Fiber-optic gyroscope drift compensation method and system fused with LMS (Least Mean Square) adaptive filtering

Abstract

The invention provides a fiber-optic gyroscope drift compensation method and system fused with LMS self-adaptive filtering, and relates to the technical field of LMS self-adaptive filtering, the method comprises the following steps: synchronously collecting multi-point temperature data, temperature change rate, axial and radial gradient information and original output signals of a gyroscope shell, and calculating the multi-point temperature data, the temperature change rate, the axial and radial gradient information and the original output signals of the gyroscope shell; the original signal is separated into a temperature drift component and a non-temperature noise component through variational mode decomposition, and noise is filtered out; establishing a nonlinear mapping relation by combining the temperature field data and the drift component to generate a drift predicted value, and inputting the predicted value and the residual error of the actual drift component into an LMS adaptive filter for dynamic error compensation; and finally, superposing the compensation result and the predicted value, and outputting a corrected gyro signal. According to the invention, the drift compensation precision and stability of the fiber-optic gyroscope in a complex variable-temperature environment are improved.

Description

Technical Field

[0001] The present application relates to the technical field of LMS adaptive filtering, and in particular to a fiber optic gyroscope drift compensation method and system integrating LMS adaptive filtering. Background Art

[0002] Under complex temperature fluctuations, such as the rapid startup and shutdown of aerospace equipment or sudden temperature changes in field equipment, the output signal of a fiber optic gyroscope is susceptible to the non-uniform distribution of the temperature field, leading to increased drift errors. Traditional single-point temperature compensation methods are difficult to handle temperature gradients and dynamic thermal shock. There is an urgent need for a high-precision compensation technology that can incorporate multi-dimensional temperature field characteristics and dynamically correct for drift.

[0003] Currently, the most advanced solution uses a temperature drift compensation method based on support vector regression. By measuring the temperature data of a single point on the gyroscope housing and its historical trend, a static mapping model between temperature and drift error is established. This method incorporates the temperature change rate as an auxiliary feature, uses a kernel function to fit the nonlinear relationship between temperature and drift, and optimizes model parameters during the calibration phase to improve compensation accuracy.

[0004] This approach relies on single-point temperature data, which has limited ability to characterize the spatial distribution of the temperature field. The compensation residual still fluctuates significantly when there are axial or radial temperature gradients. Static model parameters struggle to adapt to the dynamic nonlinear errors during rapid temperature changes, resulting in significant compensation lag and a decrease in error correction effectiveness, especially during sudden temperature changes. Summary of the Invention

[0005] The present application provides a fiber optic gyroscope drift compensation method and system integrating LMS adaptive filtering, which are used to solve the problems of low drift compensation accuracy and poor stability of fiber optic gyroscopes in complex temperature-varying environments in the prior art.

[0006] To solve the above technical problems, in a first aspect, the present application provides a fiber optic gyroscope drift compensation method integrating LMS adaptive filtering, comprising: Collect multi-point temperature measurement data, temperature change rate data, axial radial gradient information and gyro original output signal of the fiber optic gyroscope; performing variational mode decomposition on the original output signal of the gyroscope to generate a temperature drift component and a non-temperature noise component, and filtering out the non-temperature noise component; Establishing a nonlinear mapping relationship based on the multi-point temperature measurement data, the temperature change rate data, the axial-radial gradient information, and the temperature drift component; Based on the nonlinear mapping relationship, a drift prediction value is generated, the drift prediction value is associated with the temperature drift component, and the association result is input into an LMS adaptive filter for dynamic error compensation; The compensation result is superimposed on the drift prediction value to generate a compensated gyro output signal.

[0007] Optionally, associating the drift prediction value with the temperature drift component and inputting the association result into an LMS adaptive filter for dynamic error compensation includes: Subtracting the drift prediction value from the temperature drift component to generate a residual sequence; Inputting the residual sequence into an LMS adaptive filter, and dynamically updating an adjustment factor of the LMS adaptive filter based on a statistical feature set of the residual sequence; An approximation operation is performed on the residual sequence using the updated adjustment factor to generate a target compensation amount, which is a compensation result.

[0008] Optionally, the adjustment factors of the LMS adaptive filter include an update step size factor, a direction memory factor, and a memory depth factor; Dynamically updating the adjustment factor of the LMS adaptive filter based on the statistical feature set of the residual sequence, including: Performing time domain segmentation on the residual sequence through an LMS adaptive filter to obtain multiple residual subsequences; For each time domain segment, extract a statistical feature set from the corresponding residual subsequence, wherein the statistical feature set includes a fluctuation intensity value, a change trend value, and a correlation decay rate; When the fluctuation intensity value is greater than a preset first threshold, increasing the update step factor of the corresponding time domain segment; When the change trend value is greater than a preset second threshold, activating the direction memory factor of the corresponding time domain segment; The memory depth factor of the corresponding time domain segment is dynamically adjusted according to the correlation decay rate.

[0009] Optionally, the using the updated adjustment factor to perform an approximation operation on the residual sequence to generate a target compensation amount includes: Using the updated adjustment factor, traverse each data point in the residual sequence in chronological order to calculate the initial compensation amount corresponding to each data point; The initial compensation increment at the current moment is recursively superimposed with the initial compensation amount at the previous moment to generate an intermediate compensation amount; Boundary constraint processing is performed on the intermediate compensation amount to generate a target compensation amount.

[0010] Optionally, establishing a nonlinear mapping relationship based on the multi-point temperature measurement data, the temperature change rate data, the axial-radial gradient information, and the temperature drift component includes: Performing spatial interpolation processing on the axial-radial gradient information to generate temperature gradient distribution data; Aligning the multi-point temperature measurement data, the temperature change rate data, and the temperature gradient distribution data according to timestamps to construct multi-dimensional temperature field data, the multi-dimensional temperature field data including a temperature measurement data matrix, a temperature change rate vector, and a temperature gradient tensor; Calculating the spatial coupling term between the temperature measurement data matrix and the temperature gradient tensor, and calculating the dynamic coupling term between the temperature change rate vector and the temperature gradient tensor; fusing the spatial coupling term and the dynamic coupling term according to a preset ratio to generate an interaction factor, and generating an enhanced temperature field feature based on the interaction factor; The enhanced temperature field feature is used as a mapping source, the temperature drift component corresponding to the timestamp is used as a mapping target, and a nonlinear mapping relationship from the mapping source to the mapping target is established through an iterative optimization process.

[0011] Optionally, performing variational mode decomposition on the original gyro output signal to generate a temperature drift component and a non-temperature noise component includes: Performing a variational mode decomposition operation on the original output signal of the gyroscope to obtain a plurality of eigenmode components; For each eigenmode component, calculating a correlation coefficient between the eigenmode component and the temperature change rate data; The key eigenmode components whose correlation coefficients exceed a preset correlation threshold are screened out from all eigenmode components, all the key eigenmode components are merged into temperature drift components, and the remaining eigenmode components are classified as non-temperature noise components.

[0012] Optionally, superimposing the compensation result with the drift prediction value to generate a compensated gyro output signal includes: Perform time domain smoothing on the compensation results to generate a stable compensation amount; algebraically adding the stability compensation amount to the drift prediction value to generate a corrected drift amount; performing amplitude limiting processing on the corrected drift amount; The corrected drift amount after the amplitude limiting process is removed from the original gyro output signal to obtain a compensated gyro output signal.

