A method to accelerate the convergence of GNSS real-time satellite clock error estimation

Abstract

The present invention discloses a method for accelerating the convergence of GNSS real-time satellite clock error estimation, belonging to the field of satellite positioning and timing. The method comprises: constructing an error equation corresponding to each GNSS satellite of each measuring station; combining the error equations corresponding to multiple GNSS satellites of multiple measuring stations to obtain an error equation in matrix form; and introducing GNSS ultra-fast predicted satellite clock errors to constrain the process of real-time satellite clock error estimation using the Kalman filter method. Due to the high accuracy of the GNSS ultra-fast predicted satellite clock errors, the convergence of the GNSS real-time satellite clock error estimation is accelerated, and a highly reliable 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 in particular relates to a method for accelerating the convergence of GNSS real-time satellite clock error estimation. Background Art

[0002] Global Navigation Satellite System (GNSS) real-time precise point positioning technology has been widely applied in fields such as geodesy, atmospheric monitoring, geological disaster warning, time transfer, and precision agriculture. High-precision GNSS real-time satellite clock error products are a key prerequisite for achieving real-time precise point positioning. The atomic clocks on GNSS satellites vary extremely complexly, resulting in significant accuracy loss in long-term predictions. Therefore, real-time estimation is commonly used to obtain GNSS real-time satellite clock errors.

[0003] During the estimation of GNSS real-time satellite clock errors, it is affected by a variety of factors such as observation data interruption, communication equipment, and network delay, which may lead to interruptions in GNSS real-time satellite clock error estimation. When the observation data is restored, the clock error estimation program needs to be restarted, which involves estimating a large number of tropospheric and ambiguity parameters. In addition, due to the slow geometric changes of static ground stations relative to GNSS satellites, the convergence of GNSS real-time satellite clock error estimation is slow, which reduces the service quality of real-time precise single-point positioning during this period. Therefore, how to accelerate the convergence of GNSS real-time satellite clock error estimation and improve the GNSS satellite service performance during the convergence phase still faces great challenges.

[0004] Current GNSS real-time satellite clock error estimation methods use a white noise model to estimate the satellite clock error. Initial values ​​are derived from the broadcast ephemeris or the previous epoch, and a large variance is applied to the filter during clock error estimation. Because GNSS satellites in medium or high orbits travel relatively slowly, the geometric structure between the satellites and the ground station network changes slowly, requiring approximately half an hour to reach convergence.

[0005] Therefore, how to provide a method to accelerate the convergence of GNSS real-time satellite clock error 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 error estimation.

[0007] The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0008] In a first aspect, the present invention provides a method for accelerating the convergence of GNSS real-time satellite clock error estimation, the method comprising:

[0009] Construct the error equation corresponding to each GNSS satellite observed by each station;

[0010] Combine the error equations corresponding to multiple GNSS satellites observed by multiple stations to obtain the error equation in matrix form;

[0011] The GNSS ultra-fast prediction satellite clock error is introduced to constrain the process of real-time satellite clock error estimation using the Kalman filter method, thereby obtaining a GNSS real-time satellite clock error product; some parameters in the Kalman filter method are determined based on the error equation in matrix form.

[0012] Optionally, construct the error equation corresponding to each GNSS satellite observed by each station, including:

[0013] For each GNSS satellite observed by each station, a dual-frequency ionospheric-free combination method is used to determine the error equation corresponding to each GNSS satellite observed by each station.

[0014] Optionally, the error equation corresponding to each GNSS satellite observed by each station includes:

[0015] ;

[0016] ;

[0017] in, is the posterior residual of the pseudorange observation; is the posterior residual of the carrier phase observation; It is a receiver Receiver clock error; It is a GNSS satellite Satellite clock error; is the mapping function; represents the zenith tropospheric wet delay; is the difference between the observed pseudorange and the calculated pseudorange; is the difference between the observed and calculated carrier phase values; It is a dual-frequency ionospheric-free wavelength; It is dual-frequency ionospheric ambiguity-free.

