CN117990096A

CN117990096A - Vehicle navigation data analysis method and device, electronic equipment and storage medium

Info

Publication number: CN117990096A
Application number: CN202410105807.5A
Authority: CN
Inventors: 马万力; 罗刚; 余平; 王厚毅; 廖淑华
Original assignee: Chongqing Starnav Systems Co ltd
Current assignee: Chongqing Starnav Systems Co ltd
Priority date: 2024-01-25
Filing date: 2024-01-25
Publication date: 2024-05-07

Abstract

The application provides a vehicle navigation data analysis method, a device, electronic equipment and a storage medium, which are applied to the technical field of vehicle navigation, wherein error data is detected and removed from first original data of GNSS subsystems, then an INS (inertial navigation system) calculation parameter is obtained by calculating second original data of an INS subsystem, and then correlation and consistency analysis are carried out on GNSS input parameters, the INS calculation parameter and a combined navigation result to obtain GNSS parameters, INS parameters and corresponding combined weights for combined fusion, so that the problem that the idea carries out weight adjustment on parameters output by all subsystems is avoided, and more time and labor cost are wasted in an algorithm development process.

Description

Vehicle navigation data analysis method and device, electronic equipment and storage medium

Technical Field

The present application relates to the field of vehicle navigation technologies, and in particular, to a vehicle navigation data analysis method, device, electronic apparatus, and storage medium.

Background

The GNSS/INS vehicle-mounted integrated navigation system is a navigation system which utilizes a plurality of different single navigation systems to position the same target, utilizes complementary characteristics of a GNSS subsystem and an INS subsystem on the performance of sensor equipment to obtain optimal positioning through an information fusion technology, and is a safer, more reliable and more stable navigation mode in the vehicle-mounted field.

For the vehicle-mounted platform, the information obtained by the two independent systems is fused with each other most difficultly by the complete combined system, the degree of difficulty of data fusion processing also reflects the degree of fit of the two parties of the combined system, and the effect and positioning precision of the whole filtering system are reflected from the results. In the data fusion process, the selection of different subsystem thresholds in the combined filtering can influence the state quantity of the system, so that the result of the GNSS/INS vehicle-mounted combined system is the fitting effect of the observed data and the state vector.

In the prior art, the most common filter of the GNSS/INS vehicle-mounted integrated navigation system is a Kalman filter and a variant filter thereof, at the beginning of the filter, data enter the integrated navigation system for classification and processing, excessive error data and artificial experience setting of a threshold value can reduce the stability and filtering precision of the system, the traditional integrated navigation filter is biased to an empirical value in parameter selection of a subsystem, each parameter in the subsystem is analyzed one by one when the parameters specifically entering an integrated navigation algorithm are selected, so as to optimize the whole filtering strategy, whether the selected parameters effectively need to be tried one by one or not, in the fusion stage of the integrated navigation data, if different data sources are replaced, each output data has a difference, the weight selection problem can occur to the integrated navigation filter, the weights of all parameters in the assembly can be adjusted one by one according to simulation or actual measurement conditions in the development process of navigation equipment, and thus the time and cost in the algorithm development process can be greatly increased. According to the analysis, in the prior art, all subsystems in the integrated navigation system are easily influenced by own hardware and environment in the data fusion stage, so that the subsystems have a certain influence on the output of the same type of data, the weights of the output parameters of all subsystems in the subsequent integrated data fusion stage are required to be adjusted one by one, and more time and labor cost are wasted in the algorithm development process.

Thus, improvements are needed in the art.

Disclosure of Invention

In view of the shortcomings of the prior art, the application provides a vehicle navigation data analysis method, a device, an electronic device and a storage medium, which are applied to the technical field of vehicle navigation, wherein error data is detected and removed by first original data of GNSS subsystems, then INS calculation parameters are obtained by calculating second original data of INS subsystems, then correlation and consistency analysis are carried out on GNSS input parameters and INS calculation parameters and a combined navigation result, and GNSS parameters, INS parameters and corresponding combined weights for combined fusion are obtained, so that the problem that the weight adjustment of parameters output by all subsystems by a main idea is avoided, and more time and labor cost are wasted in an algorithm development process.

In a first aspect, the present application provides a vehicle navigation data analysis method, including the steps of:

S1: acquiring first original data of a GNSS subsystem, and eliminating error data in the first original data to obtain GNSS input parameters;

S2: acquiring second original data of the INS subsystem, and resolving the second original data to obtain INS resolving parameters;

s3: performing correlation calculation on the GNSS input parameters and the INS calculation parameters and the combined output results of the GNSS/INS combined navigation system respectively, and discarding the GNSS input parameters and the INS calculation parameters corresponding to the median of the correlation calculation results approaching 0 to obtain GNSS parameters and INS parameters which can participate in a combined strategy;

S4: carrying out consistency calculation on the GNSS parameters and the INS parameters and the combined output results respectively, and selecting the GNSS parameters and the INS parameters with the same variation trend as the combined output results in the consistency calculation results to carry out weight adjustment of a combination strategy so as to obtain combination weights;

s5: and carrying out combination data fusion of the GNSS parameters and the INS parameters according to the combination weights.

According to the vehicle navigation data analysis method provided by the application, the first original data of the GNSS subsystem is obtained, and the first original data is detected to delete the error data in the first original data, so that the optimized GNSS input parameters which can be used for the combination strategy are obtained; obtaining second original data of the INS subsystem and resolving the second original data to obtain INS resolving parameters which can be used for a combination strategy, further discarding GNSS input parameters and INS resolving parameters with low correlation through correlation calculation to obtain GNSS parameters and INS parameters, ensuring the effectiveness of data in a subsequent combination fusion process, then adjusting the combination weights of the GNSS parameters and the INS parameters which participate in the combination strategy through consistency calculation, carrying out combination fusion according to the combination weights, saving the weight debugging time of the parameters which participate in the combination fusion one by one, improving the efficiency of an algorithm development process, and reducing the waste of manpower and time.