[0013] In a second aspect, the present application provides a fiber optic gyroscope drift compensation system integrating LMS adaptive filtering, comprising: An acquisition module is used to collect multi-point temperature measurement data, temperature change rate data, axial radial gradient information, and the original output signal of the fiber optic gyroscope; a decomposition module configured to perform variational mode decomposition on the gyro original output signal to generate a temperature drift component and a non-temperature noise component, and filter out the non-temperature noise component; a building module configured to build a non-linear mapping relationship based on the multi-point temperature measurement data, the temperature rate of change data, the axial-radial gradient information, and the temperature drift component; a generating module configured to generate a drift prediction value based on the non-linear mapping relationship, associate the drift prediction value with the temperature drift component, and input an association result into an LMS adaptive filter for dynamic error compensation; a superimposing module configured to superimpose a compensation result and the drift prediction value to generate a compensated gyro output signal.

[0014] In a third aspect, the present application provides an electronic device, comprising: a memory configured to store a computer program; a processor configured to implement the steps of the fusion LMS adaptive filtering fiber optic gyroscope drift compensation method according to the first aspect when executing the computer program.

[0015] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executable by a processor to implement the steps of the fusion LMS adaptive filtering fiber optic gyroscope drift compensation method according to the first aspect.

[0016] In the present application, a fusion LMS adaptive filtering fiber optic gyroscope drift compensation method is provided, which comprises the following steps: collecting multi-point temperature measurement data, temperature rate of change data, axial-radial gradient information, and a gyro original output signal of a fiber optic gyroscope; performing variational mode decomposition on the gyro original output signal to generate a temperature drift component and a non-temperature noise component, and filtering out the non-temperature noise component; building a non-linear mapping relationship based on the multi-point temperature measurement data, the temperature rate of change data, the axial-radial gradient information, and the temperature drift component; generating a drift prediction value based on the non-linear mapping relationship, associating the drift prediction value with the temperature drift component, and inputting an association result into an LMS adaptive filter for dynamic error compensation; and superimposing a compensation result and the drift prediction value to generate a compensated gyro output signal.

[0017] The technical solution provided by the present application has the following beneficial effects: This application collects multi-point temperature measurement data, temperature change rate data, axial and radial gradient information, and the original gyroscope output signal: comprehensively obtaining raw data reflecting the spatial distribution characteristics and dynamic change characteristics of the temperature field, establishing a data foundation for subsequent precise compensation. It effectively separates signal components that are strongly correlated with temperature changes and eliminates the impact of non-temperature interference factors such as vibration on compensation accuracy. It accurately depicts the complex nonlinear relationship between the spatial gradient of the temperature field and the drift amount, improving the accuracy of model prediction. It corrects residual errors that are not fitted by the model in real time to adapt to the dynamic compensation needs when the temperature changes drastically. It realizes the organic combination of static prediction and dynamic compensation to ensure the stability of the output signal.

[0018] Furthermore, the present application also calculates the residual sequence of the drift prediction value and the actual temperature drift component, uses the LMS adaptive filter to analyze the statistical characteristics of the residual to dynamically adjust the filter parameters, and performs approximation operations on the residual based on the updated parameters to generate accurate compensation amounts, thereby realizing intelligent tracking and real-time correction of model misfitting errors.

[0019] Moreover, its technical effect is reflected in its ability to adaptively eliminate dynamic errors caused by rapid temperature changes, effectively suppress compensation lag, and improve the stability and reliability of gyroscope output in complex temperature-varying environments.

[0020] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0022] Figure 1 A flowchart of a fiber optic gyroscope drift compensation method integrating LMS adaptive filtering provided in an embodiment of the present application; Figure 2 A schematic diagram of a specific implementation of a fiber optic gyroscope drift compensation method integrating LMS adaptive filtering provided in an embodiment of the present application; Figure 3 Another specific implementation diagram of a fiber optic gyroscope drift compensation method integrating LMS adaptive filtering provided in an embodiment of the present application; Figure 4 A schematic structural diagram of a fiber optic gyroscope drift compensation system integrating LMS adaptive filtering provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] Existing methods for compensating fiber optic gyroscope temperature drift primarily rely on single-point temperature measurement data, using static models to predict and correct drift errors. This approach is suitable for scenarios with uniform and slowly changing temperature distribution. However, in practice, particularly in environments with rapid temperature fluctuations or significant temperature gradients, single-point temperature data cannot fully reflect the gyroscope's true thermal state. Static models are unable to dynamically adjust compensation parameters, resulting in compensation lags and increased residual errors during sudden temperature changes, compromising the stability and accuracy of the gyroscope's output.

[0024] In response to the above problems, this application proposes a fiber optic gyroscope drift compensation method that integrates LMS adaptive filtering. This method constructs input features that can characterize the spatial distribution characteristics of the temperature field through multi-point temperature measurement data, temperature change rate, and axial and radial gradient information, and extracts pure temperature drift components in combination with signal decomposition technology. On this basis, a nonlinear mapping model is used to predict the drift trend, and the prediction residual is corrected in real time through adaptive filtering technology. This method can not only capture the dynamic change characteristics of the temperature field more comprehensively, but also automatically adjust the compensation parameters when the temperature fluctuates rapidly, thereby improving the accuracy and stability of the gyroscope output in complex variable temperature environments, and solving the problem of insufficient compensation caused by the existing technology relying on single-point data and static models.

[0025] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific embodiments. Obviously, the embodiments described are only a part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present application.

[0026] The core of this application is to provide a fiber optic gyroscope drift compensation method integrating LMS adaptive filtering, and a flow chart of a specific implementation method is shown as follows: Figure 1 As shown, the method includes: Step 101: Collect multi-point temperature measurement data, temperature change rate data, axial radial gradient information, and gyro original output signal of the fiber optic gyroscope.

[0027] In step 101, multi-point temperature measurement data refers to the spatial temperature distribution information obtained by temperature sensors placed at different locations on the gyroscope housing. This data is used to characterize the non-uniformity of the temperature field. Temperature change rate data reflects the speed at which the temperature at each measurement point changes over time and is used to capture dynamic thermal shock effects. Axial-radial gradient information describes the steepness of temperature changes along the gyroscope's axial and radial directions, quantifying the spatial variability of the temperature field. The gyroscope's raw output signal is the uncompensated gyroscope angular velocity measurement, which contains the true angular velocity and temperature drift error.

[0028] In an embodiment of the present application, a plurality of temperature sensors are first arranged on the surface of the gyroscope shell according to a specific topological structure, and the sensor positions need to cover the axial and radial key temperature change areas; each sensor synchronously collects temperature data at a fixed sampling frequency and records the corresponding timestamp at the same time; the temperature change rate of each point is obtained by differentially calculating the temperature difference of adjacent sampling periods; the temperature gradient in the axial and radial directions is calculated based on the temperature data of adjacent sensors; the gyroscope angular velocity output signal is synchronously obtained through a high-speed data interface; and finally, the time-aligned multi-point temperature data, temperature change rate, gradient information and original gyroscope signal are packaged into a unified data frame.

