IGG III parameter adaptive robust method suitable for GNSS positioning in complex environment

Abstract

The application discloses an IGG III parameter adaptive robustness method suitable for GNSS positioning in a complex environment and relates to the technical field of satellite navigation and positioning. The method first establishes double-difference observation equations and state equations based on satellite observation values and a state transition matrix; parameter estimation is performed through extended Kalman filtering to obtain posterior residuals and Mahalanobis distance statistics; the posterior residuals are normalized to construct a test quantity of the IGG III model; chi-square test is performed on the Mahalanobis distance statistics of the current epoch to obtain adjusted IGG III model tuning parameters, and abnormal satellite deletion and observation value weight reduction processing are performed; when a preset maximum iteration number is met or no abnormal value appears in the current test, a floating-point solution is output, and the LAMBDA algorithm is combined to fix ambiguity to obtain a fixed solution or a floating-point solution. The method provided by the application can effectively weaken the adverse effects of complex environment NLOS and multipath errors on GNSS precise positioning, and improve GNSS positioning performance and reliability.

Description

Technical Field

[0001] This application relates to the field of satellite navigation and positioning technology, and in particular to an IGGⅢ parameter adaptive robustness method suitable for GNSS positioning in complex environments. Background Technology

[0002] Continuous, reliable, and high-precision location information is crucial in fields such as autonomous driving, disaster relief, and the Internet of Things (IoT). Among various positioning technologies, the Global Navigation Satellite System (GNSS) is widely used due to its advantages such as real-time, all-weather capability, and ability to provide consistent location information globally. Over the past few decades, the development of GNSS has greatly facilitated precise positioning in open environments. However, with the continuous expansion of positioning application scenarios, urban environments pose unavoidable challenges to GNSS positioning. Due to the large number of tall buildings, overpasses, and trees in cities, GNSS signals are easily refracted, diffracted, reflected, and blocked, causing non-line-of-sight (NLOS), multipath (MP), and other observation anomalies. These anomalies significantly reduce the reliability and accuracy of GNSS positioning, causing positioning errors. Therefore, exploring effective anomaly detection methods remains an important aspect of ensuring the reliability of GNSS in urban environments.

[0003] Traditional GNSS commonly employs the Extended Kalman Filter (EKF) for parameter estimation. However, while the EKF is theoretically derived based on the Mean Squared Error (MMSE) criterion and performs well when observations follow a normal distribution, it is extremely sensitive to observation anomalies. This means that when anomalies exist, the EKF lacks an inherent resistance mechanism, easily leading to filter divergence and significant degradation in estimation accuracy, thus severely limiting its practical performance in complex environments. Therefore, researchers typically employ robust estimation methods to mitigate the impact of outliers by adjusting the weights of observations containing different types of gross errors in a hierarchical manner. In the field of robust GNSS estimation, the IGGⅢ robust function, through its multi-segment weight factor design, separately handles moderate and large deviations, significantly improving the sensitivity of outlier detection and is widely used in GNSS precise positioning.

[0004] While the IGGⅢ method has achieved good results in addressing observation anomaly issues, it typically makes the ideal assumption that all observation error distributions have consistent statistical characteristics and often uses uniform tuning parameters for anomaly detection across different scenarios. However, in real-world urban dynamic positioning scenarios, due to the complexity and variability of the observation environment, the varying strengths of NLOS and MP affecting satellite signals in different scenarios, and the inability of existing stochastic models to accurately describe observation accuracy, the observation error distributions exhibit different statistical characteristics across different scenarios. Therefore, using fixed tuning parameters for anomaly detection in complex urban dynamic positioning scenarios is unreasonable and may exacerbate false positives and false negatives, hindering the improvement of GNSS precise positioning performance stability. Summary of the Invention

[0005] Therefore, it is necessary to provide an IGGⅢ parameter adaptive robustness method suitable for GNSS positioning in complex environments to address the above-mentioned technical problems.