Further, the GNSS input parameters at least include positioning data, and step S1 includes:

s11: calculating a plurality of positioning data according to the first original data;

s12: obtaining error positioning data which are different from other positioning data in the positioning data, and eliminating error data corresponding to the error positioning data in the first original data;

the step S11 includes: the formula for calculating the positioning data according to the first original data is as follows:

Where x _i、y_i、z_i denotes the location information of four satellites, where i=1, 2, 3, 4; v _ti denotes the clock difference between the GNSS receiver and the GNSS subsystem, which is four satellites; p _i denotes the pseudoranges of the GNSS receiver to four satellites; x, y, z represent receiver position; c denotes the propagation speed of light in vacuum c=3x108 m/s.

According to the vehicle navigation data analysis method provided by the application, through a technical positioning data formula, error data, which is inconsistent with positioning data output by other parameters, of the positioning data output by the first original data can be eliminated, and the accuracy of GNSS input parameters is improved.

Further, the GNSS input parameters further include board card data, and step S1 further includes:

s13: extracting board card data corresponding to 15 epoch data in the GNSS subsystem, wherein the board card data at least comprises: invalid solution, single-point solution, pseudo-range solution, floating solution, fixed solution, satellite number and satellite signal number, longitude standard deviation, latitude standard deviation, elevation standard deviation, east direction speed standard deviation, north direction speed standard deviation, and sky direction speed standard deviation;

s14: dividing the board card data corresponding to the 15 calendar metadata into 3 groups, calculating the average value of the board card data, and eliminating error data corresponding to error average values higher than a first preset threshold value in the average value;

step S14 includes: the formula for calculating the average value is: wherein xj represents the board data (j=1, 2, 3..15) corresponding to the jth epoch data,/> Represents the average of the first set of data,Represents the average of the second set of data,Mean values of the third set of data are shown.

According to the vehicle navigation data analysis method provided by the application, the accuracy of GNSS input parameters is improved by extracting the average value of the board card data under different calendar data and deleting the error data corresponding to the average value of the board card data in the first original data exceeding the first preset value.

Further, the GNSS input parameters further include a clock of each satellite in the GNSS subsystem, and step S1 further includes:

S15: calculating a difference value of a clock of each satellite in the GNSS subsystem relative to the GNSS subsystem time, wherein a formula for calculating the difference value is as follows: Δt=a _f0+a_f1(t-t_oc)+a_f2(t-t_oc)², wherein a _f0、a_f1、a_f2 is a navigation message error parameter, t _oc is a navigation message time parameter, and t is an observation time of the GNSS subsystem;

S16: and when the difference value is larger than a second preset threshold value, eliminating error data corresponding to the error difference value.

According to the vehicle-mounted navigation data analysis method provided by the application, through a formula for calculating the difference value of the clock of each satellite in the GNSS subsystem relative to the GNSS subsystem time, error data corresponding to the GNSS subsystem output time exceeding the second preset threshold value in the first original data are deleted, and the accuracy of GNSS input parameters is improved.

Further, the INS solution parameter at least includes INS pose information, and step S2 includes:

S21: obtaining the output angular velocity w of the gyroscope in the second original data;

S22: establishing a rotation matrix by using the quaternion, and carrying out attitude calculation based on the quaternion to obtain the INS attitude information;

step S22 includes:

s221: the rotation matrix established by using the quaternion is: Wherein w represents the angular velocity of the gyroscope, and x, y and z represent three output shafts of the gyroscope respectively; /(I) Representing the posture from a car body coordinate system to a navigation coordinate system of the car, n represents the car body coordinate system, and b represents the navigation coordinate system;

S222: the formula for carrying out gesture calculation based on the quaternion is as follows:

wherein/> The magnitude of the rotation angle is represented by q _nb, which is a quaternion representation of the angle, t _k-1 represents the time immediately before sampling, t _k represents the current sampling time,For the estimated value of the rotation angle, w _k-1 represents the angular velocity of the gyroscope at the time immediately before sampling, and w _k represents the angular velocity of the gyroscope at the current sampling time.

According to the vehicle navigation data analysis method provided by the application, the attitude information of the INS subsystem is calculated by the attitude calculation method based on the quaternion, so that the INS attitude information which can participate in combination fusion operation is obtained, and the accuracy of vehicle navigation is improved.

Further, the INS calculation parameter further includes INS speed information, and step S2 further includes:

s23: obtaining an output value of an INS accelerometer in the second original data;

s24: calculating the speed according to the output value of the accelerometer to obtain INS speed information;

step S24 includes:

s241: performing matrix conversion on the output value of the INS accelerometer to obtain the output value of the accelerometer under a navigation coordinate system:

Where C ₁₁、C₁₂、C₁₃、C₂₁、C₂₂、C₂₃、C₃₁、C₃₂、C₃₃ is the conversion matrix, f ₁、f₂、f₃ represents the accelerometer output value in the navigational coordinate system,/> The method comprises the steps that a conversion matrix from a vehicle body coordinate system to a navigation coordinate system is represented, upper and lower labels n and b are respectively represented as the vehicle body coordinate system and the navigation coordinate system, f ⁿ represents accelerometer output under the vehicle body coordinate system, f _e、f_n、f_u represents output of accelerometers under the northeast, north and heaven directions under the geographic coordinate system of northeast, f ⁿ represents accelerometer output under the vehicle body coordinate system, and f ^b represents accelerometer output under the vehicle body coordinate system;

S242: according to the output value resolving speed of the accelerometer under the navigation coordinate system, the formula for obtaining INS speed information is as follows: Where v _k-1 denotes the speed at the previous time instant; a _k-1 represents the acceleration at the previous time (i.e., the output value of the accelerometer in the navigation coordinate system at the previous time), and a _k represents the acceleration at the current time (i.e., the output value of the accelerometer in the navigation coordinate system at the current time); /(I) A rotation matrix representing the last time,A rotation matrix representing the current time; g represents a gravitational constant, t _k-1 represents a previous time, and t _k represents a current time, according to a local gravitational constant setting.

Further, the INS calculation parameters further include INS position information, and step S24 includes:

S25: according to the INS speed information, calculating the position of the INS subsystem to obtain INS position information;

the step S25 includes: according to the INS speed information, the position of the INS subsystem is calculated, and a calculation formula is as follows:

Where p _k-1 denotes INS position information at the previous time, and p _k denotes INS position information at the current time.