[0029] For example, during the startup phase of a vehicle-mounted fiber optic gyroscope, four temperature sensors placed on the housing surface measured temperatures of 25.0°C, 26.8°C, 28.5°C, and 30.2°C, respectively. The calculated temperature change rates were 0.2°C / s, 0.23°C / s, 0.25°C / s, and 0.27°C / s, with an axial gradient of 0.5°C / cm and a radial gradient of 0.6°C / cm. The synchronously collected raw gyroscope output was 15.3° / h. The gradient value is calculated by dividing the temperature difference between adjacent sensors by the spacing between them: for example, axial gradient = (30.2 - 25.0) / 10.4 = 0.5°C / cm.

[0030] Step 102: performing variational mode decomposition on the original gyro output signal to generate a temperature drift component and a non-temperature noise component, and filtering out the non-temperature noise component.

[0031] In step 102, variational mode decomposition (VMD) is an adaptive signal decomposition method that separates complex signals into components with different vibration characteristics. Temperature drift components refer to signal components related to temperature changes, and their changing trends are highly synchronized with the temperature data. Non-temperature noise components include interference signals that are unrelated to temperature, such as mechanical vibration.

[0032] In an embodiment of the present application, a variational modal decomposition process is performed on the original output signal of the gyroscope, and after a preset number of decomposition layers, several intrinsic modal components are obtained through iterative optimization; the correlation coefficient between each component and the temperature change rate data is calculated, and the key components whose correlation coefficient exceeds the threshold are screened; the key components are superimposed to synthesize the temperature drift component, and the remaining components are classified as non-temperature noise; digital filtering technology is used to filter out the non-temperature noise components, and retain the pure temperature-related signal.

[0033] For example, the 15.3° / h gyro signal is decomposed into five components with amplitudes of 6.2, 4.1, 2.8, 1.5, and 0.7° / h, respectively. The correlation coefficients between each component and the temperature change rate are calculated to be 0.62, 0.88, 0.91, 0.65, and 0.58. The 4.1 and 2.8 components with correlation coefficients greater than 0.8 are selected and merged into the 6.9° / h temperature drift component, and the rest are merged into the 8.4° / h noise component. The correlation coefficient calculation formula is: wherein represents a correlation coefficient, represents an observation value of the component signal (unit: ° / h), represents an average value of the component signal, represents an observation value of the temperature rate of change, represents an average value of the temperature rate of change, represents an observation sample number.

[0034] Step 103: based on the multi-point temperature measurement data, the temperature rate of change data, the axial-radial gradient information, and the temperature drift component, a nonlinear mapping relationship is established.

[0035] In step 103, the nonlinear mapping relationship is a mathematical model describing the complex relationship between the multi-dimensional temperature field characteristics and the temperature drift component.

[0036] In the embodiments of the present application, first, the axial-radial gradient information is subjected to spatial interpolation processing to generate continuous gradient distribution data; then the temperature measurement data, the rate of change data and the gradient data are aligned in time sequence to construct multi-dimensional input features containing spatial characteristics and dynamic characteristics; then the spatial coupling term of the temperature measurement matrix and the gradient tensor, and the dynamic coupling term of the rate of change vector and the gradient tensor are calculated; finally, the two types of coupling terms are fused to generate enhanced features according to a preset proportion, and a nonlinear mapping relationship with the temperature drift component is established through iterative optimization.

[0037] For example, the temperature data [25.0, 26.8, 28.5, 30.2]℃, the rate of change [0.2, 0.23, 0.25, 0.27]℃ / s and the gradient data [[0.5, 0.6], [0.55, 0.65]]℃ / cm are constructed to construct multi-dimensional features, the spatial coupling term 2.85 and the dynamic coupling term 0.145 are calculated, and the mapping relationship with the 6.9° / h drift component is established after fusion according to the 7:3 proportion.

[0038] Step 104: based on the nonlinear mapping relationship, a drift prediction value is generated, the drift prediction value is associated with the temperature drift component, and the association result is input into a least mean square adaptive filter (LMS) for dynamic error compensation.

[0039] In step 104, the drift prediction value is a temperature drift estimate calculated based on multidimensional temperature field data using a nonlinear mapping relationship. This value reflects the expected gyro output drift under the current temperature field conditions. This prediction value serves as a baseline compensation for subsequent dynamic error correction. The LMS adaptive filter is a filter that automatically adjusts its parameters and is used to dynamically track error changes.

[0040] In an embodiment of the present application, the difference between the drift prediction value and the actual temperature drift component is first calculated to obtain a residual sequence; then the residual sequence is segmented in the time domain to extract statistical characteristics such as the fluctuation intensity and change trend of each segment; the step size factor, memory factor and other parameters of the filter are dynamically adjusted according to the characteristic value; finally, the residual sequence is filtered using the adjusted parameters to output a real-time updated compensation amount.

[0041] For example, the residual between the predicted value 2.1° / h and the actual value 6.9° / h is 4.8° / h. After analysis, the fluctuation intensity is 0.05. The step size factor is adjusted to 0.015, and the compensation amount sequence is generated as 0.0054, 0.0099, 0.01341, 0.01657, and 0.01891° / h.

[0042] Step 105: superimpose the compensation result and the drift prediction value to generate a compensated gyro output signal.

[0043] In step 105, the compensated gyro output signal is a correction result after removing the temperature drift error from the original output. By superimposing static prediction compensation and dynamic residual compensation, the measurement deviation caused by temperature is eliminated, and finally an accurate signal closer to the true angular velocity is output.

[0044] In an embodiment of the present application, the compensation amount output by the LMS filter is algebraically added to the predicted value to obtain a correction value; the correction value is then amplitude-limited to ensure that it does not exceed the physical range of the gyroscope; finally, the correction value is subtracted from the original output signal to obtain the accurately compensated gyroscope output.

[0045] For example, the compensation amount of 0.01891° / h is added to the predicted value of 2.1° / h to obtain 2.11891° / h, which is then limited to 2.12° / h and subtracted from 15.3° / h, resulting in a final output of 13.18° / h.

[0046] This method effectively suppresses the gyroscope drift error in complex variable temperature environments by combining multi-dimensional temperature field feature extraction, signal decomposition and dynamic compensation, improves the stability and reliability of the output signal, and solves the problem of insufficient compensation of traditional methods when the temperature changes rapidly and gradients.

[0047] In order to solve the problem that the traditional temperature drift compensation method is not adaptable enough in a dynamic temperature changing environment, in some embodiments, step 104: the drift prediction value is associated with the temperature drift component, and the association result is input into the LMS adaptive filter for dynamic error compensation, such as Figure 2 Shown, including: Step 201: Subtract the drift prediction value from the temperature drift component to generate a residual sequence.

[0048] In step 201, the residual sequence is the difference between the drift prediction value and the actual temperature drift component. It reflects the residual error of the nonlinear mapping model that is not fitted. This sequence contains the dynamic error characteristics when the temperature changes rapidly. The residual sequence is the correlation result.

[0049] In an embodiment of the present application, the drift prediction value calculated based on the temperature field data is subtracted from the actual temperature drift component obtained by signal decomposition at each time point to generate a residual data sequence containing timing characteristics, providing error input for subsequent adaptive filtering.

