Micro inertial navigation error parameter identification and prediction method of carrier motion excitation

Abstract

According to the carrier motion excitation micro inertial navigation error parameter identification and prediction method provided by the invention, micro inertial navigation use data and calibration data are effectively combined, device errors are preferentially compensated according to a use environment and a motion condition, historical data are fully utilized, the micro inertial navigation key error parameter compensation precision is improved, and the micro inertial navigation performance is further improved; meanwhile, the influence of motion excitation on error parameter compensation precision is also introduced, so that the calibration result is prevented from being polluted by data which does not excite errors. Finally, the learning and prediction method for the micro inertial navigation error parameter variation trend can reduce device errors caused by the increase of the service life, and prolongs the service life of micro inertial navigation.

Description

Technical Field

[0001] The invention belongs to the field of navigation, guidance and control technology, and in particular relates to a method for identifying and predicting error parameters of a micro-inertial navigation system with carrier motion excitation. Background Art

[0002] Micro-inertial navigation systems are gradually developing in the direction of miniaturization, low cost, and low power consumption, and are widely used in the navigation of moving objects such as pedestrians, unmanned vehicles, and drones. The autonomous navigation performance of micro-inertial navigation is greatly affected by the environment, the carrier, and its motion mode. The parameters drift greatly after long-term operation. Error calibration and compensation are the main means to ensure its performance. Traditional calibration and error compensation methods are limited by laboratory-specific equipment and cannot be calibrated in situ on the carrier. The on-site rapid calibration method frees the error compensation of micro-inertial navigation from site restrictions, but a single rapid calibration may have some problems such as insufficient error excitation, reduced calibration accuracy, and affected the performance of micro-inertial navigation. Summary of the invention

[0003] To solve the above problems, the present invention provides a method for identifying and predicting micro-inertial navigation error parameters under carrier motion excitation. By establishing a deep learning network model to predict the error, the historical measurement data is fully utilized to stimulate the error, thereby improving the micro-inertial navigation error compensation accuracy and enhancing the micro-inertial navigation performance.

[0004] A method for identifying and predicting error parameters of a micro-inertial navigation system under carrier motion excitation is disclosed. The error parameters used to compensate the micro-inertial navigation system are determined according to the progress of a turntable calibration experiment and a carrier motion experiment. If only the turntable calibration experiment is carried out or the turntable calibration experiment and the carrier motion experiment are carried out simultaneously, the error parameter calibration result of the turntable calibration experiment is directly taken as the error parameter for compensating the micro-inertial navigation system. If only the carrier motion experiment is carried out, it is determined whether an error parameter prediction model has been established. If so, the error parameter identification result of the carrier motion experiment and the error parameter prediction result output by the error parameter prediction model are selected as the error parameter for compensating the micro-inertial navigation system. If not, the error parameter identification result of the carrier motion experiment is taken as the error parameter for compensating the micro-inertial navigation system.

[0005] Furthermore, the micro-inertial navigation system includes an accelerometer and a gyroscope, and the error parameter calibration result of the turntable calibration experiment is obtained as follows:

[0006] The gyroscope error parameter model and accelerometer error parameter model are constructed as follows:

[0007]

[0008] in, are the three-axis components of the carrier angular velocity measured by the gyroscope in the carrier coordinate system b; They are the three-axis components of the expected value of the carrier angular velocity in the carrier coordinate system b; They are the three-axis components of the gyroscope zero bias in the carrier coordinate system b; are the three-axis components of the gyroscope random noise in the carrier coordinate system b; K ωx , K ωy , K ωz are the three-axis components of the gyroscope scale coefficient in the carrier coordinate system b; θ ωx ,θ ωy ,θ ωz They are the three-axis components of the gyroscope installation angle error in the carrier coordinate system b; are the three-axis components of the carrier acceleration measured by the accelerometer in the carrier coordinate system b; They are the three-axis components of the expected value of carrier acceleration in the carrier coordinate system b; They are the three-axis components of the accelerometer zero bias in the carrier coordinate system b; are the three-axis components of the accelerometer random noise in the carrier coordinate system b; K ax , K ay , K az are the three-axis components of the accelerometer scale coefficient in the carrier coordinate system b; θ az ,θ ay ,θ az They are the three-axis components of the accelerometer installation angular error in the carrier coordinate system b;