[0006] The following technical solution is adopted in this specification: This specification provides an IGGⅢ parameter adaptive robustness method for GNSS positioning in complex environments, including: Obtain pseudorange and carrier phase observations from multiple GNSS satellites within the same epoch, and establish the double-difference observation equation and state equation for that epoch by combining the state transition matrix. Based on the double-difference observation equation and the state equation, parameter estimation is performed by extended Kalman filtering to obtain the filtered floating-point solution of the epoch. Based on the filtered floating-point solution, the post-hoc pseudorange residual, the post-hoc carrier phase residual, and the Mahalanobis distance statistic are obtained. The Mahalanobis distance statistic is verified using a chi-square test: if the Mahalanobis distance statistic is less than or equal to the chi-square test critical value, then the observation environment of the epoch is in a low multipath interference state. The two tuning parameters of the IGGⅢ model are adjusted using a scaling factor. and Magnification adjustment is performed; if the Mahalanobis distance statistic is greater than the chi-square test critical value, then the observation environment of the epoch is in a state of high multipath interference, and the scaling factor is adjusted accordingly. and The tuning parameters are adjusted by reducing their size to tighten the robustness criteria of the epoch observation environment; and the adjusted tuning parameters are then constrained within preset upper and lower boundaries to obtain the adaptive threshold of the epoch. and ; The absolute maximum values ​​of the post-approval pseudorange residual and the post-approval carrier phase residual are respectively compared with the adaptive threshold. and The comparison is performed, and observations that do not meet the threshold are deleted and downweighted. Based on the deleted and downweighted observations, the double-difference observation equation and the state equation are iteratively updated, and a robust floating-point solution is output through extended Kalman filtering. The ambiguity of the floating-point solution is fixed to achieve centimeter-level positioning.

[0007] Furthermore, the construction of the double-difference observation equation and state equation for the epoch specifically includes: The epochs are obtained using standard single-point positioning results and constant acceleration motion models. arrive State transition matrix ; Based on the state transition matrix In addition to the observations from multiple GNSS satellites within the same epoch, a double-difference observation equation and a state equation are established. The double-difference observation equation and state equation are expressed as follows: in, and Representing epochs and The state vector; Represents the epoch arrive The state transition matrix is ​​provided by the standard single-point localization results or the constant acceleration motion model; Indicates system state noise; Represents the epoch The observation vector consists of pseudorange observations and carrier phase observations; Represents the measurement matrix; Indicates measurement noise; and The noise is uncorrelated white noise with a mean of 0. The corresponding stochastic model is represented as: ; in, Let Variance be the variance-covariance matrix of the system state noise; For the Kronecker function; This is the variance-covariance matrix for measuring noise.

[0008] Furthermore, the parameter estimation via extended Kalman filtering is expressed as: in, The gain matrix is ​​represented by the state variance covariance matrix and the observation variance covariance matrix, which dynamically adjust the influence of state information and observation information on the current epoch state estimate. and Let represent the one-step state prediction vector and its variance-covariance matrix, respectively. Represents the identity matrix; This represents the variance-covariance matrix estimated for the current epoch.

[0009] Furthermore, the post-approval pseudorange residual and post-approval carrier phase residual are obtained by combining the observation vector of the GNSS satellite within the current epoch with the floating-point solution output by the extended Kalman filter, and the calculation formula is as follows: ; in, This represents the corresponding post-test residual; Represents the epoch The observation vector consists of pseudorange observations and carrier phase observations; Represents the measurement matrix; Represents the epoch The state vector.

[0010] Furthermore, the Mahalanobis distance statistic is calculated using the following formula: in, Indicates Mahalanobis distance; Represents the innovation vector, through Calculated; Represents the measurement matrix; The variance-covariance matrix represents the one-step state prediction vector. This is the variance-covariance matrix for measuring noise.

