A method and system for real-time evaluation of ionosphere interpolation precision and optimal interpolation method selection in GNSS augmentation positioning

Abstract

The application discloses a method and system for real-time evaluation of ionosphere interpolation precision and optimal interpolation method selection in GNSS enhanced positioning, comprising: using GNSS observation data of reference stations in a CORS network, constructing a regional ionosphere model by using multiple interpolation methods; calculating ionosphere interpolation residuals of each reference station, and then calculating unit weight variance, which is used as the ionosphere interpolation precision of the reference station; obtaining the ionosphere delay standard deviation at the rover station by the error propagation law, combining the unit weight variance and the relative geometric relationship between the rover station and the reference station; comparing the ionosphere delay standard deviations among various interpolation methods, and selecting the optimal interpolation method at the point. The application realizes real-time evaluation of ionosphere interpolation precision and dynamic selection of the optimal method under the condition of a sparse CORS network or high ionosphere activity, and significantly improves the ambiguity fixing rate and positioning precision.

Description

Technical Field

[0001] This invention belongs to the field of GNSS high-precision positioning technology, specifically relating to a method and system for real-time evaluation of ionospheric interpolation accuracy and selection of the optimal interpolation method in GNSS enhanced positioning. Background Technology

[0002] High-density continuously operating reference station (CORS) networks have been deployed globally to support GNSS-enhanced positioning technologies such as network RTK and PPP-RTK. These technologies derive rover correction values ​​from the ionospheric delay of the reference stations using interpolation methods to mitigate the impact of ionospheric errors. However, ionospheric delay exhibits rapid spatiotemporal variations, and interpolation errors increase significantly, especially under sparse CORS networks or conditions of high ionospheric activity, leading to ambiguity fixation failures and decreased positioning accuracy.

[0003] Existing research mainly focuses on improving interpolation models, such as the linear combination method (LCM), linear interpolation method (LIM), and low-order surface fitting method (LSM). Although these methods are effective under certain conditions, their performance is highly dependent on ionospheric spatial correlation and network density. Furthermore, existing techniques typically use empirical prior models to evaluate ionospheric correction accuracy, which cannot dynamically adapt to ionospheric changes. Therefore, how to evaluate ionospheric interpolation accuracy in real time and select the optimal interpolation method based on this accuracy has become a key issue in improving the performance of GNSS-enhanced positioning. Summary of the Invention

[0004] The purpose of this invention is to address the problems in existing technologies, such as inaccurate interpolation accuracy assessment, reliance on experience in interpolation method selection, inability of traditional single empirical models to accurately capture rapid spatiotemporal changes in the ionosphere, and lack of consideration for method adaptability. This invention provides a method for real-time assessment of ionospheric interpolation accuracy and selection of the optimal interpolation method in GNSS-enhanced positioning. By utilizing the real-time observation residuals and adjustment theory of the reference station network, it achieves real-time and dynamic assessment of ionospheric interpolation accuracy, eliminating reliance on fixed prior empirical models and making the accuracy estimate more closely reflect the actual state of the ionosphere. Through parallel processing and real-time comparison of the expected accuracy of multiple interpolation methods, it can dynamically select the current optimal interpolation method for the rover station, significantly improving the robustness of the system in different scenarios.

[0005] According to one aspect of the present invention, a method for real-time evaluation of ionospheric interpolation accuracy and selection of the optimal interpolation method in GNSS enhanced positioning is provided, comprising:

[0006] S1. Based on GNSS observation data from reference stations in the CORS network, various interpolation methods are used to construct regional ionospheric models, and the variance-covariance matrix of parameter estimates in each regional ionospheric model is solved.

[0007] S2. Based on the variance-covariance matrix and combined with the error propagation law, calculate the standard deviation of ionospheric delay at the location of the user terminal.

[0008] S3. Using the standard deviation of ionospheric delay as accuracy information, compare the standard deviation of ionospheric delay corresponding to different interpolation methods, and select the optimal interpolation method for the user's location.