[0009] Taking the carrier angular velocity measured by the gyroscope, the expected value of the carrier angular velocity, the gyroscope random noise, the carrier acceleration measured by the accelerometer, the expected value of the carrier acceleration, and the accelerometer random noise as known quantities, the least squares method is used to solve the gyroscope error parameter model and the error parameter calibration results in the accelerometer error parameter model, wherein the error parameter calibration results in the gyroscope error parameter model are gyroscope zero bias, gyroscope scale coefficient, and gyroscope installation angle error; the error parameter calibration results in the accelerometer error parameter model are accelerometer zero bias, accelerometer scale coefficient, and accelerometer installation angle error.

[0010] Furthermore, the micro-inertial navigation system includes an accelerometer and a gyroscope, and the method for obtaining the error parameter identification result of the carrier motion experiment is as follows:

[0011] The gyroscope and accelerometer error parameter models are constructed as follows:

[0012]

[0013] Z=HX+V

[0014] Among them, the state parameter sequence δVn is the three-axis acceleration error of the carrier in the navigation coordinate system n measured by the accelerometer; φ n is the three-axis attitude error of the carrier in the navigation coordinate system n; μ b is the installation angle error of the gyroscope and accelerometer in the carrier coordinate system b; δK a is the three-axis scale coefficient error of the accelerometer; δK ω is the gyroscope three-axis scale coefficient error; is the three-axis zero bias of the accelerometer in the carrier coordinate system b; ε b is the three-axis zero bias of the gyroscope in the carrier coordinate system b; is the derivative of X, the observed value Z = [δV n φ n ] T , W and V are uncorrelated Gaussian white noises, F is the state matrix, and H is the observation matrix;

[0015] The Kalman filter micro-inertial navigation error parameter identification algorithm is used to solve the gyroscope and accelerometer error parameter model. The error parameter identification result is the three-axis zero bias of the accelerometer. Gyroscope three-axis bias ε b 、Accelerometer three-axis scale coefficient error δK a , Gyroscope three-axis scale coefficient error δK ω , the installation angle error μ of the gyroscope and accelerometer in the carrier coordinate system b b .

[0016] Furthermore, the method for obtaining the error parameter prediction result output by the error parameter prediction model is as follows:

[0017] The environmental parameter information and date information of the day of the carrier motion experiment are input into the error parameter prediction model based on Transformer, and the error parameter prediction model outputs the error parameter prediction results, wherein the error parameter prediction results include gyroscope zero bias, gyroscope scale coefficient, gyroscope installation angle error, accelerometer zero bias, accelerometer scale coefficient, and accelerometer installation angle error.

[0018] Furthermore, the method for obtaining the credibility of the error parameter identification result of the carrier motion experiment and the error parameter prediction result output by the error parameter prediction model is as follows:

[0019] The motion feature code ID1 of the carrier motion trajectory is obtained by using the data self-encoding method as follows:

[0020] ID1=AutoEncoder(Body,Mode)

[0021] Among them, Body is the carrier type label, Mode is the movement type label sequence formed by a series of movements performed by the carrier during the movement test, and AutoEncoder is the data self-encoding method;

[0022] The parameter feature extraction algorithm is used to obtain the environmental parameter feature code ID2 of the carrier motion trajectory as follows:

[0023] ID2=Onehot(E)=Onehot(T,RH)

[0024] Among them, E is the environmental parameter information of the carrier motion experiment, T is temperature, RH is humidity, and Onehot is the unique hot encoding method;

[0025] Based on the motion feature code ID1 and the environmental parameter feature code, the error parameter credibility acquisition model based on the Gaussian multivariate regression model is constructed as follows:

[0026] s=(Y,r,ID1,ID2)

[0027] m(s)=E[μ(s)]

[0028] k(s,s')=E[(f(s)-m(s))(f(s')-m(s'))]

[0029] μ(s)~GP(m(s),k(s,s'))

