A self-calibration method for magnetic shielding parameters of a laser gyroscope IMU

Abstract

This application provides a self-calibration method for the magnetic shielding parameters of a laser gyroscope IMU, relating to the fields of inertial navigation and precision instrument technology. The method includes the following stages: Offline initialization modeling stage: Applying a multi-dimensional standard excitation magnetic field to the IMU, simultaneously acquiring external excitation magnetic field data, residual magnetic field data inside the IMU, control parameter data of the magnetic shielding system, and zero-bias drift data of the laser gyroscope. This application provides a self-calibration method for the magnetic shielding parameters of a laser gyroscope IMU, abandoning the model of permanently fixing magnetic shielding parameters. It creatively proposes a data-driven online real-time parameter optimization mechanism, which can automatically adapt to wide temperature range changes and shielding performance degradation caused by material aging, ensuring that the IMU always operates in a state where the magnetic shielding effectiveness is approximately optimal.

Description

Technical Field

[0001] This application relates to the field of inertial navigation and precision instrument technology, and in particular to a self-calibration method for magnetic shielding parameters of a laser gyroscope (IMU). Background Technology

[0002] As a core component of high-precision navigation systems, the accuracy and stability of laser gyroscope inertial measurement units (IMUs) directly determine navigation performance. In practical applications, IMUs often operate in complex electromagnetic environments, and external magnetic field interference can cause zero-bias drift in the laser gyroscope, severely impairing navigation accuracy. To suppress magnetic field interference, high-precision laser gyroscope IMUs generally employ high-permeability materials such as permalloy to construct magnetic shielding shells, supplemented by active compensation coils, forming a composite magnetic shielding system. This system optimizes shielding effectiveness and minimizes residual magnetic fields entering the IMU by adjusting the mechanical structure of the shield (such as the extension and retraction of the shielding wings) and the current of the compensation coils—the "shielding parameters."

[0003] However, the magnetic shielding parameters of traditional laser gyroscope IMUs are typically set and fixed at the factory and remain unchanged throughout the product's lifecycle. This fixed-parameter approach has the following inherent drawbacks: Poor temperature adaptability: In a wide temperature range (e.g. -40℃ to 85℃), the permeability of magnetic materials such as permalloy will change significantly with temperature. At the same time, the magnetic circuit gap will change due to thermal expansion and contraction of the shielding shell. Both of these factors together cause the magnetic shielding effectiveness to drift, and the factory-set optimal parameters will no longer be optimal after temperature changes.

[0004] Insufficient long-term stability: During long-term operation, the magnetic properties of the magnetic material will deteriorate due to aging effects (such as irreversible deflection of magnetic domains), causing residual magnetic fields to gradually accumulate inside the shielding shell, which in turn causes the zero bias of the laser gyroscope to drift slowly but continuously, exceeding the allowable range of system accuracy.

[0005] Lack of online calibration capability: When shielding performance changes due to environmental factors or aging, existing technologies lack online, adaptive calibration methods. Recalibration requires removing the IMU from the device and returning it to a laboratory for offline debugging using specialized magnetic field equipment. This cannot meet the needs of applications requiring long-term, continuous, and stable operation, such as spacecraft in orbit, aircraft onboard systems, and shipboard systems.

[0006] Existing calibration techniques primarily focus on software compensation of measurements from magnetic field sensors (such as magnetometers), failing to establish a direct, quantitative mapping relationship between "magnetic shielding parameters," "internal residual magnetic field," and "final output performance (zero bias drift) of the laser gyroscope." Therefore, it is impossible to fundamentally improve and maintain the core navigation accuracy of the laser gyroscope through active adjustment of the shielding system. The traditional "fixed parameters and offline debugging" approach is no longer suitable for applications across wide temperature ranges, material aging, and complex dynamic electromagnetic interference, becoming a key technical bottleneck restricting the accuracy and reliability of high-precision laser gyroscope IMUs throughout their entire lifecycle. Summary of the Invention