[0050] Step 202: Input the residual sequence into an LMS adaptive filter, and dynamically update the adjustment factor of the LMS adaptive filter based on the statistical feature set of the residual sequence.

[0051] In step 202, the statistical feature set includes three characteristic indicators extracted from the residual sequence: fluctuation intensity, change trend, and correlation decay. Fluctuation intensity represents the severity of error changes, change trend reflects the direction of error evolution, and correlation decay describes the duration of error impact. The adjustment factors include the step size parameter that controls the convergence speed, the memory parameter that maintains the consistency of the compensation direction, and the length parameter that determines the range of historical data used.

[0052] In an embodiment of the present application, the residual sequence is first divided into several time period subsequences, and three statistics, namely, the fluctuation range, the change slope, and the autocorrelation characteristics, are calculated for each subsequence. When the fluctuation exceeds the set standard, the step size parameter is increased to speed up the response. When a continuous unidirectional change is detected, the memory parameter is enhanced to maintain the compensation direction. The amount of historical data used is dynamically adjusted according to the duration of the error impact.

[0053] Step 203: using the updated adjustment factor, perform an approximation operation on the residual sequence to generate a target compensation amount, which is a compensation result.

[0054] In step 203, the approximation operation is a process of gradually correcting the residual sequence using the adjusted filter parameters. The target compensation amount is the final compensation value output after dynamic adjustment.

[0055] In an embodiment of the present application, each data point in the residual sequence is processed in chronological order, the basic compensation amount is calculated according to the current step size parameter, the historical compensation amounts are weighted and fused in combination with the memory parameters, and the iterative update is performed within the limited historical data range, and finally the target compensation amount that matches the real-time error is output.

[0056] Here's a specific example: During the onboard fiber optic gyro temperature compensation process, the difference between the predicted drift value of 2.1° / h and the actual temperature drift component of 6.9° / h was first calculated as the initial residual, a difference of 4.8° / h. This residual sequence was then segmented to obtain five data points: 4.8, 4.7, 4.5, 4.3, and 4.1. Statistical characteristics were calculated for the first residuals of 4.8, 4.7, and 4.5. The fluctuation intensity was determined using the standard deviation formula σ = sqrt[(4.8 - 4.667)² + (4.7 - 4.667)² + (4.5 - 4.667)²] / 3 = 0.152° / h. The trend was calculated using a linear fit slope b = -0.15° / h / point. The correlation decay rate was calculated using the autocorrelation function to be 0.92. Since the fluctuation intensity of 0.152 exceeds the preset threshold of 0.1, the step size factor of the LMS filter is adjusted from 0.01 to 0.015; the absolute value of the change trend of -0.15 exceeds the threshold of 0.1, the directional memory factor is activated and set to 0.9; based on the correlation decay rate of 0.92, the memory depth is set to 5 data points. The residual sequence is approximated using the adjusted parameters. The first data point, 4.8° / h, is scaled by a step factor of 0.015 to obtain a compensation increment of 0.072° / h. Since there is no historical compensation value at the previous moment, 0.072° / h is directly output. The second data point, 4.7° / h, is calculated as an increment of 0.0705° / h. This is weighted by combining the previous compensation value of 0.072° / h and a memory factor of 0.9 to obtain a new compensation value of 0.9 × 0.072 + 0.0705 = 0.1353° / h. All residuals are processed sequentially to generate the compensation value sequence [0.072, 0.1353, 0.1938, 0.2475, 0.2967]° / h. The sequence is superimposed with the predicted value of 2.1° / h to obtain the corrected value sequence [2.172, 2.2353, 2.2938, 2.3475, 2.3967]° / h. After limiting to ensure that it does not exceed 2.4° / h, the compensated output is [13.128, 13.0647, 13.0062, 12.9525, 12.9033]° / h from the original output of 15.3° / h, completing the entire process of dynamic error compensation. In the fluctuation intensity calculation, 4.667 is the average of the three residuals, and the slope of the trend is ,in is the residual value, For time point, and are the mean, Indicates the number of data points.

[0057] In an embodiment of the present application, by dynamically tracking residual characteristics and adaptively adjusting compensation parameters, the system can quickly respond to error changes caused by sudden temperature changes while maintaining the stability of the compensation process, effectively solving the lag and mismatch problems of traditional fixed parameter compensation methods under variable temperature conditions, and improving the compensation effect in complex environments.

[0058] In order to further improve the parameter adjustment accuracy of the LMS adaptive filter in a dynamic temperature-varying environment, in some embodiments, step 202: the adjustment factors of the LMS adaptive filter include an update step size factor, a direction memory factor, and a memory depth factor.

[0059] Based on the statistical feature set of the residual sequence, the adjustment factor of the LMS adaptive filter is dynamically updated, such as Figure 3 Shown, including: Step 301: segment the residual sequence in the time domain through an LMS adaptive filter to obtain multiple residual subsequences.

[0060] In step 301, the residual subsequence is a short-term data segment obtained by dividing the continuous residual data into fixed time windows. Each subsequence contains the residual values ​​of several consecutive sampling points, which are used to reflect the error characteristics in a local time period.

[0061] In an embodiment of the present application, the entire residual sequence is slidingly segmented according to a preset time window length, and partial data overlap is maintained between adjacent subsequences to ensure continuity, thereby obtaining multiple subsequence fragments covering different time periods, providing an analysis unit for subsequent feature extraction.

[0062] Step 302: For each time domain segment, extract a statistical feature set from the corresponding residual subsequence, wherein the statistical feature set includes a fluctuation intensity value, a change trend value, and a correlation decay rate.

[0063] In step 302, the fluctuation intensity value represents the severity of the residual variation within the subsequence and is obtained by calculating the dispersion of the data points. The trend value reflects the direction of residual evolution and is represented by the fitted slope. The correlation decay rate describes the duration of the residual effect and is obtained by calculating the autocorrelation characteristics.

[0064] In an embodiment of the present application, the standard deviation of each residual subsequence is calculated as the fluctuation intensity, the slope of the straight line is fitted using the least squares method as the change trend, and the rate of decrease in the correlation between adjacent data points is calculated through the autocorrelation function as the decay rate, forming three key indicators that describe the residual characteristics of this period.

[0065] Step 303: When the fluctuation intensity value is greater than a preset first threshold, increase the update step factor of the corresponding time domain segment.

[0066] In step 303, a preset first threshold is used to determine whether residual fluctuations are severe. When the fluctuation intensity exceeds this threshold, it indicates a dramatic temperature change, requiring an increase in the step size factor to accelerate the compensation response. For example, if the first threshold is set to 0.1° / h, and the calculated fluctuation intensity of a residual subsequence reaches 0.15° / h (calculated using the standard deviation formula), the step size factor adjustment is triggered.

[0067] In an embodiment of the present application, when the fluctuation intensity of a subsequence exceeds a set threshold, the step size factor is gradually increased according to a preset adjustment rule, so that the filter can track the drastically changing error more quickly, and an upper limit is set to prevent oscillation caused by excessive adjustment. The specific implementation process is as follows: first, the proportion of the fluctuation intensity exceeding the threshold is calculated, and then the corresponding adjustment multiple is found in the preset step size adjustment coefficient table based on the proportion, and finally the current updated step size factor is multiplied by the multiple to obtain a new step size factor value; for example, the current fluctuation intensity value is 0.05, which exceeds the first threshold value of 0.03, and the excess ratio is 66%. The adjustment multiple obtained from the table is 1.2, and the original step size factor of 0.01 is adjusted to 0.01×1.2=0.012.