[0030] Wherein, s is the true value of the state quantity, s' is the estimated value of the state quantity obtained based on the IMU data Y of the micro-inertial navigation before compensation and the auxiliary navigation reference information r, Y is the IMU data, and Y includes the three-axis components Gyro_x, Gyro_y, Gyro_z of the carrier angular velocity measured by the gyroscope in the carrier coordinate system b and the three-axis components Acc_x, Acc_y, Acc_z of the carrier acceleration measured by the accelerometer in the carrier coordinate system b; r is the auxiliary navigation reference information, and r includes the position information, velocity information and attitude information of the GNSS; k(s, s') is the covariance function, μ(s) is the error parameter identification result of the three-dimensional motion experiment under the state quantity true value s or the credibility of the error parameter prediction result output by the error parameter prediction model, E[·] represents the expectation function, f(·) represents the regression model function, GP(·) represents the Gaussian process distribution function, and m(·) represents the mean function.

[0031] Beneficial effects:

[0032] The present invention provides a method for identifying and predicting error parameters of a micro-inertial navigation system under carrier motion excitation, which effectively combines micro-inertial navigation system usage data and calibration data, preferentially compensates for device errors according to usage environment and motion conditions, makes full use of historical data, improves the compensation accuracy of key error parameters of the micro-inertial navigation system, and further improves the performance of the micro-inertial navigation system. At the same time, the present invention also introduces the influence of motion excitation on the compensation accuracy of error parameters, and avoids the calibration result being polluted by data that fails to excite the error. Finally, the method for learning and predicting the variation trend of the error parameters of the micro-inertial navigation system proposed by the present invention can reduce the device error caused by the increase in service life and extend the service life of the micro-inertial navigation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The present invention provides a flow chart of a method for identifying and predicting micro-inertial navigation error parameters for carrier motion excitation. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0035] First, the present invention divides the historical micro-inertial navigation data into micro-inertial navigation carrier motion experiment data and micro-inertial navigation laboratory turntable calibration experiment data according to the experimental type of data collection. The historical micro-inertial navigation data is processed in units of division, that is, the micro-inertial navigation motion data is the data of a complete motion trajectory, and the micro-inertial navigation calibration data is the data of a complete calibration experiment. The storage structures of the two types of micro-inertial navigation experimental data are different, as follows:

[0036] (1) Micro-inertial navigation vehicle motion experiment data structure

[0037] The data is divided into units based on each complete motion trajectory data, and the data format is shown in Table 1.

[0038] Table 1 Data structure design of carrier motion experiment

[0039]

[0040] (2) Micro-inertial navigation turntable calibration experiment data structure

[0041] The complete calibration data of each time is used as the division unit, and the data format is shown in Table 2.

[0042] Table 2 Turntable calibration experiment data structure design

[0043]

[0044] Secondly, the present invention refers to the time synchronization information to align the collected IMU data with the reference data, that is, to align the IMU data, navigation reference auxiliary information data and environmental parameter information in the micro-inertial navigation motion data, and to align the IMU data, IMU reference data and environmental parameter information in the micro-inertial navigation calibration data. The aligned data is subjected to data preprocessing such as data outlier removal.

[0045] For the motion data of the micro-inertial navigation vehicle, the subsequent vehicle / motion type identification is performed as follows: First, the IMU data is divided according to the selected window length to obtain the sample size = acquisition time / window length. The sample data of 20 seconds during the vehicle startup phase is input into the vehicle type classifier to obtain the vehicle type label. The data samples are input into the motion type classifier in chronological order to obtain the motion type label time series of the trajectory.

[0046] Based on this, Figure 1 As shown, a method for identifying and predicting error parameters of a micro-inertial navigation system with carrier motion excitation is used to determine the error parameters for compensating the micro-inertial navigation system according to the progress of a turntable calibration experiment and a carrier motion experiment. If only a turntable calibration experiment is carried out or both the turntable calibration experiment and the carrier motion experiment are carried out simultaneously, the error parameter calibration result of the turntable calibration experiment is directly taken as the error parameter for compensating the micro-inertial navigation system. If only a carrier motion experiment is carried out, it is determined whether an error parameter prediction model has been established. If so, the error parameter identification result of the carrier motion experiment and the error parameter prediction result output by the error parameter prediction model with a greater credibility are selected as the error parameter for compensating the micro-inertial navigation system. If not, the error parameter identification result of the carrier motion experiment is taken as the error parameter for compensating the micro-inertial navigation system.