[0007] This application is made in view of the above-mentioned problems, and its purpose is to provide a self-calibration method for the magnetic shielding parameters of a laser gyroscope IMU to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a self-calibration method for magnetic shielding parameters of a laser gyroscope IMU, comprising the following stages: Offline initialization modeling stage: Apply a multi-dimensional standard excitation magnetic field to the IMU, and simultaneously collect external excitation magnetic field data, IMU internal residual magnetic field data, magnetic shielding system control parameter data, and laser gyroscope zero-bias drift data; Based on the collected data, establish a coupled mapping model f that describes the quantitative relationship between shielding parameters, external excitation magnetic field, and laser gyroscope zero-bias drift, and store the model; Online real-time calibration phase: Periodically collect the internal residual magnetic field value B1 and the laser gyroscope zero-bias drift value D1 during IMU operation. Substitute the internal residual magnetic field value B1 as an equivalent external magnetic field into the coupling mapping model f. Substitute B1 and D1 into the coupling mapping model f, and with the gyroscope zero-bias drift value meeting the preset accuracy condition as the optimization objective, solve in reverse to obtain the current optimal shielding parameter P1. Adjust the actuator of the magnetic shielding system according to the optimal shielding parameter P1. Periodic recalibration phase: Monitor the preset recalibration trigger conditions. When the trigger conditions are met, re-execute data acquisition and model fitting to update the coefficients of the coupled mapping model f.

[0009] Furthermore, the coupling mapping model f is a multivariate function, and its expression is: In the formula: D represents the zero-bias drift of the laser gyroscope, with units of ° / h / Gs; P is the shielding parameter, which is a dimensionless normalized composite parameter obtained by normalizing the shielding wing extension and coil compensation current. B represents the intensity of the external excitation magnetic field, measured in Gs. a, b, c, and d are dimensional model coefficients, obtained by fitting measured data under multidimensional magnetic field excitation; The constant term d is used to compensate for the system's fixed zero bias, installation errors, and the residual magnetic field of the material background.

[0010] Furthermore, in the online real-time calibration phase, after adjusting the actuator, a closed-loop verification step is also included: collecting the adjusted internal residual magnetic field value B2 and the laser gyroscope zero-bias drift value D2. If D2 does not meet the preset accuracy condition, iterative optimization is performed using B2 and D2 as inputs until the condition is met or the maximum number of iterations is reached.

[0011] Furthermore, the online real-time calibration cycle is 100ms, the response time of a single calibration process is no more than 300ms, and the maximum number of iterations is no more than 3.

[0012] Furthermore, the recalibration triggering conditions include at least one of the following: The cumulative continuous operating time of the IMU reaches the first threshold. The temperature change range of the environment in which the IMU is located reaches the second threshold. The fluctuation amplitude of the zero-bias drift of the laser gyroscope reaches the third threshold.

[0013] Furthermore, the first threshold is 1000 hours, the second threshold is 10°C, and the third threshold is 0.0005° / h / Gs.

[0014] Furthermore, in the offline initialization modeling stage, the multi-dimensional standard excitation magnetic field includes a magnetic field with an intensity between 0.1mT and 1mT and a frequency between 10Hz and 1000Hz that varies along the three orthogonal axes of X, Y, and Z.

[0015] Compared with the prior art, the present invention has the following advantages: it proposes a data-driven online real-time parameter optimization mechanism that can automatically adapt to the shielding performance degradation caused by wide temperature range changes and material aging, so that the IMU always works in a state where the magnetic shielding effectiveness is close to optimal.

[0016] A three-dimensional direct coupling mapping model was established, and the final navigation performance index of laser gyroscope zero bias drift was taken as the optimization target for calibration. By solving the parameters in reverse through the model, a direct closed loop from "shielding performance adjustment" to "navigation accuracy improvement" was realized. After calibration, the laser gyroscope zero bias drift can be stably controlled within 0.001° / h / Gs, which is an order of magnitude higher than the accuracy of the traditional fixed parameter method.

[0017] Through a three-stage closed-loop design of "offline initialization, online real-time calibration, and periodic recalibration", a long-term calibration system covering the entire product lifecycle has been constructed. The periodic recalibration mechanism can track and compensate for long-term slow-change effects. Through simulation and experimental verification, it can ensure that the cumulative amount of laser gyroscope zero-bias drift is low during 10 years of long-term operation of the IMU, and the whole process does not require disassembly.

[0018] The entire calibration algorithm has low computational complexity and fast response speed, making it fully suitable for real-time operation in embedded systems. It does not change the main hardware structure of the IMU and can be implemented only through software upgrades and enhanced control logic. It is easy to promote and apply in existing spaceborne, airborne, shipborne, and ground-based high-precision laser gyroscope IMU products, and has extremely high engineering application value. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this drawing or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this drawing. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the overall process of the self-calibration method for magnetic shielding parameters of a laser gyroscope IMU provided in an embodiment of the present invention.

[0021] Figure 2 This is a flowchart illustrating the specific steps of the offline initialization modeling stage in an embodiment of the present invention.

[0022] Figure 3 This is a schematic diagram of the closed-loop control principle during the online real-time calibration stage in an embodiment of the present invention.

