A gyro position calibration method and system

Abstract

The application belongs to the technical field of gyroscopes, and discloses a gyroscope position calibration method and system, the method comprising: applying angular velocity excitation to the gyroscope, obtaining the theoretical rotation angle of the gyroscope at a plurality of sampling positions in a rotation period; recording the arrival time of the gyroscope rotating to each sampling position; determining the actual rotation angle at each sampling position according to the angular velocity excitation, the preset initial error term of the gyroscope and the arrival time at each sampling position; constructing the cost function of the error term based on the actual rotation angle and the theoretical rotation angle at each sampling position; and solving the cost function of the error term based on an optimization algorithm to calibrate the preset initial error term and obtain the calibrated error term. The application solves the constructed cost function of the error term by using the optimization algorithm, calibrates the preset initial error term, and has high calibration accuracy, low test time consumption and high calibration efficiency.

Description

Technical Field

[0001] This invention belongs to the field of gyroscope technology, specifically relating to a gyroscope position calibration method and system. Background Technology

[0002] A gyroscope, also known as an angular velocity gyroscope, is an instrument used to sense and maintain orientation. It can measure or maintain the rotational angular velocity of a reference direction.

[0003] Three-axis gyroscopes are core sensing devices in inertial navigation systems, and their measurement accuracy directly affects the accuracy of attitude calculation. Therefore, reducing the measurement error of three-axis gyroscopes and improving their measurement accuracy has become a crucial issue. For single-axis gyroscopes, the main factors affecting their static measurement accuracy are the sensor's zero-bias error, scale coefficient error, and random drift error. However, for three-axis gyroscopes, the accuracy of the measurement results is related to the zero-bias error, scale coefficient error, random drift error of each individual single-axis gyroscope, as well as the non-orthogonal installation error between the sensitive axes of each individual single-axis gyroscope.

[0004] Compared to single-axis sensors, triaxial sensors have more calibration parameters and a more complex calibration process. Currently, gyroscope calibration typically employs position calibration and rate calibration methods. These two methods have advantages such as simple principles and ease of implementation. However, as the number of calibration parameters increases, the amount of data increases dramatically, the testing time increases, and the testing conditions become more stringent, requiring high-precision testing equipment. The calibration results depend on the accuracy of the testing equipment.

[0005] The current methods for position and rate calibration of gyroscopes have drawbacks, including stringent testing conditions, the need for precise calibration equipment and a high-precision north-pointing reference, and long calibration times. Summary of the Invention

[0006] The purpose of this invention is to provide a gyroscope position calibration method and system to solve the problems of harsh testing conditions, the need for precise calibration testing equipment and high-precision north reference, and long calibration time in the prior art.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for calibrating the position of a gyroscope, the method comprising: An angular velocity excitation is applied to the gyroscope to obtain the theoretical rotation angles at multiple sampling positions within one rotation cycle. Record the arrival time of the gyroscope at each sampling position; Based on the angular velocity excitation, the gyroscope's preset initial error term, and the arrival time at each sampling position, the actual rotation angle at each sampling position is determined. Based on the actual and theoretical rotation angles at each sampling location, a cost function for the error term is constructed. The cost function of the error term is solved based on the optimization algorithm to calibrate the preset initial error term and obtain the calibrated error term.

[0008] Preferably, the preset initial error terms include: drift error, scaling factor error, and other errors; wherein, the other errors include at least: installation error and temperature error.

[0009] Preferably, the expression for the cost function of the error term is: ; In the formula, J The cost value of the cost function. For the first i The theoretical rotation angle of each sampling position. For the first i The actual rotation angle of each sampling position I This represents the total number of sampling locations.

[0010] Preferably, the first i The actual rotation angle of each sampling position The expression is: ; In the formula, For the first i -1 actual rotation angle at sampling position This refers to the angular velocity output by the gyroscope. For the first i The arrival time corresponding to each sampling location For the first i -1 arrival times corresponding to sampling locations; Among them, the angular velocity output by the gyroscope The relation is: ; In the formula, For angular velocity excitation, For scaling factor error, For drift error, Other errors.