[0009] As a further technical solution, the variance-covariance matrix of the parameter estimates in the ionospheric model for each region is solved, including:

[0010] A design matrix is ​​constructed based on the ionospheric delay error and site coordinates of each reference station;

[0011] A weight matrix is ​​constructed based on the baseline difference between each reference station and the main reference station;

[0012] Based on the design matrix and weight matrix, the parameter estimates of the ionospheric model in each region are solved by the weighted least squares method, and the residual vector is calculated.

[0013] Calculate the unit weighted variance based on the residual vector and the weight matrix;

[0014] Based on the unit weight variance and the design matrix, calculate the variance-covariance matrix of the parameter estimates.

[0015] As a further technical solution, S2 includes:

[0016] The number of reference stations and the number of parameters to be estimated in the regional ionospheric model are obtained based on the variance-covariance matrix, and the extended unit weight variance scaling factor is calculated.

[0017] The extended unit weight variance scaling factor and variance-covariance matrix are introduced into the accuracy transfer model based on the error propagation law, and the ionospheric delay error at the user's location is calculated.

[0018] As a further technical solution, the calculation formula for the extended unit weight variance scaling factor is as follows:

[0019]

[0020] Where n is the number of reference stations and t is the number of parameters to be estimated in the regional ionospheric model.

[0021] As a further technical solution, the expression for the accuracy transfer model is:

[0022]

[0023] Where K is the extended unit weight variance scaling factor. The variance is the unit weight. It is the identity matrix. P is the weight matrix, and n is the number of reference stations.

[0024] As a further technical solution, after determining the optimal interpolation method for the user's location, it also includes:

[0025] The constraint weights are determined based on the standard deviation of ionospheric delay corresponding to the optimal interpolation method.

[0026] Obtaining prior values ​​of ionospheric delay based on parameter estimation;

[0027] Based on the prior values ​​of ionospheric delay and constraint weights, an observation equation containing virtual observation values ​​of ionospheric delay is constructed.

[0028] The constructed observation equations are integrated into the user-end positioning solution model for positioning calculation.

[0029] According to one aspect of the present invention, a system for real-time evaluation of ionospheric interpolation accuracy and selection of the optimal interpolation method in GNSS enhanced positioning is provided, comprising:

[0030] The first processing module is used to construct regional ionospheric models based on GNSS observation data from reference stations in the CORS network using various interpolation methods, and to solve the variance-covariance matrix of the parameter estimates in each regional ionospheric model.

[0031] The second processing module is used to calculate the standard deviation of ionospheric delay at the user's location based on the variance-covariance matrix and the error propagation law.

[0032] The third processing module is used to use the standard deviation of ionospheric delay as accuracy information, compare the standard deviation of ionospheric delay corresponding to different interpolation methods, and select the optimal interpolation method for the user's location.

[0033] According to one aspect of the present invention, an electronic device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method for real-time evaluation of ionospheric interpolation accuracy and selection of optimal interpolation method in GNSS enhanced positioning.

[0034] According to one aspect of the present invention, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps of the method for real-time evaluation of ionospheric interpolation accuracy and selection of the optimal interpolation method in GNSS enhanced positioning.

[0035] According to one aspect of the present invention, a computer program product comprising instructions is provided, which, when run on a computer, causes the computer to perform the steps of the method for real-time evaluation of ionospheric interpolation accuracy and selection of the optimal interpolation method in GNSS enhanced positioning.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] 1. This invention utilizes observation data from a reference station and employs multiple interpolation methods to construct a regional ionospheric model, achieving real-time and dynamic evaluation of ionospheric interpolation accuracy. This eliminates reliance on fixed prior empirical models, making the accuracy estimate more closely reflect the actual state of the ionosphere. By processing the expected accuracy of multiple interpolation methods in parallel and comparing them in real time, the optimal interpolation method can be dynamically selected for the rover, significantly improving the system's robustness under different scenarios.