[0023] Figure 4 This is a flowchart illustrating the triggering and execution of the periodic recalibration phase in an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the following description and illustration are provided in conjunction with embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0025] Obviously, the following description is merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios without any inventive effort. Furthermore, it is understood that although the effort involved in such development may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0026] Unless otherwise specified, the terms "comprising" and "including" as used in this application can be open-ended or closed-ended. For example, "comprising" and "including" can mean that other components not listed may also be included, or that only the listed components may be included.

[0027] Unless otherwise specified, the term "or" is inclusive in this application. For example, the phrase "A or B" means "A, B, or both A and B". More specifically, the condition "A or B" is satisfied by any of the following conditions: A is true (or exists) and B is false (or does not exist); A is false (or does not exist) and B is true (or exists); or both A and B are true (or exist).

[0028] Example 1: Offline Initialization Modeling This embodiment details the process of establishing the coupling mapping model.

[0029] First, the laser gyroscope IMU to be calibrated is mounted at the center of a standard three-axis Helmholtz coil magnetic field test bench, ensuring precise relative positioning between the IMU and the coil. A series of standard excitation magnetic fields are applied via the test bench control system: the magnetic field directions are sequentially along the X, Y, and Z axes; the magnetic field strength in each direction varies in 0.1 mT increments between 0.1 mT and 1 mT; and at each strength, the magnetic field frequency is selected at logarithmic intervals between 10 Hz and 1000 Hz. The applied external magnetic field vector B is monitored and recorded using a high-precision magnetometer.

[0030] Meanwhile, the magnetic sensor inside the IMU measures the residual magnetic field value B1 inside the shielded shell in real time. The high-precision laser gyroscope data acquisition system synchronously records the zero-bias drift D1 of the laser gyroscope generated under different external magnetic field excitations. The IMU's control system records the shielding parameter P corresponding to maintaining the shielding state. This parameter has been normalized. For example, P=0 corresponds to the shielding wings being fully retracted and the compensation current being 0, while P=1 corresponds to the shielding wings being fully extended and the compensation current being maximum.

[0031] Hundreds of valid data sets {B, P, D} covering the above multi-dimensional scan were collected, and multiple linear regression fitting was performed using the least squares method. The mapping model was set as follows: ; By solving the normal equations using mathematical tools or embedded system programming, we obtain a set of optimal coefficients that minimize the sum of squared errors between the calculated and measured values: a = 0.002, b = 0.005, c = 0.001, d = 0.0001 (the dimensions of coefficient a are ° / h / Gs; the dimensions of coefficients b and c are ° / h / Gs; the dimension of the constant term d is ° / h / Gs). Therefore, the initial coupling mapping model obtained by fitting is: ; The coefficient of determination R² of the model was calculated, and it was verified that R² ≥ 0.998, indicating that the model has extremely high fitting accuracy and can accurately characterize the quantitative relationship among the three. Finally, the model and its coefficients were written into the FLASH memory of the IMU main control board.

[0032] Example 2: Online Real-Time Calibration This example demonstrates a self-calibration process of the IMU during operation.

[0033] The IMU operates in a shipboard environment, with its embedded processor executing a self-calibration routine every 100ms.

[0034] Data acquisition: Read the magnetic sensor values ​​to obtain the current internal residual magnetic field value B1=0.3mT; read the zero-bias data of the laser gyroscope in the past cycle to obtain the laser gyroscope zero-bias drift D1=0.0177° / h / Gs.

[0035] Solution: Read the current internal residual magnetic field value B1 = 0.3mT. According to the conversion rule 1mT = 10Gs, convert it to 3Gs and substitute it into the model for calculation: D = 0.002×P + 0.005×B1 + 0.001×P×B1 + 0.0001 (Note: Substitute B1 = 3 for calculation). Simplify the equation and solve for the unknown P. The optimal shielding parameter P1 is approximately equal to 0.52, where 0.3mT is 3Gs, and the conversion rule is 1mT = 10Gs.

[0036] Adjustment: P1 is converted into specific execution instructions. Assuming the correspondence between P and the shield wing displacement L (mm) and coil current I (mA) is: L=5P, I=200P, then the calculation shows that the shield wing needs to extend to 2.6mm, and the compensation coil needs to carry 104mA of current. The processor controls the corresponding mechanism's action through the DAC and drive circuit.

[0037] Verification: After a short delay to allow the system to stabilize after the operation, the data was collected again. The adjusted internal residual magnetic field value B2 was measured to be 0.1 mT, and the adjusted laser gyroscope zero-bias drift value D2 was 0.0008° / h / Gs. D2 ≤ 0.001° / h / Gs, which meets the accuracy requirements. This online calibration was completed within 250 ms without manual intervention. Here, 0.1 mT is equivalent to 1 Gs, and the conversion rule is 1 mT = 10 Gs.

