A combined navigation optimization method, computer system, and computer program product

Abstract

The present invention discloses an integrated navigation optimization method and computer system, comprising: extracting GNSS messages to obtain MSM observation messages and BRDC ephemeris messages; decoding the GNSS messages to obtain multi-system GNSS observation values ​​and multi-system satellite broadcast ephemeris parameters; constructing a GNSS pseudorange observation equation: calculating satellite positions, correcting system errors, and obtaining a linearized error equation; estimating position parameters and their variance-covariance matrix, extracting satellite ranging accuracy from the broadcast ephemeris; extracting the standard deviation of the position parameters, calculating the ranging accuracy projection matrix, and calculating the GNSS protection level; setting an along-course alarm threshold, and incorporating the GNSS solution results into the integrated navigation system when the protection level is less than the alarm threshold, thereby correcting the accumulated drift of the INS system. The present invention has the advantage of obtaining an overall more optimal integrated navigation output result and optimizing the overall accuracy of the integrated navigation system in complex environments.

Description

Technical Field

[0001] The present invention relates to the field of navigation and positioning, and in particular to a combined navigation optimization method and a computer system. Background Art

[0002] GNSS / INS integrated navigation is a common technique for obtaining high-precision positioning in industrial production. GNSS offers high accuracy and low cost, but its navigation information is incomplete and cannot function without necessary observations. INS, a navigation system based on accelerometers and gyroscopes, measures the acceleration and angular velocity of an aircraft in space, thereby calculating its position, velocity, and attitude. It outputs navigation information independently without external information, exhibits strong anti-interference capabilities, and enables autonomous navigation. In GNSS / INS integrated navigation, the INS system is unaffected by the external environment and can independently perform dead reckoning. Currently, a simpler strategy for handling complex environments is to discard GNSS results with reduced accuracy and use only the INS system for dead reckoning. However, this leads to a gradual accumulation of errors, significantly degrading positioning quality for integrated navigation users exposed to complex environments for extended periods. Fusion of the two systems is a key approach to optimizing navigation and positioning results.

[0003] GNSS is a positioning method that determines the distance between the satellite and the user by measuring the time difference between the transmission of a satellite signal and its reception by the user. This determination then uses the spatial resection method to obtain the user's absolute position. However, in complex environments such as modern urban elevated roads, urban canyons, and dense forests, GNSS signals are affected by factors such as obstruction, reflection, and diffraction. This can cause the measured pseudorange and carrier phase values ​​to deviate from their true values, reducing positioning accuracy. Even the more widely used and mature RTK technology cannot avoid this problem. Using RTK in such complex environments can lead to varying degrees of accuracy degradation in the GNSS / INS integrated navigation system.

[0004] Currently, a relatively simple strategy for handling complex environments is to set thresholds to detect GNSS results with degraded accuracy and activate alternative navigation solutions. However, these thresholds often rely on manual experience and lack adaptability to dynamic changes in data and scenarios. This can lead to GNSS results being incorrectly discarded or included, causing a decrease in system accuracy. This is an area that this application focuses on improving. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide an integrated navigation optimization method and a computer system to correct the accumulated error of the inertial navigation device INS and improve the final result of the integrated navigation.

[0006] In order to solve the above technical problems, the present invention provides a combined navigation optimization method, comprising the following steps:

[0007] Step S1: Collect GNSS observation data in a real environment and extract the RTCM message transmitted by the GNSS chip, including MSM observation information and broadcast ephemeris navigation information;

[0008] Step S2: Decode the MSM observation information and broadcast ephemeris navigation information to obtain multi-system GNSS observation values ​​and multi-system satellite broadcast ephemeris parameter information;

[0009] Multi-system GNSS observations include GNSS pseudorange, carrier phase, and signal-to-noise ratio;

[0010] Step S3: Construct the GNSS pseudorange observation equation based on the analyzed observation values: ;

[0011] Where, is the observed pseudorange, To directly calculate the pseudorange, is the speed of light, and The time when the receiver receives the signal and the time when the satellite transmits the signal are respectively and are the ionospheric and tropospheric propagation errors, respectively. Propagate noise for signal;

[0012] Step S4: Calculate the satellite position using the multi-system broadcast ephemeris, and use the model to correct the various system errors in the observation equation to obtain the linearized error equation: ;

[0013] Where, is the measured value, is the design matrix calculated after Taylor expansion, To estimate the parameters, To measure noise;