[0011] Furthermore, the standardization process is calculated using the following formula: in, Indicates the standardized post-test residual; Represents the post-test residual variance-covariance matrix The One diagonal element; Indicates the error per unit weight; Represents the number of observations; post-hoc residual variance-covariance matrix .

[0012] Furthermore, the adaptive tuning parameters and Acquisition, including: Based on the initial values ​​of the IGGⅢ tuning parameters and the Mahalanobis distance, adaptive adjustment is performed using an adaptive adjustment formula. By setting the upper and lower boundaries of the two tuning parameters of IGGⅢ, if the tuning parameters calculated according to the adaptive expression of the IGGⅢ tuning parameters are within the preset value range, then these parameters are used as the adaptive tuning parameters. and If the value exceeds the boundary, it is truncated to the corresponding boundary value, and the boundary value is used as the adaptive tuning parameter. and ; The adaptive adjustment formula is expressed as: in, Indicates the upper quantile as Degrees of freedom are The chi-square test is used because, due to the complexity of the dynamic positioning environment, the probability of problems with observations during data fusion is relatively high. The value is 0.2, which corresponds to the 20th percentile. and Indicates the scaling factor. The value range is 0.1 to 0.5. The value range is 0.5 to 1.0; and These represent the two tuning parameters of the IGGⅢ model. The value range is 1.0 to 2.5. The value range is 2.5 to 8.5; MD i Indicates Mahalanobis distance; The upper and lower boundaries of the two tuning parameters of IGGⅢ are expressed as follows: in, , ; , .

[0013] Furthermore, the absolute maximum values ​​of the standardized post-hoc pseudorange residual and the standardized post-hoc carrier phase residual are respectively related to the adaptive threshold. and The comparison process includes: If the absolute maximum value of the standardized post-test pseudorange residual is greater than Then delete the satellite observations corresponding to the pseudorange residuals and reconstruct the double-difference observation equations; If the absolute maximum value of the standardized post-test pseudorange residual is less than or equal to If so, then perform a carrier phase check; The carrier phase verification process includes: If the absolute maximum value of the standardized post-hoc carrier phase residual is greater than If so, delete the observations of the corresponding satellite and reconstruct the double-difference observation equation; If the absolute maximum value of the standardized apocalyptic carrier phase residual is located at Then, the variance of the observed values ​​is amplified by using the weight reduction method of the IGGⅢ model; The formula for determining whether to delete or reduce the weight of the observed values ​​is expressed as follows: in, Represents the weighting factor; It represents the variance-covariance matrix of the equivalent measurement noise.

[0014] Furthermore, this continues until the current iteration number reaches the preset maximum iteration number or the absolute maximum value of the standardized post-hoc carrier phase residual at the current epoch is less than or equal to... Stop iterative updates when the time is right.

[0015] Furthermore, the ambiguity of the robust floating-point solution is fixed using the LAMBDA method, specifically including: The optimal solution is obtained by solving the integer ambiguity using LAMBDA. and suboptimal solutions ; Based on the optimal solution and suboptimal solutions Determine the Ratio test value; When the Ratio test value is greater than a preset threshold, it is accepted. If the solution is a fixed integer least squares ambiguity, then the floating-point ambiguity solution is output. The Ratio test value is calculated using the following formula: ; in, Indicates the test threshold; Represents the ambiguity floating-point solution; Represents floating-point solutions The variance-covariance matrix.

[0016] This specification provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the above-described IGGⅢ parameter adaptive robustness method.

