Laser gyroscope temperature compensation method based on Gaussian process regression and distributed temperature measurement

Abstract

The invention relates to the technical field of laser gyroscopes, in particular to a laser gyroscope temperature compensation method based on Gaussian process regression and distributed temperature measurement, which can be used for temperature compensation of a laser gyroscope in a complex temperature change environment and comprises the following steps: S1, distributed temperature measurement configuration of the laser gyroscope; s2, constructing a laser gyroscope temperature compensation model; s3, the SOC predicts the temperature drift in real time and performs temperature compensation; and S4, correcting the angular velocity output # imgabs0 # of the laser gyroscope at the moment k by SOC to obtain the corrected angular velocity output # imgabs1 # at the moment k. According to the method, a distributed temperature measurement scheme and Gaussian process regression are combined, and the angular velocity output precision of the laser gyroscope after temperature compensation is remarkably improved.

Description

Technical Field

[0001] The invention relates to the technical field of laser gyroscopes, and in particular to a laser gyroscope temperature compensation method based on Gaussian process regression and distributed temperature measurement, which can be used for temperature compensation of a laser gyroscope in a complex temperature change environment. Background Art

[0002] Laser gyro is an angular velocity sensor based on the Sagnac effect, which can be used for angular velocity measurement in the fields of navigation, guidance and control. High-precision laser gyro can measure tiny angular velocities such as the rotation of the earth and the deformation of the hull, and is one of the most important sensors in the field of measurement. Since the laser gyro is an optical instrument based on the Sagnac effect, there are components such as the optical path and laser inside. These components will undergo geometric deformation and changes in physical properties as the external temperature changes, causing the optical path to change, and ultimately causing the laser gyro to produce errors in measurement. This error caused by temperature changes is called temperature drift.

[0003] Researchers have adopted a variety of methods at both the device level and the algorithm level to suppress the temperature drift problem of the laser gyroscope in a complex temperature change environment. From the device level, after using ultra-low expansion coefficient quartz glass and optimizing the internal structure of the gyroscope, there is still a relatively obvious temperature drift. Further optimization of materials and structures not only has a long research cycle and high R&D costs. From the algorithm level, traditional temperature compensation methods usually use linear regression, support vector machines, neural networks and other methods for temperature compensation. By constructing a temperature compensation model, the temperature drift is predicted. However, in actual application, people have found that the temperature drift prediction accuracy of the above methods is low, and the generalization of temperature compensation is also low. The temperature drift problem of the laser gyroscope in a complex temperature change environment has not been effectively solved, which greatly limits the application scenarios of the laser gyroscope. Summary of the invention

[0004] The present invention proposes a laser gyro temperature compensation method based on Gaussian process regression and distributed temperature measurement, which can compensate the temperature drift of the laser gyro in a complex temperature change environment with high precision and effectively solve the problems existing in the prior art.

[0005] The present invention is implemented by the following technical solution: a laser gyro temperature compensation method based on Gaussian process regression and distributed temperature measurement, which is divided into the following steps:

[0006] S1: laser gyro distributed temperature measurement configuration;

[0007] The laser gyro used in S1.1 includes a box, a system on chip (SOC), and a cavity; the cavity is fixed in the box through a mounting hole, and the cavity is equipped with optical components required for laser propagation and photoelectric signal detection, such as an anode, a cathode, a reflector, a path length control mirror, a semi-transparent and semi-reflective mirror, a prism, an aperture, and a photodiode; the system on chip is located on the inner wall of the box, and the system on chip (SOC) is used to collect the output signal of the laser gyro, process and calculate the signal, and finally output the measured angular velocity output to the outside;

[0008] S1.2 Temperature measuring resistors are arranged near the cathode, left anode, right anode, aperture and the mounting boss of the laser gyro, and the temperature measuring resistors are PT1000 temperature measuring resistors. The temperature measuring resistors are arranged to measure the temperature near the cathode, anode, aperture and the mounting boss of the box body to characterize the global temperature characteristics of the laser gyro.