[0038] 2. This invention uses the error propagation law, combined with the unit weight variance and the relative geometric relationship between the rover and the reference station, to solve for the ionospheric delay standard deviation. This effectively suppresses the negative impact of ionospheric residuals on ambiguity fixation and position estimation, thereby improving the success rate of ambiguity fixation and positioning accuracy. The effect is particularly significant in challenging environments such as active ionosphere or sparse network.

[0039] 3. The unified accuracy evaluation and comparison framework provided by this invention is not limited to a few specific interpolation methods or random models, has good scalability, and is easy to integrate new and better interpolation algorithms. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 The flowchart of the method for real-time evaluation of ionospheric interpolation accuracy and selection of the optimal interpolation method in GNSS enhanced positioning provided by the present invention is shown. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] like Figure 1As shown in the figure, this invention proposes a method for real-time evaluation of ionospheric interpolation accuracy and selection of the optimal interpolation method in GNSS enhanced positioning, including: S1, constructing regional ionospheric models based on GNSS observation data from reference stations in the CORS network using multiple interpolation methods, and solving the variance-covariance matrix of parameter estimates in each regional ionospheric model; S2, calculating the standard deviation of ionospheric delay at the user's location based on the variance-covariance matrix and the error propagation law; S3, using the standard deviation of ionospheric delay as accuracy information, comparing the standard deviations of ionospheric delay corresponding to different interpolation methods, and selecting the optimal interpolation method at the user's location.

[0044] Specifically, after determining the optimal interpolation method for the location of the user terminal, the method also includes: using the standard deviation of ionospheric delay corresponding to the optimal interpolation method, combined with the prior value of ionospheric delay obtained based on parameter estimation, as data input to the user terminal to participate in the user terminal positioning calculation.

[0045] Specifically, based on the exosphere delay error observations from each reference station, regional ionospheric models are established. These models consist of three regional ionospheric models (LIM, LSM, and LCM) established using different interpolation methods. Weighted least squares method is used to solve for the parameter estimates of the regional ionospheric models, and the variance-covariance matrix of the parameter estimates is calculated, including:

[0046] 1. First, construct the design matrix based on the ionospheric delay error observations and station coordinates of each reference station;

[0047] 2. Then, based on the baselines of each reference station, a weight matrix is ​​constructed. The expression for the weight matrix is ​​as follows:

[0048] (1)

[0049] in, The distribution is the baseline difference in the X and Y directions. The cofactor matrix of the baseline, Let be the cofactor of the i-th reference station. The weight matrix is ​​designed to consider the impact of station elevation differences on model parameter estimation. The larger the difference between the reference station elevation and the network average elevation, the greater the corresponding cofactor. The larger the value, the higher the weight. The smaller the value, the better. This design ensures that reference stations with significant elevation differences receive appropriate weights in the modeling process, avoiding excessive influence of stations with similar elevations on the estimation of model parameters.

[0050] 3. Next, combining the design matrix, weight matrix, and ionospheric delay error observations, the weighted least squares method is used to solve for the parameter estimates of the regional ionospheric model;

[0051] 4. Then, use the parameter estimates to calculate the residual vector, and use the residual vector and the weight matrix to calculate the unit weight variance; whereby the residual vector is calculated using... The calculated value represents the difference between the model's predicted values ​​and the actual observed values. Analyzing the residual vector is an important means of evaluating the model's fit quality. Ideally, the residuals should exhibit a random distribution without significant systematic bias. When the absolute values ​​of the residuals are generally large or show regular changes, it indicates that the linear model may be insufficient to describe the relationship between elevation and tropospheric delay, and a more complex model form may need to be considered, such as a piecewise linear model or the introduction of quadratic terms.