[0017] The above-mentioned technical solutions adopted in this specification can achieve the following beneficial effects: This specification provides an adaptive robust IGGⅢ parameter method for GNSS positioning in complex environments. Based on satellite observations and the state transition matrix, double-difference observation equations and state equations are established. Parameter estimation is performed using extended Kalman filtering to obtain post-hoc residuals and Mahalanobis distance statistics. A chi-square test is performed on the Mahalanobis distance statistics to obtain adjusted IGGⅢ model tuning parameters. Abnormal satellite deletion and observation weight reduction are then implemented. A floating-point solution is output when the preset maximum number of iterations is met or no outliers are found in the current test. The adaptive adjustment of the IGGⅢ model tuning parameters using Mahalanobis distance statistics effectively mitigates the adverse effects of NLOS and multipath errors in complex environments on precise GNSS positioning. This method is more sensitive to the observation environment, provides more reasonable IGGⅢ parameter selection, and effectively improves the accuracy and reliability of dynamic positioning in complex urban scenarios. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0019] Figure 1 This document provides a flowchart illustrating an IGGⅢ parameter adaptive robustness method for GNSS positioning in complex environments. Figure 2 A schematic diagram showing the comparison of the positioning performance of the method provided by this invention with the traditional IGGⅢ fixed parameter method and the method without using IGGⅢ. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this specification clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without creative effort are within the scope of protection of this application.

[0021] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0022] Figure 1 This is a flowchart illustrating an IGGⅢ parameter adaptive robustness method for GNSS positioning in complex environments, as described in this specification. The method specifically includes the following steps: Step 100: Obtain pseudorange and carrier phase observations from multiple GNSS satellites within the same epoch, and establish the double-difference observation equation and state equation for that epoch using standard single-point positioning results or constant acceleration motion models.

[0023] Specifically, the expressions for the double-difference observation equation and the state equation are as follows: In the formula, and Representing epochs and The state vector; Represents the epoch arrive The state transition matrix is ​​provided by the standard single-point localization results or the constant acceleration motion model; Indicates system state noise; Represents the epoch The observation vector consists of pseudorange observations and carrier phase observations; Represents the measurement matrix; Indicates measurement noise; and The noise is uncorrelated white noise with a mean of 0, and the corresponding stochastic model is: In the formula, Let Variance be the variance-covariance matrix of the system state noise; For the Kronecker function; This is the variance-covariance matrix for measuring noise.

[0024] Step 200: Based on the double-difference observation equation and the state equation, perform extended Kalman filter estimation to obtain the filtered floating-point solution for the current epoch; and based on the floating-point solution, obtain the post-hoc residual and the distance between the virtual observation Z obtained using the X parameter and the true observation Z. Mahalanobis distance statistics; Specifically, the update expression for the extended Kalman filter is: In the formula, The gain matrix is ​​represented by the state variance covariance matrix and the observation variance covariance matrix, which dynamically adjust the influence of state information and observation information on the state occupancy of the current epoch through the state variance covariance matrix and the observation variance covariance matrix. and Let represent the one-step state prediction vector and its variance-covariance matrix, respectively. Represents the identity matrix; This represents the variance-covariance matrix estimated for the current epoch.

[0025] The expression for calculating the post-test residual is: In the formula, This represents the corresponding post-test residual.

[0026] The expression for calculating Mahalanobis distance is: In the formula, Indicates Mahalanobis distance; This represents the corresponding innovation vector, through Calculated.

[0027] Step 300: Standardize the pseudorange residual and carrier phase residual obtained in step 200 to obtain standardized pseudorange residual and standardized carrier phase residual. Specifically, the expression for the standardized post-test residual is: in, Indicates the standardized post-test residual; Represents the post-test residual variance-covariance matrix The One diagonal element; Indicates the error per unit weight. This indicates the number of observations.

[0028] in: .

[0029] Step 400: Determine whether the Mahalanobis distance statistic passes the chi-square test. If the chi-square test passes, appropriately increase the tuning parameters of IGGⅢ; otherwise, decrease the tuning parameters to obtain the adaptive threshold for the current epoch. and ; Specifically, the expression for the adaptive tuning parameters of IGGⅢ is: In the formula, Indicates the upper quantile as Degrees of freedom are The chi-square test is used because, due to the complexity of the dynamic positioning environment, the probability of problems with observations during data fusion is relatively high. The value is 0.2, which corresponds to the 20th percentile. and Indicates the scaling factor. The value range is 0.1 to 0.5. The value range is 0.5 to 1.0; and These represent the two tuning parameters of the IGGⅢ model. The value range is 1.0 to 2.5. The value range is 2.5 to 8.5.