[0014] Step S5: Use the optimal unbiased parameter estimation method to obtain the position parameters , and obtain the variance-covariance matrix of the location parameters , and the a priori ranging accuracy of each satellite obtained from the broadcast ephemeris ;

[0015] Step S6: Extract the standard deviation of the position parameters from the array , use the least squares method or Kalman filter method to obtain the ranging accuracy projection matrix , calculate the GNSS protection level ;

[0016] for: ;

[0017] Where: is the variance-covariance matrix block corresponding to the location parameter, It is the transformation matrix that transforms the position parameters from the ECEF coordinate system to the ENU coordinate system;

[0018] based on Is a symmetric matrix, and its eigenvalue decomposition is obtained: ;

[0019] in: is the eigenvector matrix, which contains the error direction information; It is a diagonal matrix containing eigenvalues, which contains information about the error size; for horizontal positioning, two eigenvalues ​​are obtained and ,Right now: ;

[0020] According to the heading information provided by the integrated navigation system, the maximum eigenvalue , minimum eigenvalue Adjust along the heading, that is, generate the rotation matrix of the navigation coordinate system to the carrier coordinate system based on the heading, and convert the maximum eigenvalue Projected along the heading direction, the minimum eigenvalue Projected to the vertical heading direction constitutes an asymmetric protection level ;

[0021] That is, the protection level along the heading is , vertical heading protection level ,by As the long axis, Asymmetric protection level represented by an ellipse forming the minor axis ;

[0022] Eigenvalue and Reflects the size of the error, the eigenvector matrix Reflects the direction of the error. Asymmetric protection level The construction of more accurately reflects the positioning error distribution of the integrated navigation system in a specific heading, especially in scenarios where the heading angle error has a greater impact on positioning accuracy, significantly improving positioning accuracy and reliability;

[0023] Step S7: Setting the Alarm Threshold Along Course :

[0024] Protection level When the GNSS results are reliable, the GNSS solution results are incorporated into the integrated navigation system to correct the accumulated drift of the INS system;

[0025] Protection level When GNSS is rejected, only INS navigation calculation is performed;

[0026] Asymmetric protection levels When the GNSS results are reliable, the GNSS solution results are incorporated into the integrated navigation system to correct the accumulated drift of the INS system;

[0027] Asymmetric protection levels When , GNSS results are rejected and only INS navigation calculation is used.

[0028] In step S2, the message is decoded according to the RTCM message transmission protocol, and the GPS / BDS / Galileo / QZSS four-system GNSS observation information and GPS / BDS / Galileo / QZSS four-system satellite broadcast ephemeris parameter information in MSM7 format are extracted from the original message file.

[0029] In step S4, the satellite instantaneous position and satellite clock error are calculated using multi-system broadcast ephemeris, the influence of ionospheric delay is eliminated using a dual-frequency ionosphere-free combination, the tropospheric zenith delay is corrected using the UNB3 model, and various system errors are converted into observation equations.

[0030] In step S5, the least square method and the extended Kalman filter are used to estimate the parameters , and obtain the variance-covariance matrix of the parameters Extract the user range accuracy code value URA of multiple systems from the broadcast ephemeris, and compare it with the URA table in the satellite navigation system space signal interface control ICD file to obtain the nominal ranging accuracy vector of each satellite ;

[0031] For the least squares method: ;

[0032] Where: is the parameter estimate, is the design matrix calculated after Taylor expansion, is the covariance matrix of the observations, is the observation residual vector;

[0033] For the Extended Kalman Filter: ;

[0034] Where: It is The Kalman gain matrix for the epoch, is the adaptive observation noise matrix, It is epoch parameter estimates, That is the variance-covariance matrix of the epoch's parameters , It is a unit array. is the observation residual vector, and They represent the Kalman one-step forecast covariance value and one-step forecast parameter estimate, respectively.

[0035] The adaptive observation noise matrix Adaptive calculation is used to suppress noise disturbance. The calculation method is: ;

[0036] in: and They are Epoch and The observation noise matrix of the epoch, is the adaptive scale factor, is the new information, that is, the difference between the observed residual vector and the transformed predicted quantity, as follows:

[0037] ;

[0038] When updating will serve as New calculation formula Use it when the new information increases The filter gain will also increase. Reduce, thereby reducing the impact of fluctuating observation residuals on the robustness of the filter.

[0039] The present invention also provides a computer system comprising a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the above-mentioned combined navigation optimization method.

[0040] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned combined navigation optimization method are implemented.