[0068] Step 304: When the change trend value is greater than a preset second threshold, activating the direction memory factor of the corresponding time domain segment.

[0069] In step 304, the preset second threshold is used to determine whether the residual error is trending. Exceeding this threshold indicates a persistent unidirectional error trend, and the directional memory factor needs to be activated to maintain a stable compensation direction. For example, if the second threshold is set to 0.1° / h / point and the slope of the trend of the fitted residual subsequence is -0.15° / h / point, the directional memory factor is activated to 0.9.

[0070] In an embodiment of the present application, when it is detected that the subsequence change trend continues to exceed a threshold, the direction memory function is activated so that the current compensation direction inherits the main direction of the historical trend, reducing unnecessary direction adjustments.

[0071] Step 305: Dynamically adjust the memory depth factor of the corresponding time domain segment according to the correlation decay rate.

[0072] In an embodiment of the present application, the memory depth is dynamically adjusted according to the correlation decay rate. When the decay is fast, the amount of historical data used is reduced to improve the response speed, and when the decay is slow, the amount of historical data used is increased to ensure stability.

[0073] Here's a specific example: In the process of temperature compensation of the vehicle-mounted fiber optic gyroscope, based on the residual sequence [4.8, 4.7, 4.5, 4.3, 4.1]° / h obtained in the previous embodiment, the time domain is first segmented with 3 data points as a window to obtain two overlapping subsequences [4.8, 4.7, 4.5]° / h and [4.7, 4.5, 4.3]° / h. For the first subsequence [4.8, 4.7, 4.5]° / h, its average value is calculated to be 4.667° / h, and the fluctuation intensity is obtained by the formula σ=sqrt[(4.8-4.667)²+(4.7-4.667)²+(4.5-4.667)²] / 3 to obtain 0.152° / h, where 4.667 is the average value of the three residuals; the trend of change is fitted by the least squares method. The calculated value is -0.15° / h / point, where is the residual value, is the time point number, and The correlation decay rate is calculated by calculating the autocorrelation function of adjacent data points to obtain a value of 0.92. Because the fluctuation intensity of 0.152 exceeds the preset first threshold of 0.1, the update step factor is increased from 0.01 to 0.015. The absolute value of the trend of -0.15 exceeds the second threshold of 0.1, the directional memory factor is activated and set to 0.9. Since the decay rate of 0.92 is in the range of 0.9-1.0, the memory depth is set to 5 data points. The second subsequence [4.7, 4.5, 4.3]° / h calculates a fluctuation intensity of 0.2° / h, a trend of -0.2° / h / point, and a decay rate of 0.88. Accordingly, the step factor is increased to 0.018, the directional memory factor remains at 0.9, and the memory depth is adjusted to 4 points.

[0074] In an embodiment of the present application, the filter parameters are adaptively adjusted by dynamically analyzing the residual characteristics, so that the system can not only quickly respond to the error changes caused by sudden temperature changes, but also maintain the stability of the compensation process, effectively solving the adaptability problem of fixed parameter filters under complex variable temperature conditions, and improving the compensation accuracy and reliability.

[0075] To further improve the accuracy and stability of compensation amount generation, in some embodiments, step 203: using the updated adjustment factor to perform an approximation operation on the residual sequence to generate a target compensation amount includes: Step 401: using the updated adjustment factor, traverse each data point in the residual sequence in chronological order and calculate the initial compensation amount corresponding to each data point.

[0076] In step 401, the initial compensation amount refers to a basic compensation value directly calculated based on the current residual data point and the adjustment factor, reflecting the initial correction amount of the error at that moment.

[0077] In the embodiments of the present application, the data points in the residual sequence are processed one by one in chronological order, each data point is multiplied by the updated step factor to obtain an initial compensation amount reflecting the current error size, which provides a basic input for subsequent recursive superposition.

[0078] Step 402: recursively superimpose the initial compensation increment at the current time and the initial compensation amount at the previous time to generate an intermediate compensation amount.

[0079] In step 402, the intermediate compensation amount is a transition compensation value generated by combining the current compensation increment and the historical compensation amount, which considers the current error characteristics and maintains the continuity of compensation.

[0080] In the embodiments of the present application, the initial compensation amount calculated at the current time and the compensation result at the previous time are weighted and summed according to the weight determined by the direction memory factor, so that the compensation amount responds to the latest error change and inherits the historical compensation trend, avoiding sudden changes in the compensation process.

[0081] Step 403: boundary constraint processing is performed on the intermediate compensation amount to generate a target compensation amount.

[0082] In step 403, boundary constraint processing is a process of checking the rationality of the compensation amount to ensure that the compensation amount is within the range allowed by the physical characteristics of the gyroscope.

[0083] In the embodiments of the present application, it is checked whether the intermediate compensation amount exceeds the preset upper and lower limit values, and when it exceeds, the truncation processing is performed according to the boundary value, and the mutation amplitude of the compensation amount is limited according to the change rate to ensure the safety and stability of the output compensation amount.

[0084] The following is a specific example: In the process of temperature compensation of the on-board fiber optic gyroscope, based on the obtained residual sequence [4.8, 4.7, 4.5, 4.3, 4.1]° / h and the adjustment parameter step factor 0.015 and direction memory factor 0.9, the first residual point 4.8° / h is processed first, and it is multiplied by the step factor to obtain the initial compensation amount 4.8×0.015=0.072° / h; then the second residual point 4.7° / h is processed, and the initial compensation amount 4.7×0.015=0.0705° / h is calculated, and it is combined with the previous compensation amount 0.072° / h according to the memory. A weighted addition factor of 0.9 is used to obtain a new intermediate compensation value of 0.9 × 0.072 + 0.0705 = 0.1353° / h. Continuing with the third residual point of 4.5° / h, the initial compensation value of 4.5 × 0.015 = 0.0675° / h is added to the previous compensation value of 0.1353° / h to obtain 0.9 × 0.1353 + 0.0675 = 0.1938° / h. This process is repeated for the entire sequence, resulting in compensation values ​​of 0.072, 0.1353, 0.1938, 0.2475, and 0.2967° / h. During the boundary constraint processing phase, the upper limit of the single-step compensation value is set to 0.3° / h. After checking that all intermediate compensation values ​​are within the limit, they are directly output as the target compensation value.

[0085] In the embodiment of the present application, the compensation response speed is ensured by dynamically adjusting the step size factor, the compensation continuity is maintained by using the directional memory factor, and the output rationality is guaranteed by combining the boundary constraints, so that the generated compensation amount can both quickly track the error changes and maintain stable output, effectively solving the compensation lag and overshoot problems when the temperature changes rapidly.

[0086] To further improve the accuracy of the temperature drift prediction model, in some embodiments, step 103: establishing a nonlinear mapping relationship based on the multi-point temperature measurement data, the temperature change rate data, the axial-radial gradient information, and the temperature drift component, includes: Step 501: Perform spatial interpolation processing on the axial and radial gradient information to generate temperature gradient distribution data.