[0018] Optionally, the error equation in matrix form includes:

[0019] ;

[0020] in, is the posterior 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 parameters; is the difference vector between the pseudorange / carrier phase observation and the pseudorange / carrier phase calculation.

[0021] Optionally, some parameters in the Kalman filter method are determined based on the error equation in matrix form, including:

[0022] The design matrix and the difference vector between the observed value and the calculated value in the Kalman filter method are both determined based on the error equation in matrix form.

[0023] Optionally, the GNSS ultra-fast predicted satellite clock error is introduced to constrain the process of real-time satellite clock error estimation using the Kalman filter method, and the GNSS real-time satellite clock error product is obtained, including:

[0024] Determine GNSS ultra-rapid predicted satellite clock errors;

[0025] Based on the GNSS ultra-fast predicted satellite clock error, the real-time satellite clock error is estimated using the Kalman filter method to determine the state estimation value and obtain the GNSS real-time satellite clock error product.

[0026] Optionally, based on the GNSS ultra-rapid predicted satellite clock error, a Kalman filter method is used to estimate the real-time satellite clock error to determine a state estimate value and obtain a GNSS real-time satellite clock error product, including:

[0027] ;

[0028] ;

[0029] ;

[0030] ;

[0031] in, yes The inverse matrix of is the square of the GNSS ultra-rapid prediction satellite clock error accuracy; It is GNSS ultra-rapid predicted satellite clock errors for epochs; is the Kalman filter gain matrix; It is The design matrix of epochs; superscript is the transpose operation of the matrix; It is The weight matrix associated with the altitude angle of each epoch; It is The state variance matrix of epochs; It is The estimated value of the state for each epoch; It is The difference vector between the pseudorange / carrier phase observation value and the pseudorange / carrier phase calculation value for each epoch.

[0032] In a second aspect, the present invention provides a device for accelerating the convergence of GNSS real-time satellite clock error estimation, the device comprising:

[0033] A construction module is used to construct the error equation corresponding to each GNSS satellite observed by each station;

[0034] The joint module is used to combine the error equations corresponding to multiple GNSS satellites observed by multiple stations to obtain the error equation in matrix form;

[0035] A constraint module is used to introduce GNSS ultra-fast predicted satellite clock errors to constrain the process of real-time satellite clock error estimation using the Kalman filter method to obtain GNSS real-time satellite clock error products; some parameters in the Kalman filter method are determined based on the error equation in matrix form.

[0036] The present invention provides a method for accelerating the convergence of GNSS real-time satellite clock error estimation, which introduces GNSS ultra-fast predicted satellite clock error to constrain the process of real-time satellite clock error estimation using the Kalman filter method. Since the accuracy of the GNSS ultra-fast predicted satellite clock error is relatively high, the convergence of the GNSS real-time satellite clock error is accelerated, and a highly reliable GNSS real-time satellite clock error product is obtained.

[0037] The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 1 is a flow chart of a method for accelerating the convergence of GNSS real-time satellite clock error estimation provided by an embodiment of the present invention;

[0039] Figure 2 This is a structural diagram of a device for accelerating the convergence of GNSS real-time satellite clock error estimation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The present invention will be further described in detail below with reference to specific examples, but the embodiments of the present invention are not limited thereto.

[0041] In order to solve the problem of slow convergence speed in the existing GNSS real-time satellite clock error estimation method, the embodiment of the present invention provides a method for accelerating the convergence of GNSS real-time satellite clock error estimation. Figure 1 , Figure 1This is a flow chart of a method for accelerating the convergence of GNSS real-time satellite clock error estimation provided by an embodiment of the present invention, which specifically includes the following steps:

[0042] Step S101: construct an error equation corresponding to each GNSS satellite observed by each measuring station.

[0043] In the embodiment of the present invention, for each GNSS satellite observed by each measuring station, an error equation corresponding to each GNSS satellite observed by each measuring station may be constructed based on the GNSS satellite observation value.