[0052] Parameter estimates are obtained by using weighted least squares method. The calculation formula is:

[0053] (2)

[0054] in, Let be the vector of elevation component observations. This formula minimizes the weighted sum of squared residuals. This makes the parameter estimates statistically optimal. During the solution process, The matrix must be invertible, which requires that the elevation distribution of the reference stations not be too concentrated, otherwise it will lead to ill-conditioned matrix and unstable parameter estimates.

[0055] 5. Finally, based on the unit weight variance and the design matrix, calculate the variance-covariance matrix of the parameter estimates, as shown in the following expression:

[0056] (3)

[0057] The variance-covariance matrix contains information about the accuracy of parameter estimates. The diagonal elements represent the variance of each parameter, and the standard deviation is obtained by taking the square root, reflecting the reliability of the parameter estimates; the off-diagonal elements represent the covariance between parameters, reflecting the correlation between parameters.

[0058] Specifically, based on the variance-covariance matrix and combined with the error propagation law, the standard deviation of ionospheric delay at the user's location in the GNSS positioning system is calculated, including:

[0059] 1. Based on the number of reference stations in the GNSS positioning system and the number of parameters to be estimated in the regional ionospheric model, calculate the extended unit weight variance scaling factor. The mathematical derivation of this factor is as follows:

[0060] First, define the unit weighted variance, and calculate it using the following formula:

[0061] (4)

[0062] Where V is the residual vector, P is the weight matrix, n is the number of reference stations, and t is the number of parameters to be estimated. The unit weight variance reflects the internal consistency accuracy of the observations. When its value is close to 1, it indicates that the weight allocation of the observations is reasonable and the model matches the observation data well. When the value is significantly greater than 1, it indicates that the accuracy of the observations is overestimated or that there is a systematic error in the model. When the value is significantly less than 1, it indicates that the accuracy of the observations is underestimated.

[0063] When considering the inclusion of the user terminal in the network, it is necessary to calculate the unit weight variance that includes n+1 points (n reference stations and 1 user terminal). and The relationship between them can be derived using the following formula:

[0064] (5)

[0065] Since the user terminal is located within the reference station network, the characteristics of its interpolation residuals are similar to those of the residuals of surrounding reference stations. Based on the error distribution pattern, the following assumptions can be made:

[0066] (6)

[0067] Therefore, and The following expressions are between them:

[0068] (7)

[0069] Therefore, we define the extended unit weight variance scaling factor. Here, n represents the number of reference stations, and t represents the number of parameters to be estimated. K is the core of accuracy transfer, used to adjust the accuracy transfer weight from the reference station network to the user end. The physical meaning of K is that it reflects the proportion of change in unit weight variance when expanding from n reference stations to n+1 points including the user end; its value is always less than 1 and approaches 1 as the number of reference stations increases. When the number of reference stations is small, the K value is small, indicating that the reference station network has a weaker accuracy constraint on the user end; when the number of reference stations is large, the K value is close to 1, indicating that the reference station network can transfer accuracy information more accurately. The design of K considers the impact of the geometric distribution characteristics of the reference station network on accuracy transfer. When the reference station network is sparse or unevenly distributed, K automatically adjusts to reflect this uncertainty, avoiding the transmission of overly optimistic accuracy information to the user end.

[0070] 2. The extended unit weight variance scaling factor and variance-covariance matrix are introduced into the accuracy transfer model based on the error propagation law, and the ionospheric delay standard deviation of the user terminal location in the GNSS positioning system is calculated.

[0071] Specifically, constructing the accuracy transfer model involves organically combining K with the variance-covariance matrix, including:

[0072] First, define the interpolation error. :

[0073] (8)

[0074] in, Design vectors for the user end. For parameter estimation vectors, This is the true value of the ionospheric delay at the user end.

[0075] Will Substituting into the above equation, we get:

[0076] (9)

[0077] in, A is a substitution made for the sake of convenience in writing formulas and has no actual physical meaning. It is the identity matrix. Represents the reference station weight matrix, subscript Indicates a reference station. Indicates the user end. Represents the user-side design vector. Represents the parameter estimate vector. This represents the true value of the ionospheric delay at the user end. Represents the reference station design matrix. Represents the vector of observations at the reference station. The parameter estimate matrix obtained from the adjustment is... This is the matrix formula for adjustment.