In a second aspect, the present application provides a vehicle-mounted navigation data analysis device, the device comprising:

A first acquisition module: the method comprises the steps of acquiring first original data of a GNSS subsystem, and eliminating error data in the first original data to obtain GNSS input parameters;

and a second acquisition module: the method comprises the steps of obtaining second original data of an INS subsystem, and resolving the second original data to obtain INS resolving parameters;

And a correlation calculation module: the method comprises the steps of carrying out correlation calculation on the GNSS input parameters and the INS calculation parameters and a combination output result of a GNSS/INS combination navigation system respectively, discarding the GNSS input parameters and the INS calculation parameters corresponding to a median of the correlation calculation results approaching 0, and obtaining GNSS parameters and INS parameters which can participate in a combination strategy;

And a consistency calculation module: the method comprises the steps of carrying out consistency calculation on the GNSS parameters and the INS parameters and the combination output results respectively, and selecting the GNSS parameters and the INS parameters with the same variation trend as the combination output results in the consistency calculation results to carry out weight adjustment of a combination strategy so as to obtain combination weights;

and a fusion module: and the combination data fusion of the GNSS parameters and the INS parameters is carried out according to the combination weights.

In a third aspect, the application provides an electronic device comprising a processor and a memory storing computer readable instructions which, when executed by the processor, perform the steps of the method as provided in the first aspect above.

In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method as provided in the first aspect above.

The beneficial effects are that: according to the vehicle navigation data analysis method, the device, the electronic equipment and the storage medium, the first original data of the GNSS subsystem are obtained, and the first original data are detected to delete error data in the first original data, so that optimized GNSS input parameters which can be used for a combination strategy are obtained; obtaining second original data of the INS subsystem and resolving the second original data to obtain INS resolving parameters which can be used for a combination strategy, further discarding GNSS input parameters and INS resolving parameters with low correlation through correlation calculation to obtain GNSS parameters and INS parameters, ensuring the effectiveness of data in a subsequent combination fusion process, then adjusting the combination weights of the GNSS parameters and the INS parameters which participate in the combination strategy through consistency calculation, carrying out combination fusion according to the combination weights, saving the weight debugging time of the parameters which participate in the combination fusion one by one, improving the efficiency of an algorithm development process, and reducing the waste of manpower and time.

Drawings

Fig. 1 is a flow chart of a vehicle navigation data analysis method according to the present application.

Fig. 2 is a schematic structural diagram of a vehicle navigation data analysis device according to the present application.

Fig. 3 is a schematic structural diagram of an electronic device provided by the present application.

Description of the reference numerals: 201. a first acquisition module; 202. a second acquisition module; 203. a correlation calculation module; 204. a consistency calculation module; 205. a fusion module; 3. an electronic device; 301. a processor; 302. a memory; 303. a communication bus.

Detailed Description

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.

It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first, second", etc. are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.

The following disclosure provides many different embodiments or examples for accomplishing the objectives of the present application and solving the problems of the prior art. In the prior art, all subsystems in the integrated navigation system are easily influenced by own hardware and environment in a data fusion stage, so that the subsystems have a certain influence on the output of the same type of data, the weights of the output parameters of all subsystems are required to be adjusted one by one in the subsequent combined data fusion stage, and more time and labor cost are wasted in the algorithm development process. In order to solve the problem, the application provides a vehicle navigation data analysis method, a device, an electronic device and a storage medium, which specifically comprises the following steps:

referring to fig. 1, an embodiment of the present application provides a vehicle navigation data analysis method, which includes the following steps:

s1: acquiring first original data of a GNSS subsystem, and removing error data in the first original data to obtain GNSS input parameters;

S3: performing correlation calculation on GNSS input parameters and INS calculation parameters and a combined output result of the GNSS/INS combined navigation system respectively, and discarding the GNSS input parameters and the INS calculation parameters corresponding to a median of the correlation calculation results approaching 0 to obtain GNSS parameters and INS parameters which can participate in a combined strategy;

S4: carrying out consistency calculation on the GNSS parameters and the INS parameters and the combined output results respectively, and selecting the GNSS parameters and the INS parameters which have the same variation trend as the combined output results in the consistency calculation results to carry out weight adjustment of a combination strategy so as to obtain combination weights;

In practical application, in step S1, the GNSS subsystem is a satellite navigation system in a GNSS/INS vehicle-mounted integrated navigation system, the first raw data of the GNSS subsystem may be obtained through a GNSS receiver and software on a computer, where the GNSS receiver may be Trimble, leica, topcon, etc., and the computer software may be program software connected to the GNSS receiver for analyzing a data packet received by the GNSS receiver, such as matlab, and decoding and analyzing the data packet by the program software to obtain the first raw data. The first raw data at least comprises time service data, speed, heading, satellite information and a communication protocol of a serial port of the GNSS receiver. Because the first original data received by the GNSS is not completely accurate, if the first original data is directly used for carrying out data combination fusion to position the vehicle, a larger error exists between the obtained vehicle positioning result and the true value, so that the data with larger error or error in the first original data needs to be removed to ensure the accuracy of GNSS input parameters participating in the combination fusion strategy.

In practical applications, there are various ways to exclude the error data in the first raw data, and accurate GNSS input parameters may be obtained by detecting positioning data, detecting board data, and detecting output time of the GNSS subsystem to exclude the error data related to the above data.

Further, in some embodiments, the GNSS input parameters include at least positioning data, and step S1 includes:

s12: obtaining error positioning data which are different from other positioning data in the plurality of positioning data, and removing error data corresponding to the error positioning data in the first original data;

Where x _i、y_i、z_i denotes the location information of four satellites, where i=1, 2, 3, 4; v _ti denotes the clock difference between the GNSS receiver and the GNSS subsystem, which is four satellites; p _i denotes the pseudoranges of the GNSS receiver to four satellites; x, y, z denote GNSS receiver position; c represents the propagation speed of light in vacuum c=3×108m/s.

In practical application, positioning data is calculated according to the position information of satellites in the first original data output by the GNSS subsystem, the clock difference between the GNSS receiver and the GNSS subsystem, the pseudo range and the light speed, the GNSS subsystem comprises 55 satellites, parameters output by 4 satellites are arbitrarily selected to establish an equation, so that the positioning data can be calculated, in order to ensure the accuracy of the positioning data, the positioning data needs to be checked, the checking step is to arbitrarily select a plurality of groups (4 satellites are a group) of other satellites to obtain corresponding parameter establishment equations, the positioning data of the same point to be checked is calculated, if the positioning data is consistent with the positioning data calculated by the parameters output by other satellites in the checking process, the data can be used, and if the positioning data is inconsistent with the positioning data calculated by the parameters output by other satellites in the checking process, the data is erroneous data and needs to be excluded. By the method, the error data corresponding to the error positioning data in the first original data can be completely removed, and the accuracy of GNSS input parameters is improved.