[0038] Example 3: Periodic Recalibration The IMU has been running continuously in orbit on the satellite platform for 1,000 hours, and the "periodic recalibration" function has been triggered.

[0039] The system records the current state and automatically switches to recalibration mode. Although a standard external excitation magnetic field cannot be applied in the on-orbit environment, it can use the natural geomagnetic field background in space and the magnetic field changes generated by satellite attitude maneuvers as excitation sources, or use the test coil built into the IMU to generate a weak known excitation to replace some offline initialization functions.

[0040] In this mode, the system collects multiple sets of internal residual magnetic field values ​​B1, current shielding parameters P, and laser gyroscope zero-bias drift values ​​D1 under multiple different postures (corresponding to different external magnetic field directions and magnitudes).

[0041] Using the newly acquired dataset, the stored model coefficients [a,b,c,d] are refitted or recursively updated (e.g., using recursive least squares).

[0042] The updated model coefficients are used to overwrite the old coefficients. This process compensates for minor changes in material properties that may occur due to long-term operation in orbit, ensuring the accuracy of the model's predictions throughout the IMU's lifetime.

[0043] The working principle of this application can be divided into three stages: Phase 1: Offline Initialization Modeling (Establishing Control Baselines) The goal of this stage is to pre-build a high-precision "control model" for a specific IMU, and its workflow is as follows: Multi-dimensional data acquisition: The IMU to be calibrated is placed in a controllable standard magnetic field environment, and a series of standard excitation magnetic fields with precisely controllable intensity, direction, and frequency are applied through external devices. The system simultaneously acquires three sets of key data: External excitation magnetic field: The applied external standard magnetic field is monitored by a high-precision magnetometer.

[0044] Internal residual magnetic field: The internal magnetic field that actually acts on the sensitive element after being attenuated by the shielding shell, as measured by the magnetic sensor inside the IMU.

[0045] Laser gyroscope zero-bias drift: The high-precision data acquisition system of the IMU measures and records in real time the output zero-bias error of the laser gyroscope under the excitation of the external magnetic field.

[0046] Current shielding parameters: Simultaneously record the normalized control parameters of the active / passive shielding mechanism corresponding to the current shielding effect generated by the IMU (e.g., the extension of the shielding wing, the current value of the compensation coil, etc.).

[0047] Model Fitting and Consolidation: Using the massive amount of data collected covering a wide range of magnetic field conditions, a mathematical equation describing the quantitative mapping relationship between "shielding parameters", "internal residual magnetic field" and "gyroscope zero-bias drift" is established and solved through mathematical fitting methods (such as multiple regression analysis). A set of optimal model coefficients is obtained. After verification, the model has extremely high fitting accuracy. Finally, this quantitative mapping model and its coefficients are written into the permanent memory of the IMU as the benchmark for subsequent online calibration.

[0048] Phase Two: Online Real-Time Closed-Loop Calibration (Core Operation Process) When the IMU runs in a real working environment, the system uses the mapping model established in the previous stage as its core to perform periodic and automated closed-loop calibration. Its working principle is as follows: State awareness: Within the set working cycle, the system senses the current state in real time, specifically by reading the actual internal residual magnetic field strength measured by the internal magnetic sensor, and calculating the current laser gyroscope zero-bias drift value by processing the laser gyroscope output data.

[0049] Target decision and model solution: The system compares the current measured zero-bias drift value with the preset accuracy threshold. If the value exceeds the threshold, calibration is initiated. At this time, the system uses the currently measured internal residual magnetic field and the expected target zero-bias drift (usually set as the accuracy threshold) as known inputs, substitutes them into the stored mapping model, and solves the model equation to calculate the optimal shielding parameter settings necessary to achieve the target accuracy.

[0050] Execution and feedback verification: The processor converts the calculated optimal shielding parameters into specific control commands to drive the active shielding mechanism (such as adjusting the mechanical position of the shielding wings or changing the current of the compensation coil). After the action is completed, the system samples again to verify whether the new internal residual magnetic field and gyroscope zero-bias drift have met the accuracy requirements, thus forming a complete closed-loop control of "perception-decision-execution-verification" to ensure that the IMU continues to work in the best shielding state.