[0047] The process of establishing the micro inertial navigation error model and identifying parameters is introduced in detail below.

[0048] S2.1: First, establish a micro-inertial navigation error model according to actual application requirements and determine the error parameters to be identified. The micro-inertial navigation error model is constructed as follows:

[0049] Y=Model(k,X) (1)

[0050] Among them, Y, X∈Rn, Y=(y1,y2,…,y n ) T is the micro-inertial navigation output value, X=(x1,x2,…x n ) T is the micro-inertial navigation input value, which is an n-dimensional vector, k∈R l ,k=(k1,k2,…k l ) T is the micro-inertial navigation error model parameter vector, Model∈R n ×R l →Rn It is the mapping from micro-INS input to output, that is, the micro-INS error model architecture.

[0051] The micro-inertial navigation turntable calibration experimental data executes step S2.2, and the micro-inertial navigation motion experimental data executes step S2.3.

[0052] S2.2: Error parameter identification of turntable calibration experiment. For the micro-inertial navigation turntable calibration experiment data, in formula (1), Y = (y1, y2, ..., y n ) T is the IMU data Y in Table 2, X=(x1,x2,...x n ) T is the IMU reference data X in Table 2. According to the turntable calibration experiment principle, the error parameter calibration algorithm is designed to obtain the error parameters of each micro-inertial navigation calibration. The error parameter identification results of the turntable calibration experiment at different times constitute the calibration error parameter time series data.

[0053] S2.3: Identification of error parameters of carrier motion experiment. For micro-inertial navigation carrier motion experiment data, since the micro-inertial navigation input value cannot be directly obtained, formula (1) cannot be directly applied. Therefore, according to the types of navigation reference auxiliary information in Table 1, the micro-inertial navigation error parameter identification algorithm based on carrier motion experiment data is used to obtain the identification error parameters of each carrier motion experiment. The error parameter identification results of carrier motion experiments at different times constitute the identification error parameter time series data.

[0054] It should be noted that the accuracy of the error parameters obtained from the micro-INS carrier motion experimental data is limited by the accuracy of the navigation reference auxiliary information, the degree of excitation of the motion trajectory on the error parameters, etc. The credibility of the micro-INS identification error parameters is different from that of the micro-INS calibration error parameters. The credibility index is added on the basis of the micro-INS error parameter time series, and then the micro-INS identification and calibration error parameter sequences are fused according to the credibility.

[0055] S3.1: Calculation of the credibility of error parameters. Calculation of the credibility of error parameters in calibration experiments: For the time series of error parameters in micro-INS calibration, it is considered to be completely credible, that is, the credibility is 1. Calculation of the credibility of error parameters in carrier motion experiments: For the time series of error parameters in micro-INS identification, the design idea of ​​the credibility algorithm of error parameters in micro-INS identification is as follows:

[0056] (1) The motion feature code ID1 of the carrier motion trajectory is obtained by using the data self-encoding method as follows:

[0057] ID1=AutoEncoder(Body,Mode) (2)

[0058] Among them, Body is the carrier type label, Mode is the movement type label sequence formed by a series of movements performed by the carrier during the movement test, and AutoEncoder is the data self-encoding method;

[0059] (2) The parameter feature extraction algorithm is used to obtain the environmental parameter feature code ID2 of the carrier motion trajectory as follows:

[0060] ID2=Onehot(E)=Onehot(T,RH) (3)

[0061] Among them, E is the environmental parameter information of the carrier motion experiment in Table 1, T is the temperature in Table 1, RH is the humidity in Table 1, and Onehot is the one-hot encoding method;

[0062] (3) Design an algorithm for identifying the credibility of error parameters. The algorithm implements the function of inputting motion characteristics and environmental characteristics and outputting credibility. The mathematical form is as follows:

[0063] μ=f(Y,r,ID1,ID2) (4)

[0064] Where μ=(μ1,μ2...,μ l ) T is the credibility of the corresponding l error parameters (in the range of [0, 1]); in formula (2), Y = (y1, y2, ..., y n ) T is the IMU data Y in Table 1, and r is the auxiliary navigation reference information in Table 1.