[0087] In step 501 , the temperature gradient distribution data is continuous gradient field data generated by performing spatial interpolation processing on discrete axial and radial gradient information, reflecting the spatial variation trend of temperature.

[0088] In an embodiment of the present application, a bilinear interpolation method is used to expand the gradient information obtained from a limited number of measurement points into continuous distribution data covering the entire gyroscope surface, providing spatial gradient information for constructing a complete temperature field feature.

[0089] Step 502: Aligning the multi-point temperature measurement data, the temperature rate of change data and the temperature gradient distribution data according to timestamps to construct multi-dimensional temperature field data, the multi-dimensional temperature field data including a temperature measurement data matrix, a temperature rate of change vector and a temperature gradient tensor.

[0090] In step 502, the multi-dimensional temperature field data is a comprehensive feature set integrating temperature spatial distribution, time rate of change and gradient field, wherein the temperature measurement data matrix records temperature values at different positions, the temperature rate of change vector represents the rate of change, and the temperature gradient tensor describes the spatial change relationship.

[0091] In the embodiment of the present application, the interpolated gradient data is aligned with the original temperature measurement data and the rate of change data according to a unified timestamp to construct a multi-dimensional feature matrix containing spatial distribution characteristics and dynamic change characteristics, thereby laying a foundation for subsequent feature fusion.

[0092] Step 503: Calculating a spatial coupling term of the temperature measurement data matrix and the temperature gradient tensor, and calculating a dynamic coupling term of the temperature rate of change vector and the temperature gradient tensor.

[0093] In step 503, the spatial coupling term represents the cooperative change relationship between the temperature value and the gradient field, and the dynamic coupling term represents the influence degree of the temperature rate of change on the gradient evolution. The dynamic coupling term is a quantitative index representing the dynamic interaction between the temperature rate of change and the temperature gradient field, which is obtained by calculating the dot product of the temperature rate of change vector and the temperature gradient tensor, and reflects the transient response characteristics of the gradient field when the temperature changes rapidly. The numerical value of the dynamic coupling term represents the degree of influence of the temperature change on the spatial heat distribution. For example, when the temperature rises rapidly, the numerical value of the coupling term increases, indicating that the temperature gradient field is dynamically adjusting.

[0094] In the embodiment of the present application, the spatial coupling strength of the temperature measurement value and the gradient tensor is calculated by matrix point multiplication, and the dynamic interaction strength of the temperature rate of change and the gradient tensor is calculated by vector dot product, thereby extracting the correlation between the spatial and dynamic characteristics in the temperature field.

[0095] Step 504: Fusing the spatial coupling term and the dynamic coupling term according to a preset proportion to generate an interaction factor, and generating an enhanced temperature field feature based on the interaction factor.

[0096] In step 504, the interaction factor is a composite index obtained by weighted fusion of the spatial and dynamic coupling characteristics. The enhanced temperature field feature is a high-dimensional feature vector fused with the interaction factor.

[0097] In the embodiment of the present application, the two types of coupling terms are linearly combined into an interaction factor according to a preset importance proportion, and the factor is spliced with the original temperature field feature to form an enhanced feature set containing deep correlation characteristics.

[0098] Step 505: Using the enhanced temperature field feature as a mapping source and the temperature drift component corresponding to the timestamp as a mapping target, a nonlinear mapping relationship from the mapping source to the mapping target is established through an iterative optimization process.

[0099] In the embodiment of the present application, the enhanced features are used as input and the actual drift component is used as output. An iterative optimization algorithm is used to train the prediction model. The prediction error is minimized by continuously adjusting the model parameters, and finally a high-precision nonlinear mapping relationship is established.

[0100] Here's a specific example: During the startup phase of the on-board fiber optic gyroscope, based on the collected data of the four temperature measurement points of 25.0℃, 26.8℃, 28.5℃, and 30.2℃ and the calculated axial gradient of 0.5℃ / cm and radial gradient of 0.6℃ / cm, the gradient data is first bilinearly interpolated to generate a temperature gradient distribution matrix containing 9 nodes, where the gradient value of the newly added interpolation point is obtained by linear calculation of the gradient of the adjacent measurement point. The interpolated gradient matrix is ​​aligned with the original temperature data of 25.0℃, 26.8℃, 28.5℃, 30.2℃ and the change rate of 0.2℃ / s, 0.23℃ / s, 0.25℃ / s, and 0.27℃ / s according to millisecond timestamps to construct a 4×4 temperature measurement matrix, a 4-dimensional change rate vector, and a 3×3 gradient tensor. When calculating the spatial coupling term, the temperature measurement matrix is ​​multiplied by the corresponding elements of the gradient tensor and the sum is 2.85. The calculation formula is: in is the temperature matrix element, is the gradient tensor element; when calculating the dynamic coupling term, the dot product of the rate of change vector and the main diagonal element of the gradient tensor is 0.145, and the calculation formula is in is the rate of change element, The spatial coupling term 2.85 and the dynamic coupling term 0.145 were fused with a weight of 7:3 to obtain an interaction factor of 2.85 × 0.7 + 0.145 × 0.3 = 2.0085. This factor was combined with the original temperature field features to generate a 12-dimensional enhanced feature. Using this enhanced feature as input and the 6.9° / h temperature drift component as output, a gradient descent algorithm was used for 300 iterations of training. The resulting nonlinear mapping relationship achieved a prediction error of less than 0.05° / h on the test set, providing an accurate drift prediction benchmark for subsequent dynamic compensation.

[0101] In the embodiments of the present application, by constructing a multi-dimensional temperature field feature that integrates spatial gradients and dynamic changes and extracting their deep coupling relationships, the established prediction model can more comprehensively characterize the nonlinear relationship between temperature field and drift, thereby improving the accuracy and robustness of drift prediction in complex temperature-varying environments.

[0102] To further improve the extraction accuracy of the temperature drift component, in some embodiments, step 102: the variational mode decomposition is performed on the gyro original output signal to generate a temperature drift component and a non-temperature noise component, comprising: Step 601: performing a variational mode decomposition operation on the gyro original output signal to obtain a plurality of intrinsic mode components.

[0103] In step 601, the intrinsic mode component refers to a signal component with different vibration characteristics separated from the original signal by the variational mode decomposition method, and each component contains specific frequency range and amplitude characteristics.

[0104] In the embodiments of the present application, the gyro original output signal is input into the variational mode decomposition algorithm, and the complex signal is adaptively decomposed into a plurality of intrinsic mode components arranged from high to low frequency by a preset decomposition layer number and a constraint condition, thereby providing a basis for subsequent temperature-related component screening.

[0105] Step 602: calculating the correlation coefficient of each intrinsic mode component and the temperature change rate data.

[0106] In step 602, the correlation coefficient is an index for measuring the degree of linear correlation between the intrinsic mode component and the temperature change rate data, and the numerical range is between -1 and 1. The greater the absolute value, the stronger the correlation.

[0107] In the embodiments of the present application, for each intrinsic mode component, the Pearson correlation coefficient of the intrinsic mode component and the temperature change rate data is calculated, and the correlation degree between the two is quantified by analyzing the synchronicity of the component signal and the temperature change trend.

