Method for accelerating GNSS (Global Navigation Satellite System) real-time satellite clock error estimation convergence

Abstract

The invention discloses a method for accelerating convergence of GNSS (Global Navigation Satellite System) real-time satellite clock error estimation, which belongs to the field of satellite positioning time service and comprises the following steps of: constructing an error equation corresponding to each GNSS satellite of each observation station; combining the error equations respectively corresponding to the plurality of GNSS satellites of the plurality of observation stations to obtain an error equation in a matrix form; according to the GNSS real-time satellite clock error estimation method, the GNSS ultra-fast forecast satellite clock error is introduced, the process of real-time satellite clock error estimation through the Kalman filtering method is restrained, and due to the fact that the precision of the GNSS ultra-fast forecast satellite clock error is high, convergence of GNSS real-time satellite clock error estimation is accelerated, and a high-reliability GNSS real-time satellite clock error product is obtained.

Description

Technical Field

[0001] The present invention belongs to the field of satellite positioning and timing, and particularly relates to a method for accelerating the convergence of GNSS real-time satellite clock bias estimation. Background Art

[0002] The real-time precise point positioning technology of the Global Navigation Satellite System (GNSS) has been widely applied to multiple fields such as geodesy, atmospheric monitoring, geological disaster warning, time transfer, and precision agriculture. High-precision GNSS real-time satellite clock bias products are one of the key prerequisites for realizing real-time precise point positioning. The changes of the atomic clocks installed on GNSS satellites are extremely complex, and the accuracy loss of their long-term forecasts is relatively large. Therefore, a real-time estimation method is usually adopted to obtain the GNSS real-time satellite clock bias.

[0003] When estimating the GNSS real-time satellite clock bias, it will be affected by various factors such as interrupted observation data, communication equipment, and network delay, resulting in the interruption of GNSS real-time satellite clock bias estimation. After the observation data is restored, it is necessary to restart the clock bias estimation program, which involves estimating a large number of tropospheric and ambiguity parameters. In addition, due to the relatively slow geometric changes of static ground stations relative to GNSS satellites, the convergence of GNSS real-time satellite clock bias estimation is slow, reducing the service quality of real-time precise point positioning during this period. Therefore, how to accelerate the convergence of GNSS real-time satellite clock bias estimation and improve the GNSS satellite service performance during the convergence stage still faces great challenges.

[0004] In current GNSS real-time satellite clock bias estimation methods, the satellite clock bias is estimated as a white noise model, and the initial value of the satellite clock bias comes from broadcast ephemeris or the previous epoch. When estimating the clock bias, a relatively large variance is applied in the filtering. Since GNSS satellites in medium or high orbits fly relatively slowly and the geometric structure between the satellites and the ground station network changes slowly, it takes about half an hour to reach the convergence state.

[0005] Therefore, how to provide a method for accelerating the convergence of GNSS real-time satellite clock bias estimation with fast convergence speed and high reliability has become an important issue. Summary of the Invention

[0006] In order to solve the above problems existing in the prior art, the present invention provides a method for accelerating the convergence of GNSS real-time satellite clock bias estimation.

[0007] The technical problems to be solved by the present invention are realized through the following technical solutions: In a first aspect, the present invention provides a method for accelerating the convergence of GNSS real-time satellite clock bias estimation, the method comprising: Construct error equations corresponding to each GNSS satellite observed at each station; Combine the error equations corresponding to multiple GNSS satellites observed at multiple stations respectively to obtain an error equation in matrix form; Introduce the GNSS ultra-rapid predicted satellite clock offset to constrain the process of real-time satellite clock offset estimation using the Kalman filtering method, and obtain the GNSS real-time satellite clock offset product; some parameters in the Kalman filtering method are determined based on the error equation in matrix form.

[0008] Optionally, constructing the error equation corresponding to each GNSS satellite observed at each station includes: For each GNSS satellite observed at each station, adopt the dual-frequency ionosphere-free combination method to determine the error equation corresponding to each GNSS satellite observed at each station.

[0009] Optionally, the error equation corresponding to each GNSS satellite observed at each station includes: ; ; where is the a posteriori residual of the pseudorange observation; is the a posteriori residual of the carrier phase observation; is the receiver clock offset of the receiver; is the satellite clock offset of the GNSS satellite; is the mapping function; represents the zenith tropospheric wet delay; is the difference between the pseudorange observation and the pseudorange calculated value; is the difference between the carrier phase observation and the carrier phase calculated value; is the dual-frequency ionosphere-free wavelength; is the dual-frequency ionosphere-free ambiguity.