[0041] The present invention also provides a computer program product, comprising a computer program, which implements the steps of the above-mentioned combined navigation optimization method when executed by a processor.

[0042] The superior effects of the present invention are as follows: the GNSS positioning results correct the accumulated errors of the inertial navigation device INS, effectively improving the final results of the integrated navigation, performing quality assessment and screening on the GNSS positioning solutions in complex environments, obtaining an overall better integrated navigation output result, and optimizing the overall accuracy of the integrated navigation system in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0044] Figure 1 is a flow chart of a specific embodiment of the present invention;

[0045] Figure 2 This is a comparison diagram of horizontal positioning errors of combined navigation according to a specific embodiment of the present invention;

[0046] Figure 3 3 is a comparison diagram of the combined navigation heading angle error of an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0048] like Figure 1 As shown, the present invention provides a combined navigation optimization method, comprising the following steps:

[0049] Step S1: Collect GNSS observation data in a real-world environment and receive and save observation and ephemeris messages provided by the GNSS chip via the serial port. This example uses GNSS data acquired in a real-world environment on the afternoon of March 26, 2024, with a sampling frequency of 10 Hz. It contains 63,112 valid data items, covering typical complex scenarios such as underpasses, tree-lined areas, and urban canyons.

[0050] Extract the RTCM message sent by the GNSS chip, including MSM observation information and broadcast ephemeris navigation information.

[0051] Step S2: Decode the binary MSM observation information and broadcast ephemeris navigation information according to the RTCM3.3 message transmission protocol, and extract the GPS / BDS / Galileo / QZSS four-system GNSS observation values ​​and GPS / BDS / Galileo / QZSS four-system satellite broadcast ephemeris parameter information in MSM7 format from the original message file;

[0052] The RTCM3.3 message transmission protocol sends correction information to user devices via ground stations, including atmospheric refraction correction, satellite clock error, and orbit error, enabling users to achieve millimeter-level or even higher positioning accuracy. It also includes error detection and correction mechanisms, such as CRC, to ensure that data is not tampered with or lost during transmission, further enhancing data reliability and integrity. Observations and broadcast ephemeris parameter information in the MSM7 format further enhance this precise positioning capability. Simultaneously, data from the four satellite navigation systems – GPS, BDS, Galileo, and QZSS – are utilized to significantly enhance signal coverage and improve positioning reliability and accuracy. Multi-system support provides more stable and accurate positioning services, especially in certain regions or under specific conditions where some satellite systems may be interfered with or restricted.

[0053] The GNSS observation values ​​include GNSS pseudorange, carrier phase, and signal-to-noise ratio.

[0054] Step S3: Construct the GNSS pseudorange observation equation based on the pseudorange observation values ​​of the four systems: GPS / BDS / Galileo / QZSS: ;

[0055] Where, is the observed pseudorange, To directly calculate the pseudorange, is the speed of light, and The time when the receiver receives the signal and the time when the satellite transmits the signal are respectively and are the ionospheric and tropospheric propagation errors, respectively. The pseudorange observation values ​​of the four systems make full use of the satellite resources of different systems, increase the number of visible satellites, and improve the spatial geometric distribution of satellites, thereby improving positioning accuracy and reliability; the GNSS pseudorange observation equation takes into account multiple errors and noises to improve positioning accuracy.

[0056] Step S4: Use multi-system broadcast ephemeris to calculate the instantaneous position of the satellite and the satellite clock error, fully utilizing the advantages of multiple satellite navigation systems to improve positioning accuracy and reliability, and to compensate for the insufficient coverage or signal quality issues of a single system in certain areas; use a dual-frequency ionosphere-free combination to eliminate the influence of ionospheric delay, improve positioning accuracy, and provide additional redundant information to help detect and eliminate abnormal data and enhance the reliability of observation data; use the UNB3 model to correct the tropospheric zenith delay, further improving positioning accuracy. In particular, the UNB3 model takes into account changes in meteorological parameters such as atmospheric pressure, temperature, and water vapor pressure, adapts to the tropospheric delay correction requirements under different climatic conditions, and reduces various system errors to the observation equation;

[0057] Use Taylor expansion to linearize the observation variance and obtain the linearized error equation: ;

[0058] Where: is the measured value, is the design matrix calculated after Taylor expansion, To estimate the parameters, To measure noise, the Taylor expansion simplifies the calculation process, improves computational efficiency, and works well with the least squares method and Kalman filter. By linearizing the error equation, it more accurately describes the relationship between the observed and true values, reducing positioning bias caused by nonlinear errors. Furthermore, combined with optimal unbiased parameter estimation methods, positioning accuracy and reliability are further improved.