[0108] Step 603: screening the key intrinsic mode component whose correlation coefficient exceeds the preset correlation threshold from all intrinsic mode components, merging all key intrinsic mode components into a temperature drift component, and classifying the remaining intrinsic mode components as non-temperature noise components.

[0109] In step 603, the key intrinsic mode component refers to a signal component related to temperature change, and its correlation coefficient exceeds the preset threshold.

[0110] In the embodiments of the present application, a reasonable correlation threshold is set, and the components whose correlation coefficients exceed the threshold are screened as temperature drift components for merging, and the components with low correlation are removed, thereby realizing effective separation of temperature drift signals and noise.

[0111] The following is a specific example: During the startup of the vehicle-mounted fiber optic gyroscope, based on the collected original gyroscope output signal of 15.3° / h, the number of layers of variational mode decomposition was first set to 5, and the signal was decomposed into 5 eigenmode components through iterative optimization, with amplitudes of 6.2° / h, 4.1° / h, 2.8° / h, 1.5° / h, and 0.7° / h, respectively. For each component, combined with the synchronously collected temperature change rate data of 0.2° / s, 0.23° / s, 0.25° / s, and 0.27° / s, according to the correlation coefficient formula Calculate the correlation. The calculated correlation coefficients for each component are 0.62, 0.88, 0.91, 0.65, and 0.58, respectively. Set the correlation threshold to 0.8 and select the two components of 4.1° / h and 2.8° / h, corresponding to correlation coefficients of 0.88 and 0.91. Add their amplitudes (4.1 + 2.8 = 6.9° / h) to determine the temperature drift component. Simultaneously, the remaining three components (6.2° / h, 1.5° / h, and 0.7° / h) are combined into 8.4° / h and removed as non-temperature noise.

[0112] In the embodiment of the present application, by combining variational mode decomposition and correlation analysis, the signal components closely related to temperature changes can be accurately separated, non-temperature interference such as vibration can be effectively filtered out, and a pure input signal is provided for subsequent temperature drift compensation, thereby improving the extraction accuracy of temperature drift components under complex working conditions.

[0113] To further improve the stability and reliability of the gyro output signal, in some embodiments, step 105: superimposing the compensation result with the drift prediction value to generate a compensated gyro output signal includes: Step 701: Perform time domain smoothing on the compensation result to generate a stable compensation amount.

[0114] In step 701, the stable compensation amount refers to the compensation result after smoothing and filtering, which makes the compensation amount change more smoothly by suppressing high-frequency fluctuations.

[0115] In an embodiment of the present application, a sliding average filtering method is used to process the original compensation result. By taking the average value of multiple adjacent compensation amounts, random fluctuations are eliminated to generate a smooth and stable compensation amount sequence, thereby avoiding sudden changes in the compensation process.

[0116] Step 702: algebraically add the stability compensation amount and the drift prediction value to generate a corrected drift amount.

[0117] In step 702 , the corrected drift amount is a comprehensive compensation value obtained by directly adding the stable compensation amount to the drift prediction value, which includes two parts: static prediction and dynamic compensation.

[0118] In the embodiments of the present application, the compensation quantity after smoothing processing and the drift prediction value at the corresponding moment are added one by one according to the time point to form the final compensation quantity considering the temperature field characteristic modeling result and the real-time error tracking result at the same time.

[0119] Step 703: amplitude limiting processing is performed on the modified drift quantity.

[0120] In step 703, the amplitude limiting processing is a process of checking the reasonableness of the modified drift quantity, and ensuring that the compensation quantity is within the range allowed by the physical characteristics of the gyroscope.

[0121] In the embodiments of the present application, the upper and lower threshold values of the compensation quantity are set, and when the modified drift quantity exceeds the threshold value, it is forced to be limited within the boundary value, so as to prevent the output from being abnormal due to excessive compensation.

[0122] Step 704: removing the amplitude limiting processed modified drift quantity from the original output signal of the gyroscope to obtain the compensated gyroscope output signal.

[0123] In the embodiments of the present application, the amplitude limiting processed modified drift quantity is subtracted from the synchronously collected original gyroscope output signal to obtain the accurate output signal after eliminating the influence of temperature drift.

[0124] The following is a specific example: During the startup of the vehicle-mounted fiber optic gyroscope, based on the obtained compensation value sequence of 0.0054, 0.0099, 0.01341, 0.01657, 0.01891° / h and the drift prediction value of 2.1° / h, the compensation value is first processed by three-point sliding average. The first point 0.0054° / h remains unchanged, and the second point takes the average value of the first three compensation values ​​(0.0054+0.0099+0.01341) / 3=0.00957° / h. h, the third point (0.0099 + 0.01341 + 0.01657) / 3 = 0.01329° / h, the fourth point (0.01341 + 0.01657 + 0.01891) / 3 = 0.01630° / h, and the final point 0.01891° / h remains unchanged, resulting in a smoothed stable compensation sequence of [0.0054, 0.00957, 0.01329, 0.01630, 0.01891]° / h. These stable compensations are added point by point to the predicted value of 2.1° / h to obtain the corrected drift sequence of [2.1054, 2.10957, 2.11329, 2.11630, 2.11891]° / h. The maximum allowable compensation for the system is set to 2.12° / h. After checking that each correction value is within the limit, it is directly used for final compensation. These correction values ​​are sequentially subtracted from the original gyro output of 15.3° / h, resulting in the compensated output signal [13.1946, 13.19043, 13.18671, 13.1837, 13.18109]° / h. In the sliding average calculation formula, each smoothed compensation value is the arithmetic mean of the three adjacent original compensation values ​​to ensure smooth compensation changes. The corrected drift is calculated by simply algebraically adding the compensation value to the predicted value. During the limiting process, each correction value is checked to see if it exceeds the preset upper limit of 2.12° / h. This series of processing effectively eliminates the effects of temperature drift in the final gyro signal output while ensuring smooth output changes, enabling the gyro to maintain stable and reliable measurement performance during temperature fluctuations during the startup phase.

[0125] In the embodiment of the present application, the compensation stability is improved by smoothing processing, and the safety is ensured by combining limiting protection. The compensated signal finally generated not only effectively eliminates the influence of temperature drift, but also maintains the smoothness of output changes, thereby improving the measurement reliability of the gyroscope in complex temperature environments.

[0126] Figure 4 A schematic diagram of a specific implementation scheme of a fiber optic gyro drift compensation system integrating LMS adaptive filtering provided in the embodiment of the present application, with reference to Figure 4 , the system may include: The acquisition module 41 is used to acquire multi-point temperature measurement data, temperature change rate data, axial radial gradient information and gyro original output signal of the fiber optic gyroscope.

[0127] The decomposition module 42 is configured to perform variational mode decomposition on the original gyro output signal to generate a temperature drift component and a non-temperature noise component, and filter out the non-temperature noise component.

[0128] The establishing module 43 is configured to establish a nonlinear mapping relationship based on the multi-point temperature measurement data, the temperature change rate data, the axial-radial gradient information, and the temperature drift component.

[0129] The generating module 44 is configured to generate a drift prediction value based on the nonlinear mapping relationship, associate the drift prediction value with the temperature drift component, and input the association result into an LMS adaptive filter for dynamic error compensation.