[0009] S1.3 combines the temperature measuring resistors at the five points in S1.2 into a temperature measuring bridge and inputs it into the SOC, which measures the temperature of each point in real time. The SOC sends the angular velocity output of the laser gyro and the temperature of each temperature point to the computer through a data transmission cable;

[0010] S2: Constructing laser gyro temperature compensation model

[0011] S2.1 Place the laser gyro in the temperature rapid change test chamber, install and fix the laser gyro in the temperature rapid change test chamber, and connect the power cord and data transmission cable of the laser gyro to the power supply and computer outside the temperature rapid change test chamber respectively;

[0012] S2.2 Set the temperature change program of the temperature rapid change test chamber:

[0013] 1) Turn on the machine and keep it at 20℃ for 2 hours;

[0014] 2) Cool down to -40°C at a temperature ramp rate of 0.5°C / min and keep warm for 2 hours;

[0015] 3) Raise the temperature to 60°C at a temperature ramp rate of 0.5°C / min and keep warm for 2 hours;

[0016] 4) Cool down to 20°C at a temperature change rate of 0.5°C / min and keep warm for 2 hours;

[0017] 5) Cool down to -40°C at a temperature ramp rate of 1°C / min and keep warm for 2 hours;

[0018] 6) Raise the temperature to 60°C at a temperature ramp rate of 1°C / min and keep warm for 2 hours;

[0019] 7) Cool down to 20°C at a temperature change rate of 1°C / min and keep warm for 2 hours;

[0020] 8) Cool down to -40°C at a temperature ramp rate of 1.5°C / min and keep warm for 2 hours;

[0021] 9) Raise the temperature to 60°C at a temperature ramp rate of 1.5°C / min and keep warm for 2 hours;

[0022] 10) Cool down to 20°C at a temperature ramp rate of 1.5°C / min and keep warm for 2 hours;

[0023] 11) Cool down to -40°C at a temperature ramp rate of 2°C / min and keep warm for 2 hours;

[0024] 12) Raise the temperature to 60°C at a temperature ramp rate of 2°C / min and keep warm for 2 hours;

[0025] 13) Cool down to 20°C at a temperature change rate of 2°C / min, keep warm for 2 hours, and then turn off the machine;

[0026] S2.3 The laser gyro is powered on, and the output data is transmitted to the computer in real time and saved; the temperature rapid change test chamber starts the temperature change program of S2.2, from the first step of power on to the thirteenth step of power off, which takes a total of 143600 seconds, recorded as N seconds; after the temperature change program is completed, the laser gyro is powered off.

[0027] The collected and saved data include:

[0028] 1) The laser gyro outputs angular velocity data per second ω i , subscript i represents the time, i=1,2,3…,N;

[0029] 2) Real-time temperature of the laser gyro at 5 temperature measurement points per second

[0030] 3) Laser gyro status data per second s i , including the laser gyro's light intensity, jitter frequency, control voltage and other state parameters;

[0031] S2.4 A laser gyro temperature compensation model is constructed based on Gaussian process regression (STOunpraseuth, "Gaussian Processes for Machine Learning," J.Amer.Statist.Assoc., pp.429-429, Mar.2008, doi:10.1198 / jasa.2008.s219.); the details are as follows:

[0032] S2.4.1 Construct the input data X and response data Y of the temperature compensation model training set:

[0033] The response data is the temperature drift output data of the laser gyro per second: in is the average angular velocity output by the laser gyro at room temperature, which is calculated from the angular velocity data collected during the 20℃ insulation stage in 1) S2.2;

[0034] The input data is the real-time temperature of the laser gyro's five temperature measurement points per second. And 5 temperature square terms, time gradient terms, and space gradient terms. The specific forms are as follows:

[0035]

[0036] in,

[0037]

[0038] And define

[0039] Use x i represents the temperature at the i-th moment and its square term, time gradient term and space gradient term, that is:

[0040] Therefore, the input data X can be expressed as:

[0041]