[0010] Optionally, the error equation in matrix form includes: ; where is the a posteriori residual vector; is the design matrix; is the parameter vector to be estimated, including: satellite clock offset, receiver clock offset, zenith tropospheric wet delay, and ambiguity parameter; is the vector of the difference between the pseudorange / carrier phase observation and the pseudorange / carrier phase calculated value.

[0011] Optionally, some parameters in the Kalman filtering method are determined based on the error equation in matrix form, including: The design matrix, the difference vector between the observed value and the calculated value in the Kalman filtering method are both determined based on the error equation in matrix form.

[0012] Optionally, introduce GNSS ultra-rapid predicted satellite clock offsets to constrain the process of real-time satellite clock offset estimation using the Kalman filtering method to obtain GNSS real-time satellite clock offset products, including: Determine GNSS ultra-rapid predicted satellite clock offsets; Based on the GNSS ultra-rapid predicted satellite clock offsets, use the Kalman filtering method to perform real-time satellite clock offset estimation to determine the state estimation value and obtain GNSS real-time satellite clock offset products.

[0013] Optionally, based on the GNSS ultra-rapid predicted satellite clock offsets, use the Kalman filtering method to perform real-time satellite clock offset estimation to determine the state estimation value and obtain GNSS real-time satellite clock offset products, including: ; ; ; ; Wherein, is the inverse matrix of; is the square of the accuracy of GNSS ultra-rapid predicted satellite clock offsets; is the th epoch of GNSS ultra-rapid predicted satellite clock offsets; is the Kalman filter gain matrix; is the th epoch of the design matrix; the superscript is the transpose operation of the matrix; is the th epoch of the elevation angle-related weight matrix; is the th epoch of the state variance matrix; is the th epoch of the state estimation value; is the th epoch of the difference vector between the pseudorange / carrier phase observation value and the pseudorange / carrier phase calculated value.

[0014] In a second aspect, the present invention provides an apparatus for accelerating the convergence of GNSS real-time satellite clock offset estimation, the apparatus includes: A construction module, configured to construct an error equation corresponding to each GNSS satellite observed at each station; Combined module, configured to combine error equations corresponding to multiple GNSS satellites observed by multiple stations respectively, so as to obtain an error equation in matrix form; Constraint module, configured to introduce GNSS ultra-rapid predicted satellite clock offsets to constrain the process of real-time satellite clock offset estimation using the Kalman filtering method, so as to obtain GNSS real-time satellite clock offset products; some parameters in the Kalman filtering method are determined based on the error equation in matrix form.

[0015] In the method for accelerating the convergence of GNSS real-time satellite clock offset estimation provided by the present invention, GNSS ultra-rapid predicted satellite clock offsets are introduced to constrain the process of real-time satellite clock offset estimation using the Kalman filtering method. Since the accuracy of GNSS ultra-rapid predicted satellite clock offsets is relatively high, the convergence of GNSS real-time satellite clock offsets is accelerated, and GNSS real-time satellite clock offset products with high reliability are obtained.

[0016] The following will further elaborate on the present invention in conjunction with the accompanying drawings. Description of the Drawings

[0017] Figure 1 is a schematic flowchart of a method for accelerating the convergence of GNSS real-time satellite clock offset estimation provided by an embodiment of the present invention; Figure 2 is a schematic structural diagram of a device for accelerating the convergence of GNSS real-time satellite clock offset estimation provided by an embodiment of the present invention. Detailed Embodiments

[0018] The following further describes the present invention in detail with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0019] To solve the problem of slow convergence in existing GNSS real-time satellite clock offset estimation methods, an embodiment of the present invention provides a method for accelerating the convergence of GNSS real-time satellite clock offset estimation. Refer to Figure 1 , Figure 1 is a schematic flowchart of a method for accelerating the convergence of GNSS real-time satellite clock offset estimation provided by an embodiment of the present invention, which specifically includes the following steps: Step S101: Construct error equations corresponding to each GNSS satellite observed by each station.

[0020] In the embodiment of the present invention, for each GNSS satellite observed by each station, error equations corresponding to each GNSS satellite observed by each station can be constructed based on GNSS satellite observations.