[0030] To ensure that the adjusted tuning parameters are not excessively amplified or reduced, upper and lower boundaries are set for the two tuning parameters of IGGⅢ, and their expressions are as follows: In the formula, , ; , If the tuning parameter calculated based on the adaptive expression of the IGGⅢ tuning parameter is within the preset range, then the parameter is used; if it exceeds the value boundary, then it is truncated to the corresponding boundary value.

[0031] Step 500: Outlier removal and weight reduction are performed according to the rule of "pseudorange first, carrier phase later". When the absolute maximum value of the standardized post-test pseudorange residual is greater than At that time, delete the corresponding abnormal satellite observations and reconstruct the double-difference observation equations, then return to step 200. When the absolute maximum value of the standardized post-hoc pseudorange residual is less than and equal to If the current iteration number is still less than the preset maximum iteration number, check whether the absolute maximum value of the standardized carrier phase residual is greater than 1. If true, delete the corresponding abnormal satellite observations and reconstruct the double-difference observation equations, then return to step 200. When the absolute maximum value of the standardized apocalyptic carrier phase residual is located at If the variance of the observed values ​​is amplified according to the weight reduction method of the IGGⅢ model, the process returns to step 200. Specifically, the IGGⅢ model identifies and removes pseudorange and carrier phase anomalies in observations, determining whether to delete or reduce the weight of the current observation using the following formula: s in, Represents the weighting factor; It represents the variance-covariance matrix of the equivalent measurement noise.

[0032] Considering the correlation of standardized post-hoc residuals, robustness processing is performed only on the observations corresponding to the absolute maximum value in each iteration. First, it is determined whether the absolute maximum value of the standardized post-hoc residual is greater than... If yes, delete the corresponding satellite observations; otherwise, further judgment is needed. Then, determine whether the absolute maximum value of the standardized post-test residuals is greater than... If yes, dilate the variance-covariance of the corresponding satellite observations; otherwise, leave it unchanged. The loop continues until the current iteration count reaches the preset maximum iteration count or no abnormal observations of pseudorange or carrier phase are detected, at which point it exits the loop.

[0033] Step 600: Based on the results of Step 500, repeat the iterative update of Steps 200-500 until the current iteration count reaches the preset maximum iteration count or the absolute maximum value of the standardized post-hoc carrier phase residual at the current epoch is less than or equal to... When the loop terminates, the robust floating-point solution is output. Step 700: Use the LAMBDA algorithm to fix the floating-point ambiguity. When the ambiguity is successfully fixed, output the fixed solution of the corresponding baseline vector; otherwise, directly output the floating-point solution of the baseline vector.

[0034] Specifically, the optimal solution for integer ambiguity is obtained based on LAMBDA. and suboptimal solutions .

[0035] The Ratio test value is determined using the following formula: In the formula, This represents the test threshold, which is typically set to 1.5 / 2.0 / 2.5 / 3.0.

[0036] To demonstrate the rationality and effectiveness of this invention, the solution results of the classic IGGⅢ fixed parameter method and those without the IGGⅢ method were compared, and experimental analysis was conducted using measured GNSS dynamic data from Wuhan city.

[0037] Figure 2 This chart compares the positioning performance of the present invention with that of the traditional IGGⅢ fixed-parameter method and the method without IGGⅢ. The chart shows that the present invention improves accuracy by 30.14%, 7.89%, and 28.36% in the E, N, and U directions, respectively, and the ambiguity fixation rate increases from 59.48% to 68.61%. This indicates that the present invention is more sensitive to the observation environment and can provide more reasonable IGGⅢ parameter selection, thereby effectively improving the accuracy and reliability of dynamic positioning in complex urban scenarios.