[0042] S2.4.2 Calculation of Gaussian Process Regression Kernel Matrix

[0043] The Gaussian process regression kernel matrix K for the input data X is calculated as follows:

[0044] K=K(X,X)

[0045] Among them, K(X,X) represents the Gaussian process regression kernel matrix for calculating the input data X:

[0046]

[0047] K rc = k(x r ,x c );(r=1,2,3…,N,c=1,2,3…,N) represents the kernel function of the input data X, and the subscripts r and c represent the rth row and cth column of K(X,X) respectively; the kernel function can map low-dimensional data to high-dimensional space, thereby improving the nonlinear fitting ability of the model. The expression of the kernel function is:

[0048]

[0049] where σ 2 is the amplitude hyperparameter of the kernel function, which is used to control the variance of the output; l represents the length scale hyperparameter of the kernel function, which is used to control the impact of the input distance on the covariance; based on experience, it is selected as

[0050] σ 2 =0.0001, l=1;

[0051] S2.4.3 Store the calculated Gaussian process regression kernel matrix K, input data X and response data Y into

[0052] SOC of laser gyro;

[0053] S3: SOC real-time prediction of temperature drift and temperature compensation

[0054] S3.1 The actual use time is recorded as time k, and SOC collects the laser gyro angular velocity output ω at time k k And the temperature observation data T at time k k :

[0055]

[0056] S3.2 Constructing the temperature observation vector Z k :

[0057]

[0058] S3.3 SOC calculation temperature observation vector Z k The covariance vector between the training set input data X is:

[0059]

[0060] S3.4 Gaussian process regression prediction mean μ for SOC calculation at time k k :

[0061] μ k =K ZX T K -1 Y

[0062] Among them, the predicted mean μ k Represents an estimate of the output; K ZX T Denotes the covariance vector K ZX The transpose of K -1 represents the inverse matrix of the kernel matrix K;

[0063] The Gaussian process at time k is regressed to predict the mean μ k is the temperature drift of the laser gyro at time k;

[0064] S4 SOC corrects the angular velocity output of the laser gyro at time k Get the corrected angular velocity output at time k

[0065]

[0066] After S4 is executed, k=k+1, and S3 to S4 are executed in a loop, so that the angular velocity output of the laser gyro after temperature compensation at the latest moment can be obtained in real time.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] 1. Based on the working principle and basic characteristics of the laser gyro, the present invention designs a distributed temperature measurement scheme, which can obtain observation data that can comprehensively reflect the working status and temperature characteristics of the laser gyro;

[0069] 2. The present invention performs temperature compensation based on Gaussian process regression. Compared with the traditional linear regression model, it can effectively handle the nonlinear relationship between the laser gyro output and temperature, and is suitable for various types of function approximation; 3. Compared with the traditional support vector machine temperature compensation method, the temperature compensation method based on Gaussian process regression does not require the specification of a complex model structure in advance, which reduces the burden of parameter adjustment;

[0070] 4. Compared with the traditional neural network temperature compensation method, which requires a large amount of training data, the temperature compensation method based on Gaussian process regression can use small sample training data to achieve higher generalization, saving training costs and shortening production cycles;

[0071] 5. The present invention combines a distributed temperature measurement scheme with Gaussian process regression, and the output accuracy of the laser gyro angular velocity is significantly improved after temperature compensation. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 : Schematic diagram of the structure of the laser gyro of the present invention;

[0073] Figure 2 : Layout diagram of the laser gyro distributed temperature measurement solution of the present invention;

[0074] Figure 3 :Implementation process of the present invention;

[0075] Figure 4 :Comparison chart of various temperature compensation solutions. DETAILED DESCRIPTION

[0076] In order to explain the technical solution disclosed in the present invention in detail, further elaboration is made below in conjunction with specific embodiments. The specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0077] Figure 3 The implementation process of the present invention is divided into the following steps:

[0078] S1: laser gyro distributed temperature measurement configuration;

[0079] S2: Construct laser gyro temperature compensation model;

[0080] S3: SOC real-time prediction of temperature drift and temperature compensation

[0081] S4 SOC corrects the angular velocity output of the laser gyro at time k Get the corrected angular velocity output at time k

[0082]

[0083] After each execution of S4, k=k+1, and the process returns to S3 and starts execution again.