[0021] In GNSS real-time satellite clock error estimation, the receiver needs to obtain GNSS satellite observations by observing GNSS satellite signals to solve parameters such as satellite clock error, receiver clock error, zenith tropospheric wet delay, and ambiguity. Since GNSS satellite observations contain various errors, such as antenna phase center deviation, relativistic effect, satellite clock error, receiver clock error, ionospheric delay, and tropospheric delay, error equations need to be constructed to model and estimate the errors.

[0022] In one implementation, error equations corresponding to each GNSS satellite observed at each station are constructed, including: For each GNSS satellite observed at each station, the dual-frequency ionosphere-free combination method is used to eliminate the influence of the first-order ionosphere to determine the error equations corresponding to each GNSS satellite observed at each station.

[0023] In the embodiment of the present invention, the error equations corresponding to each GNSS satellite observed at each station include the a posteriori residuals of the pseudorange observations and the a posteriori residuals of the carrier phase observations : ; ; wherein, and respectively represent the dual-frequency ionosphere-free combinations of GNSS pseudorange and carrier phase observations; is the receiver clock error of the receiver , in meters; is the satellite clock error of the GNSS satellite , in meters; is the mapping function; represents the zenith tropospheric wet delay, in meters; is the difference between the pseudorange observation value and the pseudorange calculated value, in meters, that is, "observation value minus calculated value" of the pseudorange; is the difference between the carrier phase observation value and the carrier phase calculated value, in meters, that is, "observation value minus calculated value" of the carrier phase; is the dual-frequency ionosphere-free wavelength, in meters; is the dual-frequency ionosphere-free ambiguity, in cycles.

[0024] Step S102, combining the error equations corresponding to multiple GNSS satellites observed at multiple stations respectively to obtain an error equation in matrix form.

[0025] Usually, when estimating the clock error, it is estimated based on the data observed at multiple stations within a preset range, and each station can observe multiple GNSS satellites. Therefore, an error equation in matrix form can be constructed based on the observations of multiple GNSS satellites at multiple stations.

[0026] In the embodiment of the present invention, by combining the error equations corresponding to multiple GNSS satellites observed by multiple stations, an error equation in matrix form can be obtained: ; wherein, is the a posteriori residual vector; is the design matrix; is the parameter vector to be estimated, including satellite clock error, receiver clock error, zenith tropospheric wet delay, and ambiguity parameter; is the difference vector between the pseudo-range / carrier phase observation value and the pseudo-range / carrier phase calculated value, that is, the "observation value minus the calculated value" vector.

[0027] In the embodiment of the present invention, a number in represents the a posteriori residual of a pseudo-range observation or the a posteriori residual of a carrier observation.

[0028] Step S103: Introduce the GNSS ultra-rapid predicted satellite clock error to constrain the process of real-time satellite clock error estimation using the Kalman filtering method, and obtain the GNSS real-time satellite clock error product; some parameters in the Kalman filtering method are determined based on the error equation in matrix form.

[0029] In the embodiment of the present invention, the GNSS ultra-rapid predicted satellite clock error, such as the prediction product updated hourly by the IGS (International Global Navigation Satellite System Service) analysis center, is used as a constraint to optimize the real-time clock error estimation.

[0030] In the embodiment of the present invention, the specific processing process of introducing the GNSS ultra-rapid predicted satellite clock error to constrain the process of real-time satellite clock error estimation using the Kalman filtering method is as follows: In real-time GNSS satellite clock error estimation, the Kalman filtering is a dynamic recursive method, and its core is to iteratively update the parameter estimation value through the state equation and the measurement equation.

[0031] First, set the initial values of the Kalman filter. Specifically, assume that the initial values of the state vector and the variance-covariance matrix can be respectively expressed as and Among them, the state vector refers to the state vector of the parameters to be estimated, including receiver clock error, satellite clock error, zenith tropospheric wet delay, and ambiguity parameters, etc. The variance-covariance matrix can include satellite clock error variance-covariance matrix, receiver clock error variance-covariance matrix, zenith tropospheric wet delay variance-covariance matrix, and ambiguity variance-covariance, etc.

