the output of ideal acceleration data and raw acceleration data from the dual-axis MEMS accelerometer at ±15° angles to the x and y axes, the calculation includes: Based on the ideal acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are ±15° apart, construct the target output matrix corresponding to the ±15°. Based on the raw acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are at ±15°, construct the raw output matrix corresponding to the ±15°. The quadratic parameter matrix is ​​calculated based on the target output matrix and the original output matrix corresponding to ±15°.

5. The method according to claim 2, characterized in that, When constructing the parameter correction matrix based on the first-order parameter matrix and the second-order parameter matrix, the following steps are included: Based on the first-order parameter matrix and the second-order parameter matrix, calculate the initial parameter correction matrix; Based on the initial parameter correction matrix and the original output matrix corresponding to ±15°, calculate the initial target output matrix corresponding to ±15°. Calculate the error between the target output matrix and the initial target output matrix at ±15° to obtain the error matrix at ±15°; Based on the error matrix corresponding to ±15°, the x-axis zero bias and y-axis zero bias in the initial parameter correction matrix are corrected to obtain the parameter correction matrix.

6. The method according to claim 2, characterized in that, After constructing the parameter correction matrix, the following is also included: Based on the parameter correction matrix, verify whether the true error matrix corresponding to ±15° meets the preset accuracy requirements.

7. A calibration device for a dual-axis MEMS accelerometer, characterized in that, include: The data acquisition module is used to acquire the raw acceleration data output by the dual-axis MEMS accelerometer when the angle between the x-axis and y-axis is within ±15°. The output data calibration module is used to correct the original acceleration data using a pre-constructed parameter correction matrix to obtain corrected ideal acceleration data, and output the ideal acceleration data as the calibration result. The parameter correction moment includes at least one of the following correction parameters: x-axis scale factor, x-axis non-orthogonal cross term with respect to y-axis, x-axis zero bias, y-axis scale factor, y-axis non-orthogonal cross term with respect to x-axis, and y-axis zero bias.

8. The apparatus according to claim 7, characterized in that, It also includes a parameter correction matrix construction module, which, when constructing the parameter correction matrix, is specifically used for: Based on the ideal acceleration data and raw acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are ±90°, a first-order parameter matrix is ​​calculated, wherein the first-order parameter matrix includes the first-order parameter values ​​of each of the correction parameters in the parameter correction moment; Based on the ideal acceleration data and raw acceleration data output by the dual-axis MEMS accelerometer when the x-axis and y-axis are ±15° apart, a quadratic parameter matrix is ​​calculated, which includes the quadratic parameter values ​​of each correction parameter in the parameter correction moment; The parameter correction matrix is ​​constructed based on the first-order parameter matrix and the second-order parameter matrix.

9. A calibration device for a dual-axis MEMS accelerometer, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to implement the method of any one of claims 1 to 6 when executing the executable instructions.

10. A non-volatile computer-readable storage medium storing computer program instructions thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 6.

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