[0084] This embodiment has carried out a test experiment, and the angular velocity output of the laser gyro is as follows Figure 4 As shown by the thin solid line, after real-time temperature compensation, we get The superscript M is the test duration of this embodiment. Figure 4 The thick solid line in the figure is the angular velocity output without temperature compensation. It can be seen that it fluctuates violently under temperature changes. The dotted line, dot-dash line and dashed line are different traditional temperature compensation methods. It can be seen from the figure that their trends are not as stable as the thick solid line of the present invention. The statistics of laser gyro output zero bias instability under different temperature compensation methods are shown in Table 1.

[0085] Table 1 Comparison of zero bias instability of temperature compensation using different methods

[0086]

[0087] It can be seen from Table 1 that the zero bias instability of the method proposed in the present invention is the smallest, which reflects that the temperature compensation accuracy of the method is the highest, verifies the effectiveness and superiority of the method, and embodies the beneficial effects of the present invention.

Claims

1. A laser gyro temperature compensation method based on Gaussian process regression and distributed temperature measurement, characterized in that: The method is divided into the following steps: S1: Laser gyro distributed temperature measurement configuration: The laser gyro used in S1.1 includes a box body, a system on chip SOC, and a cavity; the cavity is fixed in the box body through a mounting hole, and the cavity is equipped with an anode, a cathode, a reflector, a path length control mirror, a semi-transparent and semi-reflective mirror, a prism, an aperture, and a photodiode. The optical components required for laser propagation and photoelectric signal detection; the system on chip is located on the inner wall of the box body, and the system on chip SOC is used to collect the output signal of the laser gyro, process and calculate the signal, and finally output the measured angular velocity output to the outside; S1.2 Place temperature measuring resistors near the cathode, left anode, right anode, aperture and the mounting boss of the laser gyro to measure the temperature of the cathode, anode, aperture and the mounting boss of the box to characterize the global temperature characteristics of the laser gyro; S1.3 forms a temperature measuring bridge with the temperature measuring resistors at the five points in S1.2 and inputs it into the SOC, which measures the temperature of each point in real time; the SOC sends the angular velocity output of the laser gyro and the temperature of each temperature point to the computer through the data transmission cable; S2: Constructing laser gyro temperature compensation model S2.1 Place the laser gyro in the temperature rapid change test chamber, install and fix the laser gyro in the temperature rapid change test chamber, and connect the power cord and data transmission cable of the laser gyro to the power supply and computer outside the temperature rapid change test chamber respectively; S2.2 Set the temperature change program of the temperature rapid change test chamber: 1) Turn on the machine and keep it at 20℃ for 2 hours; 2) Cool down to -40°C at a temperature ramp rate of 0.5°C / min and keep warm for 2 hours; 3) Raise the temperature to 60°C at a temperature ramp rate of 0.5°C / min and keep warm for 2 hours; 4) Cool down to 20°C at a temperature change rate of 0.5°C / min and keep warm for 2 hours; 5) Cool down to -40°C at a temperature ramp rate of 1°C / min and keep warm for 2 hours; 6) Raise the temperature to 60°C at a temperature ramp rate of 1°C / min and keep warm for 2 hours; 7) Cool down to 20°C at a temperature change rate of 1°C / min and keep warm for 2 hours; 8) Cool down to -40°C at a temperature ramp rate of 1.5°C / min and keep warm for 2 hours; 9) Raise the temperature to 60°C at a temperature ramp rate of 1.5°C / min and keep warm for 2 hours; 10) Cool down to 20°C at a temperature ramp rate of 1.5°C / min and keep warm for 2 hours; 11) Cool down to -40°C at a temperature ramp rate of 2°C / min and keep warm for 2 hours; 12) Raise the temperature to 60°C at a temperature ramp rate of 2°C / min and keep warm for 2 hours; 13) Cool down to 20°C at a temperature change rate of 2°C / min, keep warm for 2 hours, and then turn off the machine; S2.3 The laser