Further, in some specific embodiments, the GNSS input parameters further include board data, and step S1 further includes:

s13: and extracting board card data corresponding to 15 epoch data in the GNSS subsystem, wherein the board card data at least comprises: invalid solution, single-point solution, pseudo-range solution, floating solution, fixed solution, satellite number and satellite signal number, longitude standard deviation, latitude standard deviation, elevation standard deviation, east direction speed standard deviation, north direction speed standard deviation, and sky direction speed standard deviation;

S14: dividing the board card data corresponding to the 15 calendar metadata into 3 groups, calculating the average value of the board card data, and removing error data corresponding to the error average value higher than a first preset threshold value in the average value;

step S14 includes: the formula for calculating the average value is: wherein xj represents board data (j=1, 2, 3..15) corresponding to the jth epoch data,/> Represents the average of the first set of data,Represents the average of the second set of data,Mean values of the third set of data are shown.

In practical application, error data elimination is also required for board card data of the GNSS subsystem. The GNSS board card is a basic integrated circuit board with an input/output interface and is made of a baseband chip, a radio frequency chip, a peripheral circuit and corresponding embedded control software, and is a core component of a GNSS receiver and used for supporting the positioning of the GNSS subsystem. Wherein, the board card data includes at least: the method comprises the steps of invalid solution, single-point solution, pseudo-range solution, floating solution, fixed solution, satellite number and satellite signal number, longitude standard deviation, latitude standard deviation, elevation standard deviation, east direction speed standard deviation, north direction speed standard deviation and sky direction speed standard deviation, and calendar data are measurement time intervals of a GNSS receiver. And randomly selecting the board card data under 15 epoch data, dividing the board card data into 3 groups of building cube programs, calculating the average value of the board card data, obtaining the error average value which is larger than a first preset threshold value in the 3 groups of average values, and removing the error board card data corresponding to the average value, thereby improving the accuracy of GNSS input parameters. The first preset threshold is set by a technician according to the actual measurement condition, the first preset threshold is the maximum allowable error value of the average value, and the error value exceeding the average value of the first preset value is the maximum allowable error value of the average value, so that error data is necessarily present in corresponding board card data, and the error data needs to be eliminated, so that the vehicle positioning information with larger difference from the true value is avoided being output in the combined fusion stage.

Further, in a specific embodiment, the GNSS input parameters further include clocks of each satellite in the GNSS subsystem, and step S1 further includes:

S15: calculating the difference value of the clock of each satellite in the GNSS subsystem relative to the time of the GNSS subsystem, wherein the formula for calculating the difference value is as follows: Δt=a _f0+a_f1(t-t_oc)+a_f2(t-t_oc)², wherein a _f0、a_f1、a_f2 is a navigation message error parameter, t _oc is a navigation message time parameter, and t is an observation time of the GNSS subsystem;

In practical application, a state error exists in the output mode of the GNSS board card, the state error includes a time error caused by time and a frequency error caused by transmission frequency, and the reason why the error occurs is that a clock of a satellite in the GNSS subsystem has a deviation from the time of the GNSS subsystem when the clock is synchronous, so, in order to correct the state error, a difference value of the clock of each satellite in the GNSS subsystem relative to the time of the GNSS subsystem can be calculated and corrected, where a formula for calculating the difference value is:

Δt=a _f0+a_f1(t-t_oc)+a_f2(t-t_oc)², where a _f0、a_f1、a_f2 is a navigation message error parameter, t _oc is a navigation message time parameter, and t is an observation time of the GNSS subsystem. The correction of the difference value of the clock of each satellite in the GNSS subsystem to the GNSS subsystem time can be realized through the converted state error equation. . When the difference value is greater than a second preset threshold value, error data corresponding to the error difference value is eliminated, namely that when the clock of the satellite in the GNSS subsystem and the time of the GNSS subsystem are in synchronization and have overlarge deviation, and the allowable time deviation (the second preset threshold value) is exceeded, the error data corresponding to the error difference value is eliminated, and the accuracy of GNSS input parameters is improved.

In step S2, the INS subsystem is an inertial navigation system in the GNSS/INS integrated navigation system, and is a navigation system that measures information such as acceleration and angular velocity of the vehicle based on sensors such as gyroscopes and accelerometers, and further calculates information such as heading and position. The GNSS subsystem and the INS subsystem can be mutually complemented, the GNSS subsystem provides global information of position and speed, the INS subsystem provides high-frequency data in a short time, and the accuracy and the stability of vehicle navigation can be improved by combining and fusing the two data. Since the second raw data output by the INS subsystem only includes angular velocity and acceleration, and does not include information such as attitude, velocity, and position, the second raw data of the INS subsystem needs to be resolved in order to obtain the INS attitude information, the INS velocity information, and the INS position information of the INS subsystem.

Further, in some specific embodiments, the INS solution parameter includes at least INS pose information, and step S2 includes:

s22: establishing a rotation matrix by using the quaternion, and carrying out attitude calculation based on the quaternion to obtain INS attitude information;

step S22 includes:

s222: the formula for carrying out gesture calculation based on quaternion is as follows: Wherein, The magnitude of the rotation angle is represented by q _nb, which is a quaternion representation of the angle, t _k-1 represents the time immediately before sampling, t _k represents the current sampling time,For the estimated value of the rotation angle, w _k-1 represents the angular velocity of the gyroscope at the time immediately before sampling, and w _k represents the angular velocity of the gyroscope at the current sampling time.