[0078] According to the law of error propagation, the variance of the interpolation error is:

[0079] (10)

[0080] in, The variance-covariance matrix of the observations. This represents the error of the rover station.

[0081] Will Substituting into the above equation, we get:

[0082] (11)

[0083] Will Substituting the values, we obtain the following formula for calculating the variance of the user-side interpolation error:

[0084] (12)

[0085] Where Q represents the covariance matrix.

[0086] Formula (12) is based on the strict error propagation law and fully considers the impact of the reference station observation accuracy, network geometric distribution and user terminal location on accuracy transmission.

[0087] Specifically, the standard deviation of the ionospheric delay at the user end is obtained through... The calculated value quantifies the uncertainty of the ionospheric delay elevation component at the user end. Unlike traditional methods that only provide tropospheric delay correction values ​​without accuracy information, this application uses a rigorous statistical model to transfer the accuracy information of the reference station network to the user end, enabling the user end to obtain a personalized accuracy assessment based on its own location characteristics. In practical applications, when the user is located inside the reference station network and the elevation is close to the network average, the STD value is small; when the user is located at the network edge or the elevation differs significantly from the network average, the STD value increases accordingly, accurately reflecting the impact of location on correction accuracy. This accuracy transfer mechanism based on the error propagation law effectively solves the fundamental problem of lacking accuracy information in existing technologies, providing reliable prior accuracy information for user end positioning calculations.

[0088] Specifically, the standard deviation of ionospheric delay is used as accuracy information, and together with the prior value of tropospheric delay obtained based on parameter estimation, it is used as data input to participate in the user-end positioning calculation, including:

[0089] 1. Compare the standard deviation of ionospheric delay from several methods and select the most suitable method.

[0090] 2. Based on the parameter estimates of the regional ionospheric model and the coordinates of the user's location, obtain the prior value of ionospheric delay.

[0091] The prior value of ionospheric delay is calculated based on parameter estimates from a regional ionospheric model and the location of the user station. Specifically, it is calculated using unknown parameters and a rough location obtained from preliminary positioning at the user station, substituted into the selected ionospheric interpolation model. The approximate elevation of the user station can be obtained through various methods, including coarse positioning based on GNSS pseudorange observations, digital elevation model queries, or input of external elevation information. In the actual system, this prior value calculation process is performed in real time and dynamically updated as the user's location changes, ensuring that the tropospheric delay correction always matches the current geographical location.

[0092] 3. Accuracy information and prior ionospheric delay values ​​are used as virtual observations in the user-end positioning solution. This includes: determining constraint weights based on accuracy information; the formula for calculating constraint weights is w=1 / σ², where σ is the standard deviation of ionospheric delay. This weight reflects the reliability of the prior information; the smaller the standard deviation, the larger the weight, indicating that the prior value is more reliable and should be given higher confidence in the solution. Based on the prior ionospheric delay values ​​and constraint weights, observation equations incorporating virtual ionospheric delay observations are constructed; these observation equations are then integrated into the user-end positioning solution model to participate in the user-end positioning solution. Specifically, in the GNSS positioning solution at the user end (rover), weighted least squares or extended Kalman filtering is typically used for parameter estimation. The observation equations include: pseudorange observation equations, carrier phase observation equations, and any existing tropospheric, ionospheric, or other constraint equations.