[0059] Step S5: Estimate position parameters using the least squares method and the extended Kalman filter method , and obtain the variance-covariance matrix of the location parameters Extract the user range accuracy code value URA of multiple systems from the broadcast ephemeris, and compare it with the URA table in the satellite navigation system space signal interface control ICD file to obtain the nominal ranging accuracy vector of each satellite Improve positioning accuracy, enhance system robustness, improve computational efficiency, and have advantages in handling nonlinear errors and providing comprehensive position parameter estimation information;

[0060] For the least squares method:

[0061] ;

[0062] Where: is the parameter estimate, is the design matrix calculated after Taylor expansion, is the covariance matrix of the observations, is the observation residual vector;

[0063] For the Extended Kalman Filter:

[0064] ;

[0065] Where: It is The Kalman gain matrix for the epoch, is the adaptive observation noise matrix, It is epoch parameter estimates, That is the variance-covariance matrix of the epoch's parameters , It is a unit array. is the observation residual vector, and They represent the Kalman one-step forecast covariance value and one-step forecast parameter estimate, respectively.

[0066] The adaptive observation noise matrix Adaptive calculation is used to suppress noise disturbance. The calculation method is: ;

[0067] in: and They are Epoch and The observation noise matrix of the epoch, is the adaptive scale factor, is the new information, that is, the difference between the observed residual vector and the transformed predicted quantity, as follows:

[0068] ;

[0069] When updating will serve as New calculation formula Use it when the new information increases The filter gain will also increase. Reduce, thereby reducing the impact of fluctuating observation residuals on the robustness of the filter.

[0070] Step S6: Assume that after the positioning solution, the position error only contains the deviation term from the satellite ranging error And the variance is Gaussian white noise, that is, the position error Subject to the mean , the standard deviation is Gaussian distribution of: , then the GNSS protection level is calculated as follows: ;

[0071] Where, is the probability density function of the standard normal distribution at the preset probability The right-hand side single-tail quantile value under the current situation is the preset probability. , corresponding to ;

[0072] Represents the positioning error in 99% of cases when the assumptions are met. The upper limit value will not be exceeded. According to the error propagation law, Calculated as: ;

[0073] Where: is the variance-covariance matrix block corresponding to the location parameter, It is the transformation matrix that transforms the position parameters from the ECEF (Earth-Centered, Earth-Fixed) coordinate system to the station center coordinate system (ENU coordinate system);

[0074] based on Is a symmetric matrix, and its eigenvalue decomposition is obtained: ;

[0075] in: is the eigenvector matrix, which contains the error direction information; It is a diagonal matrix containing eigenvalues, which contains information about the error size; for horizontal positioning, two eigenvalues ​​are obtained and ,Right now: ;

[0076] According to the heading information provided by the integrated navigation system, the maximum eigenvalue , minimum eigenvalue Adjust along the heading to make the maximum eigenvalue Projected along the heading direction, the minimum eigenvalue Projected to the vertical heading direction constitutes an asymmetric protection level , that is, the protection level along the heading is , vertical heading protection level ,by As the long axis, Asymmetric protection level represented by an ellipse forming the minor axis Especially in scenarios where the heading angle error has a significant impact on positioning accuracy, it can more accurately reflect the positioning error distribution of the integrated navigation system in a specific heading;

[0077] ;

[0078] It is a mapping matrix that projects the satellite nominal ranging accuracy from the observation domain to the positioning domain. The calculation method is different in the least squares estimation and Kalman filtering. In the least squares method, The calculation is as follows:

[0079] ;

[0080] Where: It is an operator for extracting position parameters. It uses the principle of least squares method to estimate the position parameters and their error characteristics by best fitting the observed data, thereby obtaining more accurate positioning results. At the same time, due to the relatively convenient calculation of least squares method, it is suitable for scenarios with high real-time requirements.

[0081] In Kalman filtering, The calculation is as follows:

[0082] ;

[0083] In this way, the state estimation and error covariance matrix are dynamically adjusted to adapt to the changes in the system state and the uncertainty of the observation data, maintaining high positioning accuracy and stability under noise interference, and having high system adaptability and robustness.