[0065] S3.2: Credible fusion of error parameter sequences. There are three cases for error parameter fusion in turntable calibration experiments and carrier motion experiments:

[0066] (1) There are turntable calibration experiments and carrier motion experiments on the same day. The error parameter calibration result of the turntable calibration experiment is taken as the final error parameter of the day, and the credibility is recorded as 1.

[0067] (2) If there is only a turntable calibration experiment on the same day, the error parameter calibration result of the turntable calibration experiment is taken as the final error parameter of the day, and the credibility is recorded as 1.

[0068] (3) There is only a carrier motion experiment on the same date. When the error parameter prediction model has been established, the error parameter calibration result of the carrier motion experiment and the error parameter prediction result are credibly fused as the final error parameter of the day, and a credible fusion algorithm is designed to provide credibility; when the error parameter prediction model has not been established, the error parameter calibration result of the carrier motion experiment is taken as the final error parameter of the day, and the credibility is provided by the error parameter credibility algorithm identified in S3.1. The threshold-based credible fusion algorithm is designed as follows:

[0069]

[0070] Among them, k i ,i=1,2,...,l represents the i-th error parameter, They represent the i-th error parameter obtained by prediction using the established error parameter prediction model and least squares identification, respectively. Indicates the credibility of the i-th error parameter predicted and identified.

[0071] Then we obtain a credible sequence of error parameters in the following form.

[0072] Table 3

[0073]

[0074]

[0075] Based on building a micro-inertial navigation error parameter prediction model, the model realizes the function of inputting a credible error parameter sequence, environmental parameter feature coding and time information, and outputting the error parameter prediction value.

[0076] According to the actual application scenario, the environmental parameters E=(T, RH...) that have a significant impact on the identification accuracy and variation of the micro-inertial navigation error parameters are selected, and the model is established as follows

[0077] (k,μ)=G(E,Day) (5)

[0078] Where E is the environmental parameter information in Tables 1 and 2, T is the temperature in Table 1, RH is the humidity in Table 1, k∈R l ,k=(k1,k2,...k l ) T is the parameter vector of the micro-inertial navigation error model, Day is the date information of the day when the data was collected, and μ is the credibility obtained by equation (4) in S3.2 and S3.1.

[0079] When the micro-INS is used for the carrier motion experiment, the predicted value of the micro-INS error parameter is used to compensate the micro-INS output. When processing the data, S3.1 is executed to obtain the identification error parameter and its credibility of the carrier motion experiment. Then, the error parameter credibility fusion algorithm in S3.2 (3) is used to extract the error parameter identification value and prediction value with high credibility, so as to improve the error parameter accuracy.

[0080] Furthermore, based on the motion feature code ID1 and the environmental parameter feature code, an error parameter credibility acquisition model based on the Gaussian multivariate regression model is constructed as follows:

[0081] s=(Y,r,ID1,ID2)

[0082] m(s)=E[μ(s)]

[0083] k(s,s')=E[(f(s)-m(s))(f(s')-m(s'))]

[0084] μ(s)~GP(m(s),k(s,s'))

[0085] Wherein, s is the true value of the state quantity, s' is the estimated value of the state quantity obtained based on the IMU data Y of the micro-inertial navigation before compensation and the auxiliary navigation reference information r, Y is the IMU data, and Y includes the three-axis components Gyro_x, Gyro_y, Gyro_z of the carrier angular velocity measured by the gyroscope in the carrier coordinate system b and the three-axis components Acc_x, Acc_y, Acc_z of the carrier acceleration measured by the accelerometer in the carrier coordinate system b; r is the auxiliary navigation reference information, and r includes the position information, velocity information and attitude information of the GNSS; k(s, s') is the covariance function, μ(s) is the error parameter identification result of the three-dimensional motion experiment under the state quantity true value s or the credibility of the error parameter prediction result output by the error parameter prediction model, E[·] represents the expectation function, f(·) represents the regression model function, GP(·) represents the Gaussian process distribution function, and m(·) represents the mean function.