[0093] Specifically, an observation equation is constructed that includes virtual observations of ionospheric delay, where the virtual observations are prior ionospheric delay values ​​calculated using the optimal interpolation method, and their accuracy is characterized by the calculated standard deviation, as shown in the following expression:

[0094] (13)

[0095] (14)

[0096] (15)

[0097] (16)

[0098] (17)

[0099] Among them, (13) and (14) are carrier phase double difference formulas, (15) and (16) are pseudorange double difference formulas, and (17) is the ionospheric virtual observation approximation, which introduces prior information of the external ionospheric layer to constrain the estimation of ionospheric parameters. and These represent the double difference observations of receivers r and u on satellite j or satellite i at frequencies f1 and f2, respectively. Represents the double-difference geometric distance; This represents the double-difference ionospheric delay term; This represents the double-difference tropospheric delay term; and These represent the carrier frequencies of the first and second frequency points, respectively; the superscript "~" indicates an estimated value.

[0100] The embodiments of the present invention overcome the shortcomings of traditional methods in lacking accuracy transfer. Through precise modeling and accuracy transfer, the strong correlation between parameters is effectively reduced, and the success rate of ambiguity fixation and positioning accuracy are significantly improved. It is particularly suitable for high-precision positioning applications of network RTK and PPP-RTK under complex terrain conditions.

[0101] The implementation of the various embodiments of the present invention is based on programmed processing by a device with processor functionality. Therefore, in practical engineering, the technical solutions and functions of the various embodiments of the present invention are encapsulated into various modules. Based on this reality, and building upon the above embodiments, the embodiments of the present invention provide a system for real-time evaluation of ionospheric interpolation accuracy and selection of the optimal interpolation method in GNSS enhanced positioning. This system is used to execute a method for real-time evaluation of ionospheric interpolation accuracy and selection of the optimal interpolation method in GNSS enhanced positioning as described in the above method embodiments.

[0102] The system includes: a first processing module, used to construct regional ionospheric models based on GNSS observation data from reference stations in the CORS network using various interpolation methods, and to solve for the variance-covariance matrix of the parameter estimates in each regional ionospheric model; a second processing module, used to calculate the standard deviation of ionospheric delay at the user's location based on the variance-covariance matrix and the error propagation law; and a third processing module, used to compare the standard deviation of ionospheric delay corresponding to different interpolation methods as accuracy information, and select the optimal interpolation method for the user's location.

[0103] The system provided in this invention for real-time evaluation of ionospheric interpolation accuracy and selection of the optimal interpolation method in GNSS-enhanced positioning addresses the problems of inaccurate interpolation accuracy evaluation and reliance on experience in existing technologies. It employs several modules and utilizes observation data from a reference station to construct a regional ionospheric model using multiple interpolation methods. This enables real-time, dynamic evaluation of ionospheric interpolation accuracy, eliminating reliance on fixed prior experience models and making the accuracy estimate more closely reflect the actual state of the ionosphere. By processing the expected accuracy of multiple interpolation methods in parallel and comparing them in real-time, the system can dynamically select the optimal interpolation method for the rover station, significantly improving the system's robustness in different scenarios.

[0104] Based on the same inventive concept as the foregoing embodiments, this embodiment of the invention also provides an electronic device, including a memory and a processor. The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize a method for real-time evaluation of ionospheric interpolation accuracy and selection of the optimal interpolation method in GNSS enhanced positioning as proposed in the above embodiments.

[0105] This invention also provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, this program enables real-time, dynamic evaluation of ionospheric interpolation accuracy, eliminating reliance on fixed prior empirical models and making the accuracy estimate more closely reflect the current actual state of the ionosphere.

[0106] This invention also provides a computer program product containing instructions that, when run on a computer, generate, in whole or in part, the machine learning-based patent tradability prediction method proposed in the above embodiments. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

Claims

1. A method for real-time evaluation of ionospheric interpolation accuracy and selection of the optimal interpolation method in GNSS enhanced positioning, characterized in that, include: S1. Based on GNSS observation data from reference stations in the CORS network, various interpolation methods are used to construct regional ionospheric models, and the variance-covariance matrix of parameter estimates in each regional ionospheric model is solved. S2. Based on the variance-covariance matrix and combined with the error propagation law, calculate the standard deviation of ionospheric delay at the location of the user terminal. S3. Using the standard deviation of ionospheric delay as accuracy information, compare the standard deviation of ionospheric delay corresponding to different interpolation methods, and select the optimal interpolation method for the user's location.