[0011] Preferably, the optimization algorithm is a particle swarm optimization algorithm. Based on this algorithm, the cost function of the error term is solved to obtain the calibrated error term, including: Based on the cost function of the error term, the fitness function of the particle swarm optimization algorithm is constructed. Initialize the parameters of the particle swarm optimization algorithm, which include: the historical best position of each particle and the population best position; Calculate the fitness value of each particle based on the fitness function of the particle swarm optimization algorithm; The historical best position and the population best position are updated based on the fitness value of each particle. Based on pre-built dynamic weights, update the velocity and position of each particle, and calculate the updated fitness value for each particle. The historical best position and the population best position are updated again with the updated fitness value of each particle until global convergence is obtained, and the global best particle position is obtained. The drift error, scaling factor error and other errors corresponding to the global best particle position constitute the calibration error term.

[0012] Preferably, the method further includes: constructing dynamic weights, including: Obtain the population size, current iteration count, optimal population position, and position of each particle in the particle swarm optimization algorithm; Based on the population size and the number of iterations, the distance between the position of each particle and the optimal position of the population is calculated to obtain the iteration distance of each particle. Sum the iterative distances of all particles to obtain the current total distance; Dynamic weights are constructed based on preset weight values ​​and the current total distance.

[0013] Preferably, the calculation expression for the dynamic weight is: ; In the formula, For the first q Dynamic weights for each iteration For the first q The current total distance in the next iteration, where e is the natural base. The maximum value of the preset weight. This is the minimum value of the preset weight.

[0014] Preferably, the expression for the total distance in the q-th iteration is: ; In the formula, Represents the cosine similarity function. In the first q The optimal position of the population in the next iteration. In the first q In the nth iteration m The position of each particle. M Population size.

[0015] Preferably, the velocity update expression for each particle is: ; The update expression for the position of each particle is: ; In the formula, In the first q In the +1st iteration, the... m The speed of each particle In the q In the +1st iteration, the... m The position of each particle. In the q In the nth iteration m The position of each particle. In the first q In the nth iteration m The speed of each particle For the first m The historical best position of each particle For the first q The optimal position of the population in the next iteration. and These are the first coefficient and the second coefficient, respectively. and All are random numbers within the interval [0,1].

[0016] Secondly, the present invention provides a gyroscope position calibration system for implementing the above-described gyroscope position calibration method, the system comprising: Angle acquisition module is used to apply angular velocity excitation to the gyroscope and obtain the theoretical rotation angle of the gyroscope at multiple sampling positions within one rotation cycle; The time recording module is used to record the arrival time of the gyroscope at each sampling position. The angle calculation module is used to determine the actual rotation angle at each sampling position based on the angular velocity excitation, the gyroscope's preset initial error term, and the arrival time at each sampling position. The function construction module is used to construct the cost function of the error term based on the actual rotation angle and the theoretical rotation angle at each sampling position; The error calibration module is used to solve the cost function of the error term based on the optimization algorithm, so as to calibrate the preset initial error term and obtain the calibrated error term.

[0017] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described gyroscope position calibration method.

[0018] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described gyroscope position calibration method.

[0019] Beneficial effects: This invention pre-sets multiple sampling positions. During the rotation of the gyroscope, these sampling positions are partially aligned with the rotation direction. Each sampling position has a theoretical rotation angle. By simply recording the time it takes for the gyroscope to rotate by one theoretical rotation angle (i.e., the arrival time), the actual rotation angle can be calculated. The cost function of the error term is constructed using the actual rotation angle and the theoretical rotation angle. Then, an optimization algorithm is used to solve the cost function of the error term, thus calibrating the preset initial error term. This method has high calibration accuracy, low testing time, and can improve calibration efficiency. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a gyroscope position calibration method provided in one embodiment of the present invention; Figure 2 This is a block diagram of a gyroscope position calibration system provided in one embodiment of the present invention. Detailed Implementation

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0022] Example 1 Figure 1 This is a flowchart of a gyroscope position calibration method provided in one embodiment of the present invention. Figure 1 As shown in the figure, this embodiment provides a gyroscope position calibration method, the method including: Step S10: Apply angular velocity excitation to the gyroscope to obtain the theoretical rotation angles at multiple sampling positions within one rotation cycle. In this embodiment, during one rotation cycle, the initial attitude and final attitude of the gyroscope during rotation remain consistent, as do the initial and final positions. For example: rotate +180° around the X-axis, then rotate -180° around the X-axis; or rotate +180° around the X-axis, then rotate +180° around the X-axis; or for example: rotate +90° around the X-axis, rotate +90° around the Y-axis, rotate +90° around the X-axis, rotate +90° around the Y-axis, rotate -180° around the X-axis, rotate -180° around the Y-axis. That is, applying the above rotation process to each axis or multiple axes can ensure that the initial attitude and final attitude of the gyroscope remain consistent, and the initial and final positions also remain consistent.