[0084] Step S7: Setting the Alarm Threshold Along Course , in complex environments:

[0085] When the protection level When the GNSS results are considered reliable, the GNSS solution results are incorporated into the integrated navigation system to correct the accumulated drift of the INS system;

[0086] Specifically, based on the standard GNSS / INS 15-dimensional EKF integrated navigation, the state transfer matrix is ​​first constructed using INS data based on the inertial navigation mechanical arrangement framework for EKF time update. GNSS data is then used as the measurement value. During the measurement phase, the difference between the system state value and the time update is calculated to obtain the new information. After calculating the update weight, the new information is multiplied by the update weight to obtain the filter correction value. The sum of the two values ​​corrects the accumulated error drift of the INS.

[0087] When the protection level When , reject GNSS results and use only INS navigation calculation, that is, only use INS to perform EKF time update results.

[0088] Asymmetric protection levels When the GNSS results are reliable, the GNSS solution results are incorporated into the integrated navigation system to correct the accumulated drift of the INS system;

[0089] Specifically, based on the standard GNSS / INS 15-dimensional EKF integrated navigation, the state transfer matrix is ​​first constructed using INS data based on the inertial navigation mechanical arrangement framework for EKF time update. GNSS data is then used as the measurement value. During the measurement phase, the difference between the system state value and the time update is calculated to obtain the new information. After calculating the update weight, the new information is multiplied by the update weight to obtain the filter correction value. The sum of the two values ​​corrects the accumulated drift of the INS system.

[0090] Asymmetric protection levels When , reject GNSS results and use only INS navigation calculation, that is, only use INS to perform EKF time update results.

[0091] The protection level is a credibility indicator derived from a statistical perspective based solely on GNSS data itself. It provides a more reliable basis for judging the switching of different combined navigation schemes without adding additional data input, so as to optimize the overall accuracy of the GNSS / INS combined navigation system in complex environments. In order to verify the effectiveness of the present invention, GNSS and IMU data measured under a city elevated road on March 26, 2024 were selected to compare the GNSS / INS combined navigation results using the present invention for GNSS quality assessment with schemes based on empirical indicators such as the number of visible satellites, average satellite signal-to-noise ratio, and prior positioning accuracy. The results are as follows: Figure 2 and Figure 3 As shown, using the present invention to determine GNSS quality and incorporating the correction results into integrated navigation successfully suppresses IMU error accumulation, significantly improving both horizontal positioning accuracy and heading angle accuracy. This demonstrates that the present invention accurately estimates GNSS positioning quality and improves the results of GNSS / INS integrated navigation systems in real-world environments.

[0092] In summary, in complex environments, RTK services and INS systems are used for combined navigation. The current indicators for judging the quality of GNSS positioning mainly include the average signal-to-noise ratio of all satellites, the number of observed satellites, the average altitude angle of all satellites, RTK nominal accuracy, etc. In open environments, RTK nominal accuracy reflects the positioning error to a certain extent, but it cannot fully and effectively reflect the actual positioning situation in complex environments. Other indicators are relatively rough, and only a very small part of the GNSS information is used for judgment. Moreover, it is not sensitive to complex environments, which can lead to misjudgment of GNSS quality. In response to this situation, the present invention uses GNSS original observations for solution, combines satellite ranging errors and solution variances to construct the GNSS protection level, utilizes most of the information provided by GNSS, more fully reflects the quality of GNSS positioning, and verifies the effectiveness of this method in complex environments, which significantly optimizes the continuity, stability and positioning accuracy of GNSS / INS combined navigation.

[0093] The present invention also provides a computer system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described combined navigation optimization method. Thus, the computer system disclosed in the present invention inherits the implementation methods and technical effects of the various combined navigation optimization methods described herein.

[0094] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned combined navigation optimization method are implemented.

[0095] The present invention also provides a computer program product, comprising a computer program, which implements the steps of the above-mentioned combined navigation optimization method when executed by a processor.