[0038] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0039] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0040] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0041] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0042] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. An adaptive robustness method for IGGⅢ parameters suitable for GNSS positioning in complex environments, characterized in that, include: Obtain pseudorange and carrier phase observations from multiple GNSS satellites within the same epoch, and establish the double-difference observation equation and state equation for that epoch by combining the state transition matrix. Based on the double-difference observation equation and the state equation, parameter estimation is performed using extended Kalman filtering to obtain the filtered floating-point solution for the epoch. Based on the filtered floating-point solution, the post-approval pseudorange residual, post-approval carrier phase residual, and Mahalanobis distance statistic are obtained. The post-approval pseudorange residual and post-approval carrier phase residual are then standardized to obtain the standardized post-approval pseudorange residual and standardized post-approval carrier phase residual. The Mahalanobis distance statistic is verified using a chi-square test: if the Mahalanobis distance statistic is less than or equal to the chi-square test critical value, then the observation environment of the epoch is in a low multipath interference state. The two tuning parameters of the IGGⅢ model are adjusted using a scaling factor. and Magnification adjustment is performed; if the Mahalanobis distance statistic is greater than the chi-square test critical value, then the observation environment of the epoch is in a state of high multipath interference, and the scaling factor is adjusted accordingly. and Reduce and adjust them separately; The adjusted tuning parameters are then constrained within preset upper and lower boundaries to obtain the adaptive threshold of the epoch. and ; The absolute maximum values ​​of the standardized post-approval pseudorange residual and the standardized post-approval carrier phase residual are respectively compared with the adaptive threshold. and The comparison is performed, and observations that do not meet the threshold are deleted and downweighted. Based on the deleted and downweighted observations, the double-difference observation equation and the state equation are iteratively updated, and a robust floating-point solution is output through extended Kalman filtering. The ambiguity of the floating-point solution is fixed to achieve centimeter-level positioning.

2. The IGGⅢ parameter adaptive robustness method for GNSS positioning in complex environments as described in claim 1, characterized in that, The construction of the double-difference observation equation and state equation for the epoch specifically includes: Using standard single-point positioning results and a constant acceleration motion model, we obtain the results from the epoch. To the epoch State transition matrix ; Based on the state transition matrix In addition to the observations from multiple GNSS satellites within the same epoch, a double-difference observation equation and a state equation are established. The double-difference observation equation and state equation are expressed as follows: in, and Representing epochs and The state vector; Represents the epoch arrive The state transition matrix is ​​provided by the standard single-point localization results or the constant acceleration motion model; Indicates system state noise; Represents the epoch The observation vector consists of pseudorange observations and carrier phase observations; Represents the measurement matrix; Indicates measurement noise; and The noise is uncorrelated white noise with a mean of 0. The corresponding stochastic model is represented as: ； in, Let Variance be the variance-covariance matrix of the system state noise; For the Kronecker function; This is the variance-covariance matrix for measuring noise.

3. The IGGⅢ parameter adaptive robustness method for GNSS positioning in complex environments as described in claim 1, characterized in that, The parameter estimation using extended Kalman filtering is expressed as follows: in, The gain matrix is ​​represented by the state variance covariance matrix and the observation variance covariance matrix, which dynamically adjust the influence of state information and observation information on the current epoch state estimate. and Let represent the one-step state prediction vector and its variance-covariance matrix, respectively. Represents the identity matrix; This represents the variance-covariance matrix estimated for the current epoch.

4. The IGGⅢ parameter adaptive robustness method for GNSS positioning in complex environments as described in claim 1, characterized in that, The Mahalanobis distance statistic is calculated using the following formula: in, Indicates Mahalanobis distance; Represents the innovation vector, through Calculated; Represents the measurement matrix; The variance-covariance matrix represents the one-step state prediction vector. This is the variance-covariance matrix for measuring noise.