[0023] In this embodiment, the gyroscope is mounted on a three-axis turntable, which applies angular velocity excitation to the gyroscope to drive it to rotate. During rotation, the gyroscope is driven to rotate using the above-mentioned rotation method (the initial attitude and the final attitude of the gyroscope during rotation are consistent, and the initial position and the final position are also consistent). The arrival time of the gyroscope at each sampling position and the specific angular velocity of the applied angular velocity excitation are recorded during the rotation process.

[0024] Step S20: Record the arrival time of the gyroscope at each sampling position. For example, each 10° rotation is taken as a sampling position. Record the time when the gyroscope rotates to the sampling position, which is the arrival time.

[0025] Step S30: Determine the actual rotation angle at each sampling position based on the angular velocity excitation, the preset initial error term of the gyroscope, and the arrival time at each sampling position; in this embodiment, the preset initial error term includes: drift error, scaling factor error, and other errors; wherein, the other errors include at least: installation error and temperature error.

[0026] Specifically, no. i The actual rotation angle of each sampling position The expression is: ; In the formula, For the first i -1 actual rotation angle at sampling position This refers to the angular velocity output by the gyroscope. For the first i The arrival time corresponding to each sampling location For the first i -1 arrival times corresponding to sampling locations; Among them, the angular velocity output by the gyroscope The relation is: ; In the formula, For angular velocity excitation, For scaling factor error, For drift error, Other errors.

[0027] Step S40: Based on the actual rotation angle and theoretical rotation angle at each sampling position, construct the cost function of the error term.

[0028] In this embodiment, since the initial attitude and final attitude of the gyroscope remain consistent during one rotation cycle, and the initial position and final position also remain consistent, the gyroscope angle accumulation is zero, and there is no need to consider the Earth's rotational angular velocity. Therefore, the cost function of the error term can be constructed based on the actual rotation angle and the theoretical rotation angle.

[0029] Therefore, the expression for the cost function of the error term is: ; In the formula, J The cost value of the cost function. For the first i The theoretical rotation angle of each sampling position. For the first i The actual rotation angle of each sampling position I This represents the total number of sampling locations.

[0030] Step S50: Solve the cost function of the error term based on the optimization algorithm to calibrate the preset initial error term and obtain the calibrated error term.

[0031] In this embodiment, the optimization algorithm is a particle swarm optimization algorithm. Therefore, the cost function of the error term is solved based on the optimization algorithm to obtain the calibrated error term, including: Step a10: Based on the cost function of the error term, construct the fitness function of the particle swarm optimization algorithm. The expression for the fitness function of the particle swarm optimization algorithm is: F =min( J ), where min( J () represents the minimum cost value of the cost function; Step a20: Initialize the parameters of the particle swarm optimization algorithm, which include: the historical best position of each particle and the population best position; Step a30: Calculate the fitness value of each particle according to the fitness function of the particle swarm optimization algorithm; Step a40: Update the historical best position and the population best position based on the fitness value of each particle; Step a50: Based on the pre-built dynamic weights, update the velocity and position of each particle, and calculate the updated fitness value for each particle; Step a60: Update the historical best position and the population best position again with the updated fitness value of each particle, that is, repeat steps a20 to a50 until global convergence, and obtain the global best particle position. The drift error, scaling factor error and other errors corresponding to the global best particle position constitute the calibration error term.

[0032] In this embodiment, the particle swarm optimization algorithm can quickly solve for the calibration values ​​of drift error, scaling factor error, and other errors, that is, the drift error, scaling factor error, and other errors corresponding to the global optimal particle position. At the same time, the dynamic weight setting can prevent the particle swarm optimization algorithm from getting stuck in local optima when updating the velocity and position of each particle, thereby improving the accuracy of the calibration results.

[0033] As a further optimization of this embodiment, the method further includes: constructing dynamic weights, including: Step b10: Obtain the population size of the particle swarm optimization algorithm, the current iteration number, the optimal position of the population, and the position of each particle.

[0034] Step b20: Based on the population size and the number of iterations, calculate the distance between the position of each particle and the optimal position of the population to obtain the iteration distance of each particle; The iteration distance of each particle is: ,in, Represents the cosine similarity function. In the first q The optimal position of the population in the next iteration. In the first q In the nth iteration m The position of each particle.