[0032] In one implementation, a GNSS ultra-rapid predicted satellite clock error is introduced to constrain the process of real-time satellite clock error estimation using the Kalman filtering method, and a GNSS real-time satellite clock error product is obtained, including: Determine the GNSS ultra-rapid predicted satellite clock error; Based on the GNSS ultra-rapid predicted satellite clock error, use the Kalman filtering method to perform real-time satellite clock error estimation to determine the state estimate value, and obtain the GNSS real-time satellite clock error product.

[0033] In the embodiment of the present invention, the existing IGS analysis center begins to provide multi-system ultra-rapid satellite orbit and clock error products, and its update frequency is once per hour. The frequent update reduces the accuracy loss of the satellite clock error, which provides an opportunity for additional ultra-rapid predicted satellite clock error constraints on real-time GNSS satellite clock error estimation, thereby accelerating the convergence speed of GNSS real-time satellite clock error estimation.

[0034] In the embodiment of the present invention, some parameters in the Kalman filtering method are determined based on the error equation in matrix form, including: The design matrix in the Kalman filtering method and the difference vector between the observed value and the calculated value are both determined based on the error equation in matrix form. Among them, the difference vector between the observed value and the calculated value is the difference vector between the pseudorange / carrier phase observed value and the pseudorange / carrier phase calculated value in the error equation in matrix form.

[0035] In the embodiment of the present invention, based on the GNSS ultra-rapid predicted satellite clock error, use the Kalman filtering method to perform real-time satellite clock error estimation to determine the state estimate value, and obtain the GNSS real-time satellite clock error product, including: ; ; ; ; Among them, is the inverse matrix of; is the square of the accuracy of the GNSS ultra-rapid predicted satellite clock error; is the th epoch of the GNSS ultra-rapid predicted satellite clock error; is the Kalman filter gain matrix; is the design matrix of the th epoch; the superscript represents the transpose operation of the matrix; is the elevation angle-related weight matrix of the th epoch; is the state variance matrix of the th epoch; is the state estimated value of the th epoch; is the difference vector between the pseudorange / carrier phase observation and the pseudorange / carrier phase calculated value of the th epoch.

[0036] In the traditional GNSS real-time satellite clock error estimation model, the initial value of the satellite clock error comes from the previous epoch or the broadcast ephemeris, and its variance is set to a relatively large value and estimated as white noise. Therefore, the contribution of the initial value of the satellite clock error to the real-time satellite clock error estimated value is very small.

[0037] In the embodiments of the present invention, GNSS real-time satellite clock error estimation is constrained by using GNSS ultra-rapid predicted satellite clock errors. Among them, the satellite clock error in comes from the ultra-rapid predicted satellite clock error. Based on the accuracy of the GNSS ultra-rapid predicted satellite clock error,

[0038] is set to an appropriate value, such as the square of the accuracy of the GNSS ultra-rapid predicted satellite clock error. The predicted satellite clock error information is beneficial to strengthening the observation model, especially to improving the strength of the GNSS satellite clock error estimation model during the convergence period, thereby accelerating the convergence speed of the GNSS real-time satellite clock error estimation.

[0039] Based on the same inventive concept, the embodiments of the present invention also provide a device for accelerating the convergence of GNSS real-time satellite clock error estimation. Refer to Figure 2 Figure 2 is a schematic structural diagram of a device for accelerating the convergence of GNSS real-time satellite clock error estimation provided by the embodiments of the present invention. The device includes: A construction module 201, configured to construct an error equation corresponding to each GNSS satellite observed at each station; A joint module 202, configured to jointly combine the error equations corresponding to multiple GNSS satellites observed at multiple stations to obtain an error equation in matrix form; A constraint module 203 is used to introduce GNSS ultra-rapid predicted satellite clock errors, constrain the process of real-time satellite clock error estimation using the Kalman filtering method, and obtain GNSS real-time satellite clock error products. Some parameters in the Kalman filtering method are determined based on the error equation in matrix form.

[0040] In the embodiment of the present invention, GNSS ultra-rapid predicted satellite clock errors are introduced to constrain the process of real-time satellite clock error estimation using the Kalman filtering method. Since the accuracy of GNSS ultra-rapid predicted satellite clock errors is relatively high, the convergence of GNSS real-time satellite clock errors is accelerated, and high-reliability GNSS real-time satellite clock error products are obtained.

[0041] Optionally, the construction module 201 is specifically used for: For each GNSS satellite observed at each station, a dual-frequency ionosphere-free combination method is adopted to determine the error equation corresponding to each GNSS satellite observed at each station.