[0096] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A combined navigation optimization method comprising the following steps: Step S1: Collect GNSS observation data and extract messages, including MSM observation information and broadcast ephemeris navigation information; Step S2: Decode the MSM observation information and broadcast ephemeris navigation information to obtain multi-system GNSS observation values ​​and multi-system satellite broadcast ephemeris parameter information; Step S3: Construct the GNSS pseudorange observation equation based on the analyzed observation values: ； Where, is the observed pseudorange, To directly calculate the pseudorange, is the speed of light, and The time when the receiver receives the signal and the time when the satellite transmits the signal are respectively and are the ionospheric and tropospheric propagation errors, respectively. Propagate noise for signal; Step S4: Calculate the satellite position, correct the systematic error in the observation equation, and obtain the linearized error equation: ; Where, is the measured value, is the design matrix calculated after Taylor expansion, To estimate the parameters, To measure noise; Step S5: Use the optimal unbiased parameter estimation method to obtain the position parameters , and its variance-covariance matrix , extract the prior ranging accuracy of each satellite from the broadcast ephemeris ; Step S6: Extract the standard deviation of the position parameters from the array , use the least squares method or Kalman filter method to obtain the ranging accuracy projection matrix , calculate the GNSS protection level ; Where: is the probability density function of the standard normal distribution at the preset probability The right one-tail quantile value below; It is a mapping matrix that projects the satellite nominal ranging accuracy from the observation domain to the positioning domain; for: ; Where: is the variance-covariance matrix block corresponding to the location parameter, It is the transformation matrix that transforms the position parameters from the ECEF coordinate system to the ENU coordinate system; Step S7: Setting the Alarm Threshold Along Course ; Protection level When the GNSS solution is integrated into the integrated navigation system, the accumulated drift of the INS system is corrected. Protection level When , GNSS results are rejected and only INS navigation calculation is used.

2. The integrated navigation optimization method according to claim 1, characterized in that: The multi-system GNSS observation values ​​include GNSS pseudorange, carrier phase and signal-to-noise ratio.

3. The integrated navigation optimization method according to claim 1, characterized in that: In step S5, the position parameters are estimated using the least square method and the extended Kalman filter method. , and its variance-covariance matrix Extract the user range accuracy code value URA of multiple systems from the broadcast ephemeris, and compare it with the URA table in the space signal interface control file of the satellite navigation system to obtain the nominal ranging accuracy vector of each satellite ; For the least squares method: ; Where: is the parameter estimate, is the design matrix calculated after Taylor expansion, is the covariance matrix of the observations, is the observation residual vector; For the extended Kalman filter method: ; Where: It is epoch parameter estimates, That is the variance-covariance matrix of the epoch's parameters , It is a unit array. is the observation residual vector, and They represent the Kalman one-step forecast covariance value and one-step forecast parameter estimate, respectively.

4. The integrated navigation optimization method according to claim 3, characterized in that: The step S6 uses the least square method or the Kalman filter method to obtain the ranging accuracy projection matrix , in the least squares method, The calculation is as follows: ； Where: is an operator for extracting positional parameters; In the Kalman filter method, The calculation is as follows: 。 5. The integrated navigation optimization method according to claim 1, characterized in that: In step S2, the message is decoded according to the RTCM message transmission protocol, and the GPS / BDS / Galileo / QZSS four-system GNSS observation information and GPS / BDS / Galileo / QZSS four-system satellite broadcast ephemeris parameter information in MSM7 format are extracted from the original message file.

6. The integrated navigation optimization method according to claim 1, characterized in that: In step S4, the satellite instantaneous position and satellite clock error are calculated using the multi-system broadcast ephemeris, the influence of ionospheric delay is eliminated using the dual-frequency ionosphere-free combination, the tropospheric zenith delay is corrected using the UNB3 model, and various system errors are converted into observation equations.

7. The integrated navigation optimization method according to claim 1, characterized in that: In step S6, based on Is a symmetric matrix, and its eigenvalue decomposition is obtained: ; in: is the eigenvector matrix, which contains the error direction information; It is a diagonal matrix containing eigenvalues, which contains information about the error size; for horizontal positioning, two eigenvalues ​​are obtained and ,Right now: ; According to the heading information provided by the integrated navigation system, the maximum eigenvalue , minimum eigenvalue Adjust along the heading to make the maximum eigenvalue Projected along the heading direction, the minimum eigenvalue Projected to the vertical heading direction constitutes an asymmetric protection level ; Asymmetric protection levels When the GNSS solution is integrated into the integrated navigation system, the accumulated drift of the INS system is corrected. Asymmetric protection levels When , GNSS results are rejected and only INS navigation calculation is used.

8. The integrated navigation optimization method according to claim 7, characterized in that: The protection level along the heading is , vertical heading protection level ,by As the long axis, Asymmetric protection level represented by an ellipse forming the minor axis .

9. A computer system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: The processor executes the computer program to implement the steps of the combined navigation optimization method according to any one of claims 1 to 8.

10. A computer program product comprising a computer program, characterized in that: When the computer program is executed by a processor, the steps of the combined navigation optimization method according to any one of claims 1 to 8 are implemented.

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