[0086] Furthermore, based on the micro-inertial navigation carrier motion experiment data set structure provided in Tables 4 and 5, the error parameter calibration results of the turntable calibration experiment and the error parameter identification results of the carrier motion experiment are further introduced.

[0087] Table 4

[0088]

[0089]

[0090] Table 5

[0091]

[0092] The time series in the data set are aligned according to the timestamp and data preprocessing is performed. Then the IMU data is divided into a window length of 2s to obtain the carrier type / motion type identification samples. The sample data of the carrier startup phase is input into the carrier type classifier to obtain the carrier type label. The data samples are input into the motion type classifier in chronological order to obtain the motion type label time series of the trajectory.

[0093] Based on this, the method for obtaining the error parameter calibration results of the turntable calibration experiment is as follows:

[0094] The gyroscope error parameter model and accelerometer error parameter model are constructed as follows:

[0095]

[0096] in, are the three-axis components of the carrier angular velocity measured by the gyroscope in the carrier coordinate system b; They are the three-axis components of the expected value of the carrier angular velocity in the carrier coordinate system b; They are the three-axis components of the gyroscope zero bias in the carrier coordinate system b; are the three-axis components of the gyroscope random noise in the carrier coordinate system b; K ωx , K ωy , K ωz are the three-axis components of the gyroscope scale coefficient in the carrier coordinate system b; θ ωx ,θ ωy ,θ ωz They are the three-axis components of the gyroscope installation angle error in the carrier coordinate system b; are the three-axis components of the carrier acceleration measured by the accelerometer in the carrier coordinate system b; They are the three-axis components of the expected value of carrier acceleration in the carrier coordinate system b; They are the three-axis components of the accelerometer zero bias in the carrier coordinate system b; are the three-axis components of the accelerometer random noise in the carrier coordinate system b; K ax , K ay , K az are the three-axis components of the accelerometer scale coefficient in the carrier coordinate system b; θ az ,θ ay ,θ az They are the three-axis components of the accelerometer installation angular error in the carrier coordinate system b;

[0097] Taking the carrier angular velocity measured by the gyroscope, the expected value of the carrier angular velocity, the gyroscope random noise, the carrier acceleration measured by the accelerometer, the expected value of the carrier acceleration, and the accelerometer random noise as known quantities, the least squares method is used to solve the gyroscope error parameter model and the error parameter calibration results in the accelerometer error parameter model, wherein the error parameter calibration results in the gyroscope error parameter model are gyroscope zero bias, gyroscope scale coefficient, and gyroscope installation angle error; the error parameter calibration results in the accelerometer error parameter model are accelerometer zero bias, accelerometer scale coefficient, and accelerometer installation angle error.

[0098] Furthermore, the method for obtaining the error parameter identification result of the carrier motion experiment is as follows:

[0099] The gyroscope and accelerometer error parameter models are constructed as follows:

[0100]

[0101] Z=HX+V

[0102] Among them, the state parameter sequence δV n is the three-axis acceleration error of the carrier in the navigation coordinate system n measured by the accelerometer; φ n is the three-axis attitude error of the carrier in the navigation coordinate system n; μ b is the installation angle error of the gyroscope and accelerometer in the carrier coordinate system b; δK a is the three-axis scale coefficient error of the accelerometer; δK ω is the gyroscope three-axis scale coefficient error; is the three-axis zero bias of the accelerometer in the carrier coordinate system b; ε b is the three-axis zero bias of the gyroscope in the carrier coordinate system b; is the derivative of X, the observed value Z = [δV n φ n ] T , W and V are uncorrelated Gaussian white noises, F is the state matrix, and H is the observation matrix;

[0103] The Kalman filter micro-inertial navigation error parameter identification algorithm is used to solve the gyroscope and accelerometer error parameter model. The error parameter identification result is the three-axis zero bias of the accelerometer. Gyroscope three-axis bias ε b 、Accelerometer three-axis scale coefficient error δK a , Gyroscope three-axis scale coefficient error δK ω , the installation angle error μ of the gyroscope and accelerometer in the carrier coordinate system b b .