[0130] The superposition module 45 is configured to superpose the compensation result with the drift prediction value to generate a compensated gyro output signal.

[0131] The fiber optic gyroscope drift compensation system integrating LMS adaptive filtering in the embodiment of the present application is used to implement the aforementioned fiber optic gyroscope drift compensation method integrating LMS adaptive filtering. Therefore, the specific implementation method of the fiber optic gyroscope drift compensation system integrating LMS adaptive filtering can be seen in the embodiment part of the fiber optic gyroscope drift compensation method integrating LMS adaptive filtering in the previous text. Its specific implementation method can refer to the description of the corresponding embodiments of each part, which will not be repeated here.

[0132] The present application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for implementing the steps of any of the above-mentioned fiber optic gyroscope drift compensation methods integrating LMS adaptive filtering when executing the computer program.

[0133] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of any of the above-mentioned fiber optic gyroscope drift compensation methods integrating LMS adaptive filtering are implemented.

[0134] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, a read-only memory, a random access memory, a mobile hard disk, a magnetic disk, or an optical disk.

[0135] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of any of the above-mentioned fiber optic gyroscope drift compensation method embodiments integrating LMS adaptive filtering are implemented.

[0136] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0137] The above is a detailed introduction to the fiber optic gyroscope drift compensation method and system integrated with LMS adaptive filtering provided by the present application. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core ideas of the present application. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the present application.

Claims

1. A fiber optic gyroscope drift compensation method integrating LMS adaptive filtering, characterized in that: include: Collect multi-point temperature measurement data, temperature change rate data, axial radial gradient information and gyro original output signal of the fiber optic gyroscope; performing variational mode decomposition on the original output signal of the gyroscope to generate a temperature drift component and a non-temperature noise component, and filtering out the non-temperature noise component; Establishing a nonlinear mapping relationship based on the multi-point temperature measurement data, the temperature change rate data, the axial-radial gradient information, and the temperature drift component; Based on the nonlinear mapping relationship, a drift prediction value is generated, the drift prediction value is associated with the temperature drift component, and the association result is input into an LMS adaptive filter for dynamic error compensation; The compensation result is superimposed on the drift prediction value to generate a compensated gyro output signal.

2. The method according to claim 1, characterized in that The associating the drift prediction value with the temperature drift component and inputting the association result into an LMS adaptive filter for dynamic error compensation includes: Subtracting the drift prediction value from the temperature drift component to generate a residual sequence; Inputting the residual sequence into an LMS adaptive filter, and dynamically updating an adjustment factor of the LMS adaptive filter based on a statistical feature set of the residual sequence; An approximation operation is performed on the residual sequence using the updated adjustment factor to generate a target compensation amount, which is a compensation result.

3. The method according to claim 2, characterized in that The adjustment factors of the LMS adaptive filter include an update step size factor, a direction memory factor, and a memory depth factor; Dynamically updating the adjustment factor of the LMS adaptive filter based on the statistical feature set of the residual sequence, including: Performing time domain segmentation on the residual sequence through an LMS adaptive filter to obtain multiple residual subsequences; For each time domain segment, extract a statistical feature set from the corresponding residual subsequence, wherein the statistical feature set includes a fluctuation intensity value, a change trend value, and a correlation decay rate; When the fluctuation intensity value is greater than a preset first threshold, increasing the update step factor of the corresponding time domain segment; When the change trend value is greater than a preset second threshold, activating the direction memory factor of the corresponding time domain segment; The memory depth factor of the corresponding time domain segment is dynamically adjusted according to the correlation decay rate.

4. The method according to claim 2, characterized in that The method of performing an approximation operation on the residual sequence using the updated adjustment factor to generate a target compensation amount includes: Using the updated adjustment factor, traverse each data point in the residual sequence in chronological order to calculate the initial compensation amount corresponding to each data point; The initial compensation increment at the current moment is recursively superimposed with the initial compensation amount at the previous moment to generate an intermediate compensation amount; Boundary constraint processing is performed on the intermediate compensation amount to generate a target compensation amount.

5. The method according to claim 1, wherein The establishing of a nonlinear mapping relationship based on the multi-point temperature measurement data, the temperature change rate data, the axial-radial gradient information, and the temperature drift component includes: Performing spatial interpolation processing on the axial-radial gradient information to generate temperature gradient distribution data; Aligning the multi-point temperature measurement data, the temperature change rate data, and the temperature gradient distribution data according to timestamps to construct multi-dimensional temperature field data, the multi-dimensional temperature field data including a temperature measurement data matrix, a temperature change rate vector, and a temperature gradient tensor; Calculating the spatial coupling term between the temperature measurement data matrix and the temperature gradient tensor, and calculating the dynamic coupling term between the temperature change rate vector and the temperature gradient tensor; fusing the spatial coupling term and the dynamic coupling term according to a preset ratio to generate an interaction factor, and generating an enhanced temperature field feature based on the interaction factor; The enhanced temperature field feature is used as a mapping source, the temperature drift component corresponding to the timestamp is used as a mapping target, and a nonlinear mapping relationship from the mapping source to the mapping target is established through an iterative optimization process.

6. The method according to claim 1, characterized in that The performing variational mode decomposition on the original gyro output signal to generate a temperature drift component and a non-temperature noise component includes: Performing a variational mode decomposition operation on the original output signal of the gyroscope to obtain a plurality of eigenmode components; For each eigenmode component, calculating a correlation coefficient between the eigenmode component and the temperature change rate data; The key eigenmode components whose correlation coefficients exceed a preset correlation threshold are screened out from all eigenmode components, all the key eigenmode components are merged into temperature drift components, and the remaining eigenmode components are classified as non-temperature noise components.

7. The method according to claim 1, characterized in that The step of superimposing the compensation result and the drift prediction value to generate a compensated gyro output signal includes: Perform time domain smoothing on the compensation results to generate a stable compensation amount; algebraically adding the stability compensation amount to the drift prediction value to generate a corrected drift amount; performing amplitude limiting processing on the corrected drift amount; The corrected drift amount after the amplitude limiting process is removed from the original gyro output signal to obtain a compensated gyro output signal.

8. A fiber optic gyroscope drift compensation system integrating LMS adaptive filtering, characterized in that: include: An acquisition module is used to collect multi-point temperature measurement data, temperature change rate data, axial radial gradient information, and the original output signal of the fiber optic gyroscope; a decomposition module, configured to perform variational mode decomposition on the original output signal of the gyroscope to generate a temperature drift component and a non-temperature noise component, and filter out the non-temperature noise component; An establishing module, configured to establish a nonlinear mapping relationship based on the multi-point temperature measurement data, the temperature change rate data, the axial-radial gradient information, and the temperature drift component; A generating module, configured to generate a drift prediction value based on the nonlinear mapping relationship, associate the drift prediction value with the temperature drift component, and input the association result into an LMS adaptive filter for dynamic error compensation; The superposition module is used to superpose the compensation result with the drift prediction value to generate a compensated gyro output signal.

9. An electronic device, characterized in that: include: memory for storing computer programs; A processor is configured to implement the steps of the fiber optic gyroscope drift compensation method integrating LMS adaptive filtering as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the fiber optic gyroscope drift compensation method integrated with LMS adaptive filtering as claimed in any one of claims 1 to 7 can be implemented.

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