2. The method for real-time evaluation of ionospheric interpolation accuracy and selection of the optimal interpolation method in GNSS enhanced positioning according to claim 1, characterized in that, Solving for the variance-covariance matrix of parameter estimates in the ionospheric model for each region includes: A design matrix is ​​constructed based on the ionospheric delay error and site coordinates of each reference station; A weight matrix is ​​constructed based on the baseline difference between each reference station and the main reference station; Based on the design matrix and weight matrix, the parameter estimates of the ionospheric model in each region are solved by the weighted least squares method, and the residual vector is calculated. Calculate the unit weighted variance based on the residual vector and the weight matrix; Based on the unit weight variance and the design matrix, calculate the variance-covariance matrix of the parameter estimates.

3. The method for real-time evaluation of ionospheric interpolation accuracy and selection of the optimal interpolation method in GNSS enhanced positioning according to claim 1, characterized in that, S2 includes: The number of reference stations and the number of parameters to be estimated in the regional ionospheric model are obtained based on the variance-covariance matrix, and the extended unit weight variance scaling factor is calculated. By introducing the extended unit weight variance scaling factor and variance-covariance matrix into the accuracy transfer model constructed based on the error propagation law, the ionospheric delay error at the user's location is calculated.

4. The method for real-time evaluation of ionospheric interpolation accuracy and selection of the optimal interpolation method in GNSS enhanced positioning according to claim 3, characterized in that, The formula for calculating the extended unit weight variance scaling factor is as follows: , Where n is the number of reference stations and t is the number of parameters to be estimated in the regional ionospheric model.

5. The method for real-time evaluation of ionospheric interpolation accuracy and selection of the optimal interpolation method in GNSS enhanced positioning according to claim 3, characterized in that, The expression for the precision transfer model is: , Where K is the extended unit weight variance scaling factor. The variance is the unit weight. It is the identity matrix. P is the weight matrix, and n is the number of reference stations.

6. The method for real-time evaluation of ionospheric interpolation accuracy and selection of the optimal interpolation method in GNSS enhanced positioning according to claim 1, characterized in that, After determining the optimal interpolation method for the user's location, the following is also included: The constraint weights are determined based on the standard deviation of ionospheric delay corresponding to the optimal interpolation method. Obtaining prior values ​​of ionospheric delay based on parameter estimation; Based on the prior values ​​of ionospheric delay and constraint weights, an observation equation containing virtual observation values ​​of ionospheric delay is constructed. The constructed observation equations are integrated into the user-end positioning solution model for positioning calculation.

7. A system for real-time evaluation of ionospheric interpolation accuracy and selection of the optimal interpolation method in GNSS enhanced positioning, characterized in that, include: The first processing module is used to construct regional ionospheric models based on GNSS observation data from reference stations in the CORS network using various interpolation methods, and to solve the variance-covariance matrix of the parameter estimates in each regional ionospheric model. The second processing module is used to calculate the standard deviation of ionospheric delay at the user's location based on the variance-covariance matrix and the error propagation law. The third processing module is used to use the standard deviation of ionospheric delay as accuracy information, compare the standard deviation of ionospheric delay corresponding to different interpolation methods, and select the optimal interpolation method for the user's location.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for real-time evaluation of ionospheric interpolation accuracy and selection of the optimal interpolation method in GNSS enhanced positioning as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for real-time evaluation of ionospheric interpolation accuracy and selection of the optimal interpolation method in GNSS enhanced positioning as described in any one of claims 1 to 6.

10. A computer program product containing instructions, characterized in that, When it is run on a computer, it causes the computer to perform the steps of the method for real-time evaluation of ionospheric interpolation accuracy and selection of the optimal interpolation method in GNSS enhanced positioning as described in any one of claims 1 to 6.

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