[0042] Optionally, the error equation corresponding to each GNSS satellite observed at each station includes: ; ; Wherein, is the a posteriori residual of the pseudorange observation value; is the a posteriori residual of the carrier phase observation value; is the receiver receiver clock error; is the satellite clock error of GNSS satellite ; is the mapping function; represents the zenith tropospheric wet delay; is the difference between the pseudorange observation value and the pseudorange calculated value; is the difference between the carrier phase observation value and the carrier phase calculated value; is the dual-frequency ionosphere-free wavelength; is the dual-frequency ionosphere-free ambiguity.

[0043] Optionally, the error equation in matrix form includes: ; Wherein, is the a posteriori residual vector; is the design matrix; is the parameter vector to be estimated, including: satellite clock error, receiver clock error, zenith tropospheric wet delay, and ambiguity parameter; is the difference vector between the pseudorange / carrier phase observation value and the pseudorange / carrier phase calculated value.

[0044] Optionally, some parameters in the Kalman filtering method are determined based on the error equation in matrix form, including: The design matrix in the Kalman filtering method, as well as the difference vector between the observed value and the calculated value, are both determined based on the error equation in matrix form.

[0045] Optionally, the constraint module 203 is specifically configured to: Determine the GNSS ultra-rapid predicted satellite clock offset; based on the GNSS ultra-rapid predicted satellite clock offset, use the Kalman filtering method to perform real-time satellite clock offset estimation to determine the state estimation value and obtain the GNSS real-time satellite clock offset product.

[0046] Optionally, the constraint module 203, based on the GNSS ultra-rapid predicted satellite clock offset, uses the Kalman filtering method to perform real-time satellite clock offset estimation to determine the state estimation value and obtain the GNSS real-time satellite clock offset product, including: ; ; ; ; Wherein, is the inverse matrix of; is the square of the accuracy of the GNSS ultra-rapid predicted satellite clock offset; is the th epoch of the GNSS ultra-rapid predicted satellite clock offset; is the Kalman filter gain matrix; is the th epoch of the design matrix; the superscript is the transpose operation of the matrix; is the th epoch of the elevation angle-related weight matrix; is the th epoch of the state variance matrix; is the th epoch of the state estimation value; is the th epoch of the difference vector between the pseudorange / carrier phase observation value and the pseudorange / carrier phase calculated value.

[0047] It should be noted that the terms "first", "second", etc. are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present invention.

[0048] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification.

[0049] Although the present invention has been described in connection with various embodiments herein, however, in the process of implementing the claimed present invention, those skilled in the art can understand and implement other variations of the disclosed embodiments by viewing the accompanying drawings and the disclosure. In the description of the present invention, the term "including" does not exclude other components or steps, the word "a" or "one" does not exclude a plurality of cases, and the meaning of "a plurality" is two or more unless otherwise specifically defined. In addition, certain measures are described in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0050] For the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, reference can be made to the partial description of the method embodiments.

[0051] It should be noted that the device of the embodiments of the present invention is a device applying the above method for accelerating the convergence of GNSS real-time satellite clock error estimation. Then all embodiments of the above method for accelerating the convergence of GNSS real-time satellite clock error estimation are applicable to this device and can achieve the same or similar beneficial effects.

[0052] The above content is a further detailed description of the present invention in combination with specific preferred embodiments. It cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention pertains, without departing from the concept of the present invention, several simple deductions or substitutions can still be made, and all should be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for accelerating the convergence of real-time GNSS satellite clock bias estimation, characterized in that, The method includes: Constructing error equations corresponding to each GNSS satellite observed at each station; Combining the error equations corresponding to multiple GNSS satellites observed at multiple stations respectively to obtain an error equation in matrix form; Introducing GNSS ultra-rapid predicted satellite clock offsets to constrain the process of real-time satellite clock offset estimation using the Kalman filtering method, and obtaining GNSS real-time satellite clock offset products; some parameters in the Kalman filtering method are determined based on the error equation in matrix form.

2. The method for accelerating the convergence of real-time GNSS satellite clock error estimation according to claim 1, characterized in that, Constructing error equations corresponding to each GNSS satellite observed at each station includes: For each GNSS satellite observed at each station, adopting a dual-frequency ionosphere-free combination method to determine the error equation corresponding to each GNSS satellite observed at each station.