[0104] Furthermore, the method for obtaining the error parameter prediction result output by the error parameter prediction model is as follows:

[0105] The environmental parameter information and date information of the day of the carrier motion experiment are input into the error parameter prediction model based on Transformer, and the error parameter prediction model outputs the error parameter prediction results, wherein the error parameter prediction results include gyroscope zero bias, gyroscope scale coefficient, gyroscope installation angle error, accelerometer zero bias, accelerometer scale coefficient, and accelerometer installation angle error.

[0106] It should be noted that the parameter settings of the Transformer-based error parameter prediction model are as follows:

[0107] Table 5

[0108]

[0109]

[0110] In summary, the present invention effectively combines the micro-INS usage data and calibration data, preferentially compensates for device errors according to the usage environment and motion conditions, makes full use of historical data, improves the compensation accuracy of key error parameters of the micro-INS, and thus improves the performance of the micro-INS. At the same time, the present invention also introduces the influence of motion excitation on the compensation accuracy of error parameters to avoid the calibration results being polluted by data that fails to excite the error. Finally, the learning and prediction method for the change trend of the error parameters of the micro-INS proposed in the present invention can reduce the device error caused by the increase in service life and extend the service life of the micro-INS.

[0111] Of course, the present invention may have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may certainly make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for identifying and predicting error parameters of micro-inertial navigation system with carrier motion excitation, characterized in that: The error parameters used to compensate the micro-inertial navigation are determined according to the progress of the turntable calibration experiment and the carrier motion experiment. If only the turntable calibration experiment is carried out or the turntable calibration experiment and the carrier motion experiment are carried out at the same time, the error parameter calibration result of the turntable calibration experiment is directly taken as the error parameter for compensating the micro-inertial navigation. If only the carrier motion experiment is carried out, it is determined whether an error parameter prediction model has been established. If so, the error parameter identification result of the carrier motion experiment and the error parameter prediction result output by the error parameter prediction model with greater credibility are selected as the error parameters for compensating the micro-inertial navigation. If not, the error parameter identification result of the carrier motion experiment is taken as the error parameter for compensating the micro-inertial navigation.

2. The method for identifying and predicting error parameters of micro-inertial navigation system with carrier motion excitation according to claim 1, characterized in that: The micro-inertial navigation system includes an accelerometer and a gyroscope, and the error parameter calibration result of the turntable calibration experiment is obtained as follows: The gyroscope error parameter model and accelerometer error parameter model are constructed as follows: in, are the three-axis components of the carrier angular velocity measured by the gyroscope in the carrier coordinate system b; They are the three-axis components of the expected value of the carrier angular velocity in the carrier coordinate system b; They are the three-axis components of the gyroscope zero bias in the carrier coordinate system b; are the three-axis components of the gyroscope random noise in the carrier coordinate system b; K ωx , K ωy , K ωz are the three-axis components of the gyroscope scale coefficient in the carrier coordinate system b; θ ωx ,θ ωy ,θ ωz They are the three-axis components of the gyroscope installation angle error in the carrier coordinate system b; are the three-axis components of the carrier acceleration measured by the accelerometer in the carrier coordinate system b; They are the three-axis components of the expected value of carrier acceleration in the carrier coordinate system b; They are the three-axis components of the accelerometer zero bias in the carrier coordinate system b; are the three-axis components of the accelerometer random noise in the carrier coordinate system b; K ax , K ay , K az are the three-axis components of the accelerometer scale coefficient in the carrier coordinate system b; θ az ,θ ay ,θ az They are the three-axis components of the accelerometer installation angular error in the carrier coordinate system b; Taking the carrier angular velocity measured by the gyroscope, the expected value of the carrier angular velocity, the gyroscope random noise, the carrier acceleration measured by the accelerometer, the expected value of the carrier acceleration, and the accelerometer random noise as known quantities, the least squares method is used to solve the gyroscope error parameter model and the error parameter calibration results in the accelerometer error parameter model, wherein the error parameter calibration results in the gyroscope error parameter model are gyroscope zero bias, gyroscope scale coefficient, and gyroscope installation angle error; the error parameter calibration results in the accelerometer error parameter model are accelerometer zero bias, accelerometer scale coefficient, and accelerometer installation angle error.