[0051] Phase 3: Periodic adaptive updates (maintaining long-term accuracy) To address the slight drift in model parameters that may occur after long-term operation of the IMU due to factors such as device aging and environmental stress, this invention introduces a periodic recalibration mechanism. Its working principle is as follows: When the equipment reaches the predetermined time or condition, the system starts the model update program. At this time, by using the natural magnetic field changes in the working environment or the known excitation generated by the built-in test coil, the offline initialization process is simulated to collect a new set of state data. Then, using this new field data, the original mapping model coefficients in the memory are refitted or recursively updated, and the updated model is used to overwrite the old model. This enables the system to self-correct and ensure the accuracy and effectiveness of the calibration model throughout the entire product life cycle, achieving long-term self-maintenance of accuracy.

[0052] It should be noted that this application is not limited to the above-described embodiments. The above embodiments are merely examples, and any embodiments with the same structure and effect as the technical concept within the scope of this application are included in the technical scope of this application. Furthermore, various modifications that can be conceived by those skilled in the art to the embodiments, and other ways of constructing by combining some of the constituent elements of the embodiments, without departing from the spirit of this application, are also included in the scope of this application.

Claims

1. A method for self-calibrating the magnetic shielding parameters of a laser gyroscope IMU, characterized in that: Includes the following stages: Offline initialization modeling stage: Apply a multi-dimensional standard excitation magnetic field to the IMU, and simultaneously collect external excitation magnetic field data, IMU internal residual magnetic field data, magnetic shielding system control parameter data, and laser gyroscope zero-bias drift data; Based on the collected data, establish a coupled mapping model f that describes the quantitative relationship between shielding parameters, external excitation magnetic field, and laser gyroscope zero-bias drift, and store the model; Online real-time calibration phase: Periodically collect the internal residual magnetic field value B1 and the laser gyroscope zero-bias drift value D1 during IMU operation. Substitute the internal residual magnetic field value B1 as an equivalent external excitation magnetic field into the coupling mapping model f. Substitute B1 and D1 into the coupling mapping model f, and with the gyroscope zero-bias drift value meeting the preset accuracy condition as the optimization objective, solve in reverse to obtain the current optimal shielding parameter P1. Adjust the actuator of the magnetic shielding system according to the optimal shielding parameter P1. Periodic recalibration phase: Monitor the preset recalibration trigger conditions. When the trigger conditions are met, re-execute data acquisition and model fitting to update the coefficients of the coupled mapping model f.

2. The self-calibration method for magnetic shielding parameters of a laser gyroscope IMU according to claim 1, characterized in that, The coupling mapping model f is a multivariate function, and its expression is: In the formula: D represents the zero-bias drift of the laser gyroscope, with units of ° / h / Gs; P is the shielding parameter, which is a dimensionless normalized composite parameter obtained by normalizing the shielding wing extension and coil compensation current. B is the intensity of the external excitation magnetic field, in Gs. According to the conversion rule, 1mT = 10Gs. a, b, c, and d are dimensional model coefficients obtained by fitting measured data under multidimensional magnetic field excitation; The constant term d is used to compensate for the system's fixed zero bias, installation errors, and the residual magnetic field of the material background.

3. The self-calibration method for magnetic shielding parameters of a laser gyroscope IMU according to claim 1, characterized in that, In the online real-time calibration phase, after adjusting the actuator, a closed-loop verification step is also included: collecting the adjusted internal residual magnetic field value B2 and the laser gyroscope zero-bias drift value D2. If D2 does not meet the preset accuracy condition, iterative optimization is performed using B2 and D2 as inputs until the condition is met or the maximum number of iterations is reached.

4. The self-calibration method for magnetic shielding parameters of a laser gyroscope IMU according to claim 3, characterized in that, The online real-time calibration cycle is 100ms, the response time of a single calibration process is no more than 300ms, and the maximum number of iterations is no more than 3.

5. The self-calibration method for magnetic shielding parameters of a laser gyroscope IMU according to claim 1, characterized in that, Recalibration trigger conditions include at least one of the following: The cumulative continuous operating time of the IMU reaches the first threshold. The temperature change range of the environment in which the IMU is located reaches the second threshold. The fluctuation amplitude of the zero-bias drift of the laser gyroscope reaches the third threshold.

6. The self-calibration method for magnetic shielding parameters of a laser gyroscope IMU according to claim 5, characterized in that, The first threshold is 1000 hours, the second threshold is 10°C, and the third threshold is 0.0005° / h / Gs.

7. The self-calibration method for magnetic shielding parameters of a laser gyroscope IMU according to claim 6, characterized in that, During the offline initialization modeling phase, the multi-dimensional standard excitation magnetic field includes magnetic fields with intensities ranging from 0.1 mT to 1 mT and frequencies ranging from 10 Hz to 1000 Hz along the three orthogonal axes of X, Y, and Z.

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