3. The method for accelerating the convergence of real-time GNSS satellite clock error estimation according to claim 2, wherein The error equation corresponding to each GNSS satellite observed at each station includes: ； ； wherein, is the a posteriori residual of the pseudorange observation; is the a posteriori residual of the carrier phase observation; is the receiver clock error of the receiver; is the GNSS satellite clock error of the satellite; is the mapping function; represents the zenith tropospheric wet delay; is the difference between the pseudorange observation and the pseudorange calculated value; is the difference between the carrier phase observation and the carrier phase calculated value; is the dual-frequency ionosphere-free wavelength; is the dual-frequency ionosphere-free ambiguity.

4. The method for accelerating the convergence of real-time GNSS satellite clock error estimation according to claim 1, wherein The error equation in matrix form includes: ； Among them, is the post-fit residual vector; is the design matrix; is the parameter vector to be estimated, including: satellite clock error, receiver clock error, zenith tropospheric wet delay, and ambiguity parameter; is the difference vector between the pseudo-range / carrier phase observation value and the pseudo-range / carrier phase calculated value.

5. The method for accelerating the convergence of real-time GNSS satellite clock error estimation according to claim 1, characterized in that, Some parameters in the Kalman filtering method are determined based on the error equation in matrix form, including: The design matrix, the difference vector between the observed value and the calculated value in the Kalman filtering method are both determined based on the error equation in matrix form.

6. The method for accelerating the convergence of real-time GNSS satellite clock error estimation according to claim 1, wherein Introducing GNSS ultra-rapid predicted satellite clock offsets to constrain the process of real-time satellite clock offset estimation using the Kalman filtering method, and obtaining GNSS real-time satellite clock offset products includes: Determining GNSS ultra-rapid predicted satellite clock offsets; Based on the GNSS ultra-rapid predicted satellite clock offsets, using the Kalman filtering method for real-time satellite clock offset estimation to determine the state estimation value, and obtaining GNSS real-time satellite clock offset products.

7. The method for accelerating the convergence of real-time GNSS satellite clock error estimation according to claim 6, characterized in that, Based on the GNSS ultra-rapid predicted satellite clock offsets, using the Kalman filtering method for real-time satellite clock offset estimation to determine the state estimation value, and obtaining GNSS real-time satellite clock offset products includes: ； ； ； ； Among them, is the inverse matrix of; is the square of the GNSS ultra-rapid prediction satellite clock error accuracy; is the GNSS ultra-rapid prediction satellite clock error at the th epoch; is the Kalman filter gain matrix; is the design matrix at the th epoch; The superscript is the transpose operation of the matrix; is the elevation angle-related weight matrix at the th epoch; is the state variance matrix at the th epoch; is the state estimated value at the th epoch; is the difference vector between the pseudorange / carrier phase observation value and the pseudorange / carrier phase calculated value at the 8. A device for accelerating the convergence of GNSS real-time satellite clock error estimation, characterized in that, The device includes: A construction module for constructing error equations corresponding to each GNSS satellite observed at each station; A combination module for combining the error equations corresponding to multiple GNSS satellites observed at multiple stations respectively to obtain an error equation in matrix form; A constraint module for introducing GNSS ultra-rapid predicted satellite clock offsets to constrain the process of real-time satellite clock offset estimation using the Kalman filtering method, and obtaining GNSS real-time satellite clock offset products; some parameters in the Kalman filtering method are determined based on the error equation in matrix form.

9. The device for accelerating the convergence of real-time GNSS satellite clock error estimation according to claim 8, characterized in that, The construction module is specifically used for: For each GNSS satellite observed at each station, adopting a dual-frequency ionosphere-free combination method to determine the error equation corresponding to each GNSS satellite observed at each station.

10. The device for accelerating the convergence of real-time GNSS satellite clock error estimation according to claim 9, wherein, The error equation corresponding to each GNSS satellite observed at each station includes: ； ； Among them, is the a posteriori residual of the pseudorange observation value; is the a posteriori residual of the carrier phase observation value; is the receiver receiver clock error; is the GNSS satellite satellite clock error; is the mapping function; represents the zenith tropospheric wet delay; is the difference between the pseudorange observation value and the pseudorange calculated value; is the difference between the carrier phase observation value and the carrier phase calculated value; is the dual-frequency ionosphere-free wavelength; is the dual-frequency ionosphere-free ambiguity.

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