[0035] Step b30: Sum the iterative distances of all particles to obtain the current total distance; The expression for the total distance in the q-th iteration is: ; In the formula, Represents the cosine similarity function. In the first q The optimal position of the population in the next iteration. In the first q In the nth iteration m The position of each particle. M Population size.

[0036] Step b40: Construct dynamic weights based on preset weight values ​​and the current total distance.

[0037] In this embodiment, based on the total distance of the qth iteration in step b30 Dynamic weights can be constructed; therefore, the calculation expression for the dynamic weights is: ; In the formula, For the first q Dynamic weights for each iteration For the first q The current total distance in the next iteration, where e is the natural base. The maximum value of the preset weight. The minimum value of the preset weight values; where the maximum value of the preset weight values ​​is... Minimum value of the preset weight value You can choose flexibly according to your actual needs, for example, The value range is 1.0 to 1.5. The value range is 0.2 to 0.5.

[0038] As a further optimization of this embodiment, the update expression for the velocity of each particle is as follows: ; The update expression for the position of each particle is: ; In the formula, In the first q In the +1st iteration, the... m The speed of each particle In the q In the +1st iteration, the... m The position of each particle. In the q In the nth iteration m The position of each particle. In the first q In the nth iteration m The speed of each particle For the first m The historical best position of each particle For the first q The optimal position of the population in the next iteration. and These are the first coefficient and the second coefficient, respectively. and All are random numbers within the interval [0,1].

[0039] In this embodiment, when a particle is close to the global optimal position of the population, the current total distance... Approaching zero, With a small value, updating the position mainly relies on local search, allowing the particle to converge quickly; when the particle is far from the global view particle and the optimal position of the population, The problem is relatively large, and updating the position relies heavily on global search. This invention, through dynamically adaptive control of weights, can better prevent particles from converging to local optima.

[0040] This invention pre-sets multiple sampling positions. During the rotation of the gyroscope, these sampling positions are partially aligned with the rotation direction. Each sampling position has a theoretical rotation angle. By simply recording the time it takes for the gyroscope to rotate by one theoretical rotation angle (i.e., the arrival time), the actual rotation angle can be calculated. The cost function of the error term is constructed using the actual rotation angle and the theoretical rotation angle. Then, an optimization algorithm is used to solve the cost function of the error term, thus calibrating the preset initial error term. This method has high calibration accuracy, low testing time, and can improve calibration efficiency.

[0041] Example 2 Figure 2 This is a block diagram of a gyroscope position calibration system provided in one embodiment of the present invention. Figure 2 As shown, this embodiment provides a gyroscope position calibration system for implementing the gyroscope position calibration method in Embodiment 1. The system includes: Angle acquisition module is used to apply angular velocity excitation to the gyroscope and obtain the theoretical rotation angle of the gyroscope at multiple sampling positions within one rotation cycle; The time recording module is used to record the arrival time of the gyroscope at each sampling position. The angle calculation module is used to determine the actual rotation angle at each sampling position based on the angular velocity excitation, the gyroscope's preset initial error term, and the arrival time at each sampling position. The function construction module is used to construct the cost function of the error term based on the actual rotation angle and the theoretical rotation angle at each sampling position; The error calibration module is used to solve the cost function of the error term based on the optimization algorithm, so as to calibrate the preset initial error term and obtain the calibrated error term.

[0042] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the gyroscope position calibration method in Embodiment 1.

[0043] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the gyroscope position calibration method in Embodiment 1.

[0044] This invention pre-sets multiple sampling positions. During the rotation of the gyroscope, these sampling positions are partially aligned with the rotation direction. Each sampling position has a theoretical rotation angle. By simply recording the time it takes for the gyroscope to rotate by one theoretical rotation angle (i.e., the arrival time), the actual rotation angle can be calculated. The cost function of the error term is constructed using the actual rotation angle and the theoretical rotation angle. Then, an optimization algorithm is used to solve the cost function of the error term, thus calibrating the preset initial error term. This method has high calibration accuracy, low testing time, and can improve calibration efficiency.

[0045] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0046] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0047] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for calibrating the position of a gyroscope, characterized in that, The method includes: An angular velocity excitation is applied to the gyroscope to obtain the theoretical rotation angles at multiple sampling positions within one rotation cycle. Record the arrival time of the gyroscope at each sampling position; Based on the angular velocity excitation, the gyroscope's preset initial error term, and the arrival time at each sampling position, the actual rotation angle at each sampling position is determined. Based on the actual and theoretical rotation angles at each sampling location, a cost function for the error term is constructed. The cost function of the error term is solved based on the optimization algorithm to calibrate the preset initial error term and obtain the calibrated error term.