In practical application, the angular velocity is output by using the gyroscope, so that INS attitude information can be calculated. The specific resolving process can utilize a quaternion to build a matrix for resolving, the quaternion uses four special numerical values to represent a rotation matrix, wherein each value of three imaginary numbers represents one coordinate axis, thereby building a three-dimensional coordinate, and the unique real number represents the rotation angle in the coordinate system. The established rotation matrix is as follows: w represents the angular velocity of the gyroscope, and x, y and z represent three output shafts of the gyroscope respectively; the subscripts n, b are denoted as the car body to navigation coordinate system. And then, the rotation matrix established by the quaternion can be used for carrying out attitude calculation on the angular velocity to obtain INS attitude information, wherein a specific calculation formula is as follows: /(I) Wherein,

The magnitude of the rotation angle is represented by q _nb, which is a quaternion representation of the angle, t _k-1 represents the time immediately before sampling, t _k represents the current sampling time,For the estimated value of the rotation angle, w _k-1 represents the angular velocity of the gyroscope at the time immediately before sampling, and w _k represents the angular velocity of the gyroscope at the current sampling time.

Further, in some specific embodiments, the INS calculation parameters further include INS speed information, and step S2 further includes:

step S24 includes:

Where C ₁₁、C₁₂、C₁₃、C₂₁、C₂₂、C₂₃、C₃₁、C₃₂、C₃₃ is the conversion matrix, f ₁、f₂、f₃ represents the accelerometer output value in the navigational coordinate system,/> The method comprises the steps that a conversion matrix from a vehicle body coordinate system to a navigation coordinate system is represented, upper and lower marks n and b respectively represent the vehicle body coordinate system and the navigation coordinate system, f ⁿ represents accelerometer output under the vehicle body coordinate system, f _e、f_n、f_u respectively represents output of accelerometers under the northeast, north and heaven directions under the northeast geographic coordinate system, f ⁿ represents accelerometer output under the vehicle body coordinate system, and f ^b represents accelerometer output under the vehicle body coordinate system;

S242: according to the output value of the accelerometer under the navigation coordinate system, calculating the speed, and obtaining the formula of INS speed information is as follows: Where v _k-1 denotes the speed at the previous time instant; a _k-1 represents the acceleration at the previous time (i.e., the output value of the accelerometer in the navigation coordinate system at the previous time), and a _k represents the acceleration at the current time (i.e., the output value of the accelerometer in the navigation coordinate system at the current time); /(I) A rotation matrix representing the last time,A rotation matrix representing the current time; g represents a gravitational constant, t _k-1 represents a previous time, and t _k represents a current time, according to a local gravitational constant setting.

In practical application, the accelerometer outputs specific force under the automobile body coordinate system, the specific force is required to be transformed, and the output value of the accelerometer under the navigation coordinate system can be obtained through the carrier transformation matrix: wherein C ₁₁、C₁₂、C₁₃、C₂₁、C₂₂、C₂₃、C₃₁、C₃₂、C₃₃ is a conversion matrix,/> The method comprises the steps of representing a conversion matrix from a vehicle body coordinate system to a navigation coordinate system, wherein f ₁、f₂、f₃ represents an output value of an accelerometer in the navigation coordinate system, n and b represent the vehicle body coordinate system and the navigation coordinate system respectively, f ⁿ represents an output of the accelerometer in the vehicle body coordinate system, f _e、f_n、f_u represents an output of the accelerometer in the east, north and sky directions in the northeast geographic coordinate system respectively, f ⁿ represents an output of the accelerometer in the vehicle body coordinate system, and f ^b represents an output of the accelerometer in the vehicle body coordinate system; and then calculating the speed according to the output value of the accelerometer under the navigation coordinate system to obtain INS speed information.

Further, in some specific embodiments, the INS calculation parameters further include INS position information, and step S24 includes:

s25: according to the INS speed information, the position of an INS subsystem is calculated to obtain INS position information;

The step S25 includes: according to INS speed information, the position of an INS subsystem is calculated, and a calculation formula is as follows:

In practical application, after the speed information of the INS is obtained by performing the speed calculation on the INS output, the INS position information can be dispersed according to the INS speed, and a specific calculation formula is as follows:

In practical application, after obtaining the GNSS input parameters of the GNSS subsystem and the INS resolving parameters of the INS subsystem, in order to perform fusion at a faster speed in a subsequent combined fusion stage, the GNSS input parameters and the INS resolving parameters may be decomposed in advance, and specifically, the INS resolving parameters of the INS subsystem may be decomposed in blocks with the output frequency of the GNSS input parameters of the GNSS subsystem as a time alignment point, and the GNSS input parameters and the INS resolving parameters may be decomposed in blocks.

In practical application, the analysis of the vehicle-mounted CNSS/INS integrated navigation data is generally divided into a correlation analysis and a consistency analysis, wherein the correlation analysis refers to whether each subsystem parameter has correlation with an integrated navigation result or not, and a correlation index is defined as extremely low correlation, medium correlation, high correlation and extremely high correlation; the consistency analysis refers to the trend between each subsystem parameter and the integrated navigation result, if the consistency is good, the subsystem parameter has a strong influence on the integrated navigation result, the threshold weight of the integrated navigation result is adjusted towards the corresponding direction, and if the consistency is poor, the integrated navigation result is adjusted towards the opposite direction.

Therefore, in step S3, correlation calculation is performed between the GNSS input parameters and the INS solution parameters and the combined output result of the GNSS/INS integrated navigation system, where the correlation calculation refers to calculation of two or more data variable elements, so as to measure the correlation closeness of the two variable factors. There is a certain association or probability between elements of the correlation to be able to perform the correlation analysis. And carrying out correlation analysis on the integral input parameters of the combined system, taking plane position errors as an example, calculating the correlation of indexes such as satellite numbers, DOP values, position speed standard deviations, positioning quality, signal to noise ratios, differential age, residual errors and the like output by different subsystems, and selecting the parameters of a proper GNSS subsystem as parameters for carrying out data fusion and judgment with INS (inertial navigation system) calculation parameters according to the analyzed correlation information.

The analytical formula is as follows: The correlation coefficient p _x,y of the two variables (X, Y) is equal to the product of the covariance cov (X, Y) between them divided by their respective standard deviation (σ _x,σ_y). The coefficient always takes a value between-1 and 1, and a variable close to 0 is called no correlation, and a variable close to 1 or-1 is called positive or negative strong correlation.

If the value of the correlation analysis is close to 1 or-1, the parameter is highly correlated to the plane error, and the parameter needs to be used in a strategy of integrated navigation; if the value of the correlation analysis coefficient is closer to 0, the correlation of the parameter to the plane error is lower, and the data of the parameter can not be used in the combined navigation strategy, so that the corresponding GNSS input parameter and INS calculation parameter with the value close to 0 in the correlation calculation result are abandoned, and the GNSS parameter and the INS parameter which can participate in the combined strategy are obtained.