3. The method for identifying and predicting error parameters of micro-inertial navigation system with carrier motion excitation according to claim 1, characterized in that: The micro-inertial navigation system includes an accelerometer and a gyroscope, and the method for obtaining the error parameter identification results of the carrier motion experiment is as follows: The gyroscope and accelerometer error parameter models are constructed as follows: Z=HX+V Among them, the state parameter sequence δV n is the three-axis acceleration error of the carrier in the navigation coordinate system n measured by the accelerometer; φ n is the three-axis attitude error of the carrier in the navigation coordinate system n; μ b is the installation angle error of the gyroscope and accelerometer in the carrier coordinate system b; δK a is the three-axis scale coefficient error of the accelerometer; δK ω is the gyroscope three-axis scale coefficient error; is the three-axis zero bias of the accelerometer in the carrier coordinate system b; ε b is the three-axis zero bias of the gyroscope in the carrier coordinate system b; is the derivative of X, the observed value Z = [δV n φ n ] T , W and V are uncorrelated Gaussian white noises, F is the state matrix, and H is the observation matrix; The Kalman filter micro-inertial navigation error parameter identification algorithm is used to solve the gyroscope and accelerometer error parameter model. The error parameter identification result is the three-axis zero bias of the accelerometer. Gyroscope three-axis bias ε b 、Accelerometer three-axis scale coefficient error δK a , Gyroscope three-axis scale coefficient error δK ω , the installation angle error μ of the gyroscope and accelerometer in the carrier coordinate system b b .

4. The method for identifying and predicting error parameters of micro-inertial navigation system with carrier motion excitation according to claim 1, characterized in that: The method for obtaining the error parameter prediction results output by the error parameter prediction model is as follows: The environmental parameter information and date information of the day of the carrier motion experiment are input into the error parameter prediction model based on Transformer, and the error parameter prediction model outputs the error parameter prediction results, wherein the error parameter prediction results include gyroscope zero bias, gyroscope scale coefficient, gyroscope installation angle error, accelerometer zero bias, accelerometer scale coefficient, and accelerometer installation angle error.

5. The method for identifying and predicting error parameters of micro-inertial navigation system with carrier motion excitation according to claim 1, characterized in that: The method for obtaining the credibility of the error parameter identification results of the carrier motion experiment and the error parameter prediction results output by the error parameter prediction model is as follows: The motion feature code ID1 of the carrier motion trajectory is obtained by using the data self-encoding method as follows: ID1=AutoEncoder(Body,Mode) Among them, Body is the carrier type label, Mode is the movement type label sequence formed by a series of movements performed by the carrier during the movement test, and AutoEncoder is the data self-encoding method; The parameter feature extraction algorithm is used to obtain the environmental parameter feature code ID2 of the carrier motion trajectory as follows: ID2=Onehot(E)=Onehot(T,RH) Among them, E is the environmental parameter information of the carrier motion experiment, T is temperature, RH is humidity, and Onehot is the unique hot encoding method; Based on the motion feature code ID1 and the environmental parameter feature code, the error parameter credibility acquisition model based on the Gaussian multivariate regression model is constructed as follows: s=(Y,r,ID1,ID2) m(s)=E[μ(s)] k(s,s')=E[(f(s)-m(s))(f(s')-m(s'))] μ(s)~GP(m(s),k(s,s')) Wherein, s is the true value of the state quantity, s' is the estimated value of the state quantity obtained based on the IMU data Y of the micro-inertial navigation before compensation and the auxiliary navigation reference information r, Y is the IMU data, and Y includes the three-axis components Gyro_x, Gyro_y, Gyro_z of the carrier angular velocity measured by the gyroscope in the carrier coordinate system b and the three-axis components Acc_x, Acc_y, Acc_z of the carrier acceleration measured by the accelerometer in the carrier coordinate system b; r is the auxiliary navigation reference information, and r includes the position information, velocity information and attitude information of the GNSS; k(s, s') is the covariance function, μ(s) is the error parameter identification result of the three-dimensional motion experiment under the state quantity true value s or the credibility of the error parameter prediction result output by the error parameter prediction model, E[·] represents the expectation function, f(·) represents the regression model function, GP(·) represents the Gaussian process distribution function, and m(·) represents the mean function.

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