5. The IGGⅢ parameter adaptive robustness method for GNSS positioning in complex environments as described in claim 1, characterized in that, The standardization process is calculated using the following formula: in, Indicates the standardized post-test residual; Represents the post-test residual variance-covariance matrix The One diagonal element; Indicates the error per unit weight; Represents the number of observations; post-hoc residual variance-covariance matrix .

6. The IGGⅢ parameter adaptive robustness method for GNSS positioning in complex environments as described in claim 1, characterized in that, The adaptive tuning parameters and Acquisition, including: Based on the initial values ​​of the IGGⅢ tuning parameters and the Mahalanobis distance, adaptive adjustment is performed using an adaptive adjustment formula. By setting the upper and lower boundaries of the two tuning parameters of IGGⅢ, if the tuning parameters calculated according to the adaptive expression of the IGGⅢ tuning parameters are within the preset value range, then these parameters are used as the adaptive tuning parameters. and If the value exceeds the boundary, it is truncated to the corresponding boundary value, and the boundary value is used as the adaptive tuning parameter. and ; The adaptive adjustment formula is expressed as follows: in, Indicates the upper quantile as Degrees of freedom are The chi-square test is used because, due to the complexity of the dynamic positioning environment, the probability of problems with observations during data fusion is relatively high. The value is 0.2, which corresponds to the 20th percentile. and Indicates the scaling factor. The value range is 0.1 to 0.

5. The value range is 0.5 to 1.0; and These represent the two tuning parameters of the IGGⅢ model. The value range is 1.0 to 2.

5. The value range is 2.5 to 8.5; MD i Indicates Mahalanobis distance; The upper and lower boundaries of the two tuning parameters of IGGⅢ are expressed as follows: in, , ; , .

7. The IGGⅢ parameter adaptive robustness method for GNSS positioning in complex environments as described in claim 1, characterized in that, The absolute maximum values ​​of the standardized post-hoc pseudorange residual and the standardized post-hoc carrier phase residual are respectively related to the adaptive threshold. and The comparison process includes: If the absolute maximum value of the standardized post-test pseudorange residual is greater than Then delete the satellite observations corresponding to the pseudorange residuals and reconstruct the double-difference observation equations; If the absolute maximum value of the standardized post-test pseudorange residual is less than or equal to If so, then perform a carrier phase check; The carrier phase verification process includes: If the absolute maximum value of the standardized post-hoc carrier phase residual is greater than If so, delete the observations of the corresponding satellite and reconstruct the double-difference observation equation; If the absolute maximum value of the standardized apocalyptic carrier phase residual is located at Then, the variance of the observed values ​​is amplified by using the weight reduction method of the IGGⅢ model; The formula for determining whether to delete or reduce the weight of the observed values ​​is expressed as follows: in, Represents the weighting factor; It represents the variance-covariance matrix of the equivalent measurement noise.

8. The IGGⅢ parameter adaptive robustness method for GNSS positioning in complex environments as described in claim 1, characterized in that, Until the current iteration number reaches the preset maximum iteration number or the absolute maximum value of the standardized post-hoc carrier phase residual at the current epoch is less than or equal to Stop iterative updates when the time is right.

9. The IGGⅢ parameter adaptive robustness method for GNSS positioning in complex environments as described in claim 1, characterized in that, The ambiguity of the robust floating-point solution is fixed using the LAMBDA method, specifically including: The optimal solution is obtained by solving the integer ambiguity using LAMBDA. and suboptimal solutions ; Based on the optimal solution and suboptimal solutions Determine the Ratio test value; When the Ratio test value is greater than a preset threshold, it is accepted. If the solution is a fixed integer least squares ambiguity, then the floating-point ambiguity solution is output. The Ratio test value is calculated using the following formula: ； in, Indicates the test threshold; Represents the ambiguity floating-point solution; Represents floating-point solutions The variance-covariance matrix.

10. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in any one of claims 1 to 9.

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