2. The gyroscope position calibration method according to claim 1, characterized in that, The preset initial error terms include: drift error, scaling factor error, and other errors; wherein, the other errors include at least: installation error and temperature error.

3. The gyroscope position calibration method according to claim 2, characterized in that, The expression for the cost function of the error term is: ； In the formula, J The cost value of the cost function. For the first i The theoretical rotation angle of each sampling position. For the first i The actual rotation angle of each sampling position I This represents the total number of sampling locations.

4. The gyroscope position calibration method according to claim 3, characterized in that, No. i The actual rotation angle of each sampling position The expression is: ； In the formula, For the first i -1 actual rotation angle at sampling position This refers to the angular velocity output by the gyroscope. For the first i The arrival time corresponding to each sampling location For the first i -1 arrival times corresponding to sampling locations; Among them, the angular velocity output by the gyroscope The relation is: ； In the formula, For angular velocity excitation, For scaling factor error, For drift error, Other errors.

5. The gyroscope position calibration method according to any one of claims 1-4, characterized in that, The optimization algorithm is a particle swarm optimization algorithm. Based on the optimization algorithm, the cost function of the error term is solved to obtain the calibrated error term, including: Based on the cost function of the error term, the fitness function of the particle swarm optimization algorithm is constructed. Initialize the parameters of the particle swarm optimization algorithm, which include: the historical best position of each particle and the population best position; Calculate the fitness value of each particle based on the fitness function of the particle swarm optimization algorithm; The historical best position and the population best position are updated based on the fitness value of each particle. Based on pre-built dynamic weights, update the velocity and position of each particle, and calculate the updated fitness value for each particle. The historical best position and the population best position are updated again with the updated fitness value of each particle until global convergence is obtained, and the global best particle position is obtained. The drift error, scaling factor error and other errors corresponding to the global best particle position constitute the calibration error term.

6. The gyroscope position calibration method according to claim 5, characterized in that, The method further includes: constructing dynamic weights, including: Obtain the population size, current iteration count, optimal population position, and position of each particle in the particle swarm optimization algorithm; Based on the population size and the number of iterations, the distance between the position of each particle and the optimal position of the population is calculated to obtain the iteration distance of each particle. Sum the iterative distances of all particles to obtain the current total distance; Dynamic weights are constructed based on preset weight values ​​and the current total distance.

7. The gyroscope position calibration method according to claim 6, characterized in that, The expression for calculating the dynamic weight is: ； In the formula, For the first q Dynamic weights for each iteration For the first q The current total distance in the next iteration, where e is the natural base. The maximum value of the preset weight. This is the minimum value of the preset weight.

8. The gyroscope position calibration method according to claim 7, characterized in that, The expression for the total distance in the q-th iteration is: ； In the formula, Represents the cosine similarity function. In the first q The optimal position of the population in the next iteration. In the first q In the nth iteration m The position of each particle. M Population size.

9. The gyroscope position calibration method according to claim 7, characterized in that, The expression for updating the velocity of each particle is: ； The update expression for the position of each particle is: ； In the formula, In the first q In the +1st iteration, the... m The speed of each particle In the q In the +1st iteration, the... m The position of each particle. In the q In the nth iteration m The position of each particle. In the first q In the nth iteration m The speed of each particle For the first m The historical best position of each particle For the first q The optimal position of the population in the next iteration. and These are the first coefficient and the second coefficient, respectively. and All are random numbers within the interval [0,1].

10. A gyroscope position calibration system for implementing the gyroscope position calibration method according to any one of claims 1-9, characterized in that, The system includes: Angle acquisition module is used to apply angular velocity excitation to the gyroscope and obtain the theoretical rotation angle of the gyroscope at multiple sampling positions within one rotation cycle; The time recording module is used to record the arrival time of the gyroscope at each sampling position. The angle calculation module is used to determine the actual rotation angle at each sampling position based on the angular velocity excitation, the gyroscope's preset initial error term, and the arrival time at each sampling position. The function construction module is used to construct the cost function of the error term based on the actual rotation angle and the theoretical rotation angle at each sampling position; The error calibration module is used to solve the cost function of the error term based on the optimization algorithm, so as to calibrate the preset initial error term and obtain the calibrated error term.

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