In step S4, the consistency calculation refers to whether two sets of data or multiple sets of data have the same trend within a specific condition, and if the two sets of data have the same trend of increasing and decreasing, the same trend of size, and the like, the data can be classified into consistency and non-consistency.

Taking the consistency of the position of the GNSS board card in the GNSS subsystem and the combined horizontal error analysis as an example, the combined horizontal error comprises an error value obtained by subtracting the position of the GNSS board card from the position of the true value and an error value obtained by subtracting the position of the combined navigation horizontal position from the position of the true value, and then the consistency analysis is carried out by using the combined horizontal error. If the displacement difference trend of the GNSS board card is the same as the displacement difference trend of the integrated navigation, the GNSS parameters are proved to have good consistency, and the weight of the GNSS parameters before the integrated navigation should be adjusted according to the situation; if the displacement difference trend of the INS is the same, the INS parameters are proved to have good consistency, and the weight of the INS parameters before combination is adjusted at the moment, so that the weight adjustment of the combination strategy is realized, and the combination weight is obtained, wherein the specific data of the combination weight is calculated according to the parameters in practical application.

In step S5, after the consistency analysis of the overall data, the weights for setting each parameter of the GNSS subsystem and the INS subsystem to enter the integrated navigation need to be allocated, in specific weight allocation, the allocation is mainly performed according to the trend of data consistency, the initial value of the weights is set to 1, and when the two sets of data keep the trend of consistency, the interval of 0.1 is automatically adjusted every time when the two sets of data keep the trend of consistency, until the weights reach a satisfactory effect, and the operation is stopped. Therefore, the aim of automatically adjusting the combination weight of the GNSS parameters and the INS parameters in the combination data fusion stage can be fulfilled.

As can be seen from the above, according to the vehicle navigation data analysis method provided by the application, the first original data of the GNSS subsystem is obtained, and the first original data is detected, so that the error data in the first original data is deleted, and the optimized GNSS input parameters which can be used for the combination strategy are obtained; obtaining second original data of the INS subsystem and resolving the second original data to obtain INS resolving parameters which can be used for a combination strategy, further discarding GNSS input parameters and INS resolving parameters with low correlation through correlation calculation to obtain GNSS parameters and INS parameters, ensuring the effectiveness of data in a subsequent combination fusion process, then adjusting the combination weights of the GNSS parameters and the INS parameters which participate in the combination strategy through consistency calculation, carrying out combination fusion according to the combination weights, saving the weight adjustment time of the parameters which participate in the combination fusion one by one, improving the efficiency of an algorithm development process, and reducing the waste of manpower and time.

Referring to fig. 2, the present application provides a vehicle navigation data analysis device, which includes:

The first acquisition module 201: the method comprises the steps of acquiring first original data of a GNSS subsystem, and removing error data in the first original data to obtain GNSS input parameters;

The second acquisition module 202: the method comprises the steps of obtaining second original data of an INS subsystem, and resolving the second original data to obtain INS resolving parameters;

correlation calculation module 203: the method comprises the steps of performing correlation calculation on GNSS input parameters and INS calculation parameters and a combination output result of a GNSS/INS combination navigation system respectively, and discarding the GNSS input parameters and the INS calculation parameters corresponding to a median of the correlation calculation result approaching 0 to obtain GNSS parameters and INS parameters which can participate in a combination strategy;

The consistency calculation module 204: the method comprises the steps of carrying out consistency calculation on GNSS parameters and INS parameters and a combination output result respectively, and selecting GNSS parameters and INS parameters which have the same change trend as the combination output result in the consistency calculation result to carry out weight adjustment of a combination strategy so as to obtain a combination weight;

fusion module 205: and the combined data fusion of the GNSS parameters and the INS parameters is carried out according to the combined weights.

In practical applications, the first obtaining module 201 may be a GNSS receiver and software on a computer, where the GNSS receiver is configured to receive a data packet of a GNSS subsystem, and the software on the computer decodes the data packet to obtain first original data. The second obtaining module 202 may be program software connected to the INS subsystem on a computer, and is configured to calculate second raw data output by the INS subsystem, to obtain INS calculation parameters. The correlation calculation module 203 is program software for calculating correlations between the GNSS input parameters and the INS calculation parameters and the combined output results of the GNSS/INS integrated navigation system, respectively, in the computer. The consistency calculation module 204 is program software used for calculating consistency between GNSS input parameters and INS calculation parameters and combined output results of the GNSS/INS combined navigation system respectively in a computer, after the consistency calculation results are obtained, weight adjustment of a combination strategy is carried out on the GNSS parameters and the INS parameters which are the same as the variation trend of the combined output results in the consistency calculation results according to the consistency calculation results, so that combination weights are obtained, and combination fusion of the GNSS parameters and the INS parameters is conveniently carried out according to the obtained combination weights in a combination fusion stage.

The specific way of eliminating the error data in the first original data includes: according to the position information of satellites in the first original data output by the GNSS subsystem, the clock difference between the GNSS receiver and the GNSS subsystem, the pseudo range and the light speed, the positioning data are calculated, the GNSS subsystem comprises 55 satellites, the parameter output by 4 satellites is arbitrarily selected to establish an equation, the positioning data can be calculated, in order to ensure the accuracy of the positioning data, the positioning data are required to be checked, the checking step is to arbitrarily select a plurality of groups (4 satellites are a group) of other satellites to obtain corresponding parameter establishment equations, the positioning data of the same point to be checked are calculated, if the positioning data are consistent with the positioning data calculated by the parameters output by other satellites in the checking process, the data are indicated to be correct data to be used, and if the positioning data are inconsistent with the positioning data calculated by the parameters output by other satellites in the checking process, the data are indicated to be error data, and are required to be excluded. By the method, the error data corresponding to the error positioning data in the first original data can be completely removed, and the accuracy of GNSS input parameters is improved.