gyro is powered on, and the output data is transmitted to the computer in real time and saved; the temperature rapid change test chamber starts the temperature change program of S2.2, from the first step of power on to the thirteenth step of power off, which takes a total of 143600 seconds, recorded as N seconds; After the temperature change program is completed, the laser gyro is shut down and powered off; The collected and saved data include: 1) The laser gyro outputs angular velocity data per second ω i , subscript i represents the time, i=1,2,3…,N; 2) Real-time temperature of the laser gyro at 5 temperature measurement points per second 3) Laser gyro status data per second s i , including the laser gyro's light intensity, jitter frequency, and control voltage state parameters; S2.4 builds a laser gyro temperature compensation model based on Gaussian process regression; the details are as follows: S2.4.1 Construct the input data X and response data Y of the temperature compensation model training set: The response data is the temperature drift output data of the laser gyro per second: in is the average angular velocity output by the laser gyro at room temperature, which is calculated from the angular velocity data collected during the 20℃ insulation stage in 1) S2.2; The input data is the real-time temperature of the laser gyro's five temperature measurement points per second. And 5 temperature square terms, time gradient terms, and space gradient terms. The specific forms are as follows: in, And define Use x i represents the temperature at the i-th moment and its square term, time gradient term and space gradient term, that is: Therefore, the input data X can be expressed as: S2.4.2 Calculation of Gaussian Process Regression Kernel Matrix The Gaussian process regression kernel matrix K for the input data X is calculated as follows: K=K(X,X) Among them, K(X,X) represents the Gaussian process regression kernel matrix for calculating the input data X: K rc = k(x r ,x c );(r=1,2,3…,N,c=1,2,3…,N) represents the kernel function of the input data X, and the subscripts r and c represent the rth row and cth column of K(X,X) respectively; the kernel function can map low-dimensional data to high-dimensional space. Thereby improving the nonlinear fitting ability of the model, the expression of the kernel function is: where σ 2 is the amplitude hyperparameter of the kernel function, which is used to control the variance of the output; l represents the length scale hyperparameter of the kernel function, which is used to control the effect of input distance on covariance; S2.4.3 storing the calculated Gaussian process regression kernel matrix K, input data X and response data Y into the SOC of the laser gyro; S3: SOC real-time prediction of temperature drift and temperature compensation S3.1 The actual use time is recorded as time k, and SOC collects the laser gyro angular velocity output ω at time k k And the temperature observation data T at time k k : S3.2 Constructing the temperature observation vector Z k : S3.3 SOC calculation temperature observation vector Z k The covariance vector between the training set input data X is: S3.4 Gaussian process regression prediction mean μ for SOC calculation at time k k : μ k =K ZX T K -1 Y Among them, the predicted mean μ k Represents an estimate of the output; K ZX T Denotes the covariance vector K ZX The transpose of K -1 represents the inverse matrix of the kernel matrix K; The Gaussian process at time k is regressed to predict the mean μ k is the temperature drift of the laser gyro at time k; S4 SOC corrects the angular velocity output of the laser gyro at time k Get the corrected angular velocity output at time k After S4 is executed, k=k+1, and S3 to S4 are executed in a loop, so that the angular velocity output of the laser gyro after temperature compensation at the latest moment can be obtained in real time.

2. The laser gyro temperature compensation method based on Gaussian process regression and distributed temperature measurement according to claim 1, characterized in that: In S1.2, the temperature measuring resistor is a PT1000 temperature measuring resistor.

3. The laser gyro temperature compensation method based on Gaussian process regression and distributed temperature measurement according to claim 1, characterized in that: In S2.4.2, the magnitude hyperparameter σ of the kernel function 2 =0.0001, and the length scale hyperparameter of the kernel function is l=1.

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