Further comprises: and performing error data elimination on the board card data of the GNSS subsystem. The GNSS board card is a basic integrated circuit board with an input/output interface and is made of a baseband chip, a radio frequency chip, a peripheral circuit and corresponding embedded control software, and is a core component of a GNSS receiver and used for supporting the positioning of the GNSS subsystem. Wherein, the board card data includes at least: the method comprises the steps of invalid solution, single-point solution, pseudo-range solution, floating solution, fixed solution, satellite number and satellite signal number, longitude standard deviation, latitude standard deviation, elevation standard deviation, east direction speed standard deviation, north direction speed standard deviation and sky direction speed standard deviation, and calendar data are measurement time intervals of a GNSS receiver. And randomly selecting the board card data under 15 epoch data, dividing the board card data into 3 groups of building cube programs, calculating the average value of the board card data, obtaining the error average value which is larger than a first preset threshold value in the 3 groups of average values, and removing the error board card data corresponding to the average value, thereby improving the accuracy of GNSS input parameters. The first preset threshold is set by a technician according to the actual measurement condition, the first preset threshold is the maximum allowable error value of the average value, and the error value exceeding the average value of the first preset value is the maximum allowable error value of the average value, so that error data is necessarily present in corresponding board card data, and the error data needs to be eliminated, so that the vehicle positioning information with larger difference from the true value is avoided being output in the combined fusion stage.

Further comprises: and when the clock of the satellite in the GNSS subsystem and the time of the GNSS subsystem have overlarge deviation in synchronization and exceed the allowable time deviation (second preset threshold), eliminating error data corresponding to the error difference value.

In practical application, the INS subsystem is an inertial navigation system in a GNSS/INS integrated navigation system, and is a navigation system that measures information such as acceleration and angular velocity of a vehicle based on sensors such as gyroscopes and accelerometers, and further calculates information such as heading and position. The GNSS subsystem and the INS subsystem can be mutually complemented, the GNSS subsystem provides global information of position and speed, the INS subsystem provides high-frequency data in a short time, and the accuracy and the stability of vehicle navigation can be improved by combining and fusing the two data. Since the second raw data output by the INS subsystem only includes angular velocity and acceleration, and does not include information such as attitude, velocity, and position, the second raw data of the INS subsystem needs to be resolved in order to obtain the INS attitude information, the INS velocity information, and the INS position information of the INS subsystem.

In practical application, the INS resolving parameter comprises INS gesture information, and the resolving process of the INS gesture information comprises the following steps: the INS attitude information can be calculated by using the angular velocity output by the gyroscope. The specific resolving process can utilize a quaternion to build a matrix for resolving, the quaternion uses four special numerical values to represent a rotation matrix, wherein each value of three imaginary numbers represents one coordinate axis, thereby building a three-dimensional coordinate, and the unique real number represents the rotation angle in the coordinate system. The established rotation matrix is as follows: w represents the angular velocity of the gyroscope, and x, y and z represent three output shafts of the gyroscope respectively; the subscripts n, b are denoted as the car body to navigation coordinate system. And then, the rotation matrix established by the quaternion can be used for carrying out attitude calculation on the angular velocity to obtain INS attitude information, wherein a specific calculation formula is as follows: wherein/> The magnitude of the rotation angle is represented by q _nb, which is a quaternion representation of the angle, t _k-1 represents the time immediately before sampling, t _k represents the current sampling time,For the estimated value of the rotation angle, w _k-1 represents the angular velocity of the gyroscope at the time immediately before sampling, and w _k represents the angular velocity of the gyroscope at the current sampling time.

The INS resolving parameter also comprises INS speed information, and the resolving process of the INS speed information comprises the following steps: the accelerometer outputs specific force under the automobile body coordinate system, the specific force is required to be transformed, and the output value of the accelerometer under the navigation coordinate system can be obtained through the carrier transformation matrix: wherein C ₁₁、C₁₂、C₁₃、C₂₁、C₂₂、C₂₃、C₃₁、C₃₂、C₃₃ is a conversion matrix,/> The method comprises the steps that a conversion matrix from a vehicle body coordinate system to a navigation coordinate system is represented, f ₁、f₂、f₃ represents the output value of an accelerometer in the navigation coordinate system, upper and lower marks n and b are represented as the vehicle body coordinate system and the navigation coordinate system, f _e、f_n、f_u represents the output of the accelerometer in the northeast direction, the north direction and the heaven direction in the northeast geographic coordinate system, f ⁿ represents the output of the accelerometer in the vehicle body coordinate system, and f ^b represents the output of the accelerometer in the vehicle body coordinate system; and then calculating the speed according to the output value of the accelerometer under the navigation coordinate system to obtain INS speed information.

The INS resolving parameter also comprises INS position information, and the INS position information resolving process comprises the following steps: after the speed information of the INS is obtained by performing the speed calculation on the output of the INS, the position information of the INS can be dispersed according to the speed of the INS, and a specific calculation formula is as follows:

In practical application, after the consistency analysis of the whole data, the weights for setting each parameter of the GNSS subsystem and the INS subsystem to enter the integrated navigation are required to be distributed, in specific weight distribution, the distribution is mainly performed according to the trend of the data consistency, the initial value of the weights is set to be 1, and when the two groups of data keep the trend of the consistency, the interval of 0.1 is automatically adjusted every time when the two groups of data keep the trend of the consistency, until the weights reach a satisfactory effect, and the operation is stopped. Therefore, the aim of automatically adjusting the combination weight of the GNSS parameters and the INS parameters in the combination data fusion stage can be fulfilled.

As can be seen from the above, according to the vehicle navigation data analysis device provided by the application, the first original data of the GNSS subsystem is obtained, and the first original data is detected, so that the error data in the first original data is deleted, and the optimized GNSS input parameters which can be used for the combination strategy are obtained; obtaining second original data of the INS subsystem and resolving the second original data to obtain INS resolving parameters which can be used for a combination strategy, further discarding GNSS input parameters and INS resolving parameters with low correlation through correlation calculation to obtain GNSS parameters and INS parameters, ensuring the effectiveness of data in a subsequent combination fusion process, then adjusting the combination weights of the GNSS parameters and the INS parameters which participate in the combination strategy through consistency calculation, carrying out combination fusion according to the combination weights, saving the weight debugging time of the parameters which participate in the combination fusion one by one, improving the efficiency of an algorithm development process, and reducing the waste of manpower and time.

Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application, where the electronic device 3 includes: processor 301 and memory 302, the processor 301 and memory 302 being interconnected and in communication with each other by a communication bus 303 and/or other form of connection mechanism (not shown), the memory 302 storing computer readable instructions executable by the processor 301, which when executed by an electronic device, the processor 301 executes the computer readable instructions to perform the methods in any of the alternative implementations of the above embodiments to perform the functions of: acquiring first original data of a GNSS subsystem, and removing error data in the first original data to obtain GNSS input parameters; acquiring second original data of the INS subsystem, and resolving the second original data to obtain INS resolving parameters; performing correlation calculation on GNSS input parameters and INS calculation parameters and a combined output result of the GNSS/INS combined navigation system respectively, and discarding the GNSS input parameters and the INS calculation parameters corresponding to a median of the correlation calculation results approaching 0 to obtain GNSS parameters and INS parameters which can participate in a combined strategy; carrying out consistency calculation on the GNSS parameters and the INS parameters and the combined output results respectively, and selecting the GNSS parameters and the INS parameters which have the same variation trend as the combined output results in the consistency calculation results to carry out weight adjustment of a combination strategy so as to obtain combination weights; and carrying out combination data fusion of the GNSS parameters and the INS parameters according to the combination weights.

An embodiment of the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs a method in any of the alternative implementations of the above embodiments to implement the following functions: acquiring first original data of a GNSS subsystem, and removing error data in the first original data to obtain GNSS input parameters; acquiring second original data of the INS subsystem, and resolving the second original data to obtain INS resolving parameters; performing correlation calculation on GNSS input parameters and INS calculation parameters and a combined output result of the GNSS/INS combined navigation system respectively, and discarding the GNSS input parameters and the INS calculation parameters corresponding to a median of the correlation calculation results approaching 0 to obtain GNSS parameters and INS parameters which can participate in a combined strategy;

Carrying out consistency calculation on the GNSS parameters and the INS parameters and the combined output results respectively, and selecting the GNSS parameters and the INS parameters which have the same variation trend as the combined output results in the consistency calculation results to carry out weight adjustment of a combination strategy so as to obtain combination weights; and carrying out combination data fusion of the GNSS parameters and the INS parameters according to the combination weights.

The computer readable storage medium may be implemented by any type or combination of volatile or non-volatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable Programmable Read-Only Memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ-Only Memory, EEPROM for short), erasable Programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM for short), programmable Read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.

Further, the units described as separate units may or may not be physically separate, and units displayed as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Furthermore, functional modules in various embodiments of the present application may be integrated together to form a single portion, or each module may exist alone, or two or more modules may be integrated to form a single portion.

In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.

The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and variations will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A vehicle navigation data analysis method, characterized in that the method comprises the steps of:

2. The method according to claim 1, wherein the GNSS input parameters include at least positioning data, and step S1 includes:

wherein x _i、y_i、z_i represents the location information of the satellites, i=1, 2, 3,4, respectively the serial numbers of the four satellites; v _ti denotes the clock difference between the GNSS receiver and the GNSS subsystem, which is four satellites; p _i denotes the pseudoranges of the GNSS receiver to four satellites; x, y, z represent GNSS receiver position, i.e. positioning data; c represents the propagation speed of light in vacuum, c=3×108m/s.

3. The method for analyzing vehicle navigation data according to claim 2, wherein the GNSS input parameters further include board data, and step S1 further includes:

step S14 includes: the formula for calculating the average value is: Wherein x _j represents the board data (j=1, 2, 3..15) corresponding to the jth epoch data,/> Represents the average of the first set of data,Represents the average of the second set of data,Mean values of the third set of data are shown.

4. The method according to claim 3, wherein the GNSS input parameters further include a clock of each satellite in the GNSS subsystem, and step S1 further includes:

5. The method for analyzing vehicle navigation data according to claim 1, wherein the INS resolving parameter at least includes INS posture information, and step S2 includes:

step S22 includes:

S222: the formula for carrying out gesture calculation based on the quaternion is as follows: Wherein, The magnitude of the rotation angle is represented by q _nb, which is a quaternion representation of the angle, t _k-1 represents the time immediately before sampling, t _k represents the current sampling time,For the estimated value of the rotation angle, w _k-1 represents the angular velocity of the gyroscope at the time immediately before sampling, and w _k represents the angular velocity of the gyroscope at the current sampling time.

6. The method for analyzing vehicle navigation data according to claim 5, wherein the INS calculation parameters further include INS speed information, and step S2 further includes:

S24: calculating the speed according to the output value of the INS accelerometer to obtain INS speed information;

step S24 includes:

S241: performing matrix conversion on the output value of the INS accelerometer to obtain the output value of the INS accelerometer under a navigation coordinate system:

Wherein C11, C12, C13, C21, C22, C23, C31, C32, C33 are the transformation matrices,/> The method comprises the steps of representing a conversion matrix from a vehicle body coordinate system to a navigation coordinate system, wherein f ₁、f₂、f₃ represents an output value of an accelerometer in the navigation coordinate system, n and b represent the vehicle body coordinate system and the navigation coordinate system respectively, f ⁿ represents an output of the accelerometer in the vehicle body coordinate system, f _e、f_n、f_u represents an output of the accelerometer in the east, north and sky directions in the northeast geographic coordinate system respectively, f ⁿ represents an output of the accelerometer in the vehicle body coordinate system, and f ^b represents an output of the accelerometer in the vehicle body coordinate system;

S242: according to the output value resolving speed of the INS accelerometer under the navigation coordinate system, INS speed information is obtained, and a computing formula is as follows: where v _k-1 denotes the speed at the previous time instant; a _k-1 represents the acceleration at the previous moment, that is, the output value of the INS accelerometer in the navigation coordinate system at the previous moment, and a _k represents the acceleration at the current moment, that is, the output value of the INS accelerometer in the navigation coordinate system at the current moment; /(I) A rotation matrix representing the last time,A rotation matrix representing the current time; g represents a gravitational constant, t _k-1 represents a previous time, and t _k represents a current time, according to a local gravitational constant setting.

7. The method for analyzing vehicle navigation data according to claim 6, wherein the INS resolving parameter further includes INS position information, and step S24 includes:

，

8. A vehicle-mounted navigation data analysis device, characterized in that the device comprises:

9. An electronic device comprising a processor and a memory storing computer readable instructions which, when executed by the processor, perform the steps of the method of any of claims 1-7.

10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, performs the steps of the method according to any of claims 1-7.