[0044] In real-time GNSS satellite clock error estimation, the receiver must observe GNSS satellite signals to obtain GNSS satellite observations to resolve parameters such as satellite clock error, receiver clock error, zenith tropospheric wet delay, and ambiguity. Because GNSS satellite observations contain multiple errors, such as antenna phase center deviation, relativistic effects, satellite clock error, receiver clock error, ionospheric delay, and tropospheric delay, error equations must be constructed to model and estimate these errors.

[0045] In one implementation, the error equation corresponding to each GNSS satellite observed by each station is constructed, including:

[0046] For each GNSS satellite observed by each station, a dual-frequency ionospheric-free combination method is used to eliminate the influence of the first-order ionosphere to determine the error equation corresponding to each GNSS satellite observed by each station.

[0047] In the embodiment of the present invention, the error equation corresponding to each GNSS satellite observed by each station includes the posterior residual of the pseudorange observation value and the posterior residual of the carrier phase observation :

[0048] ;

[0049] ;

[0050] in, and They represent the dual-frequency ionospheric-free combination of GNSS pseudorange and carrier phase observations; It is a receiver The receiver clock error is in meters; It is a GNSS satellite The satellite clock error is in meters; is the mapping function; represents the zenith tropospheric wet delay in meters; It is the difference between the observed pseudorange and the calculated pseudorange, in meters, that is, the "observed pseudorange minus the calculated pseudorange"; It is the difference between the observed carrier phase and the calculated carrier phase, in meters, that is, the "observed value minus the calculated value" of the carrier phase; is the dual-frequency ionospheric-free wavelength in meters; is the dual-frequency ionospheric-free ambiguity in weeks.

[0051] Step S102 : Combining the error equations corresponding to multiple GNSS satellites observed by multiple stations to obtain an error equation in matrix form.

[0052] Typically, clock errors are estimated based on data from multiple stations within a predefined range. Each station can observe multiple GNSS satellites. Therefore, a matrix-based error equation can be constructed based on the observations of multiple GNSS satellites from multiple stations.

[0053] In an 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:

[0054] ;

[0055] in, is the posterior 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 parameters; It is the difference vector between the pseudorange / carrier phase observation value and the pseudorange / carrier phase calculation value, that is, the "observation value minus calculation value" vector.

[0056] In an embodiment of the present invention, A number in represents the posterior residual of a pseudorange observation or the posterior residual of the carrier observation The pseudorange / carrier phase calculation value is obtained by calculating the distance between the GNSS satellite orbit and the ground station, and the coordinates of the two points are known.

[0057] Step S103, introduce the GNSS ultra-fast predicted satellite clock error to constrain the process of real-time satellite clock error estimation using the Kalman filter method, and obtain the GNSS real-time satellite clock error product; some parameters in the Kalman filter method are determined based on the error equation in matrix form.

[0058] In an embodiment of the present invention, the GNSS ultra-fast prediction of satellite clock errors, such as the hourly updated forecast products of the IGS (International Global Navigation Satellite System Service) Analysis Center, is used as a constraint to optimize the real-time clock error estimation.

[0059] In the embodiment of the present invention, the specific process of introducing the GNSS ultra-fast prediction of satellite clock error and constraining the process of real-time satellite clock error estimation using the Kalman filter method is as follows:

[0060] In real-time GNSS satellite clock error estimation, Kalman filtering is a dynamic recursive method whose core is to iteratively update parameter estimates through state equations and measurement equations.

[0061] First, set the initial value of the Kalman filter. Specifically, assume that the initial value of the state vector and the variance-covariance matrix can be expressed as and 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. The variance-covariance matrix can include the satellite clock error variance-covariance matrix, the receiver clock error variance-covariance matrix, the zenith tropospheric wet delay variance-covariance matrix, and the ambiguity variance-covariance matrix.

[0062] In one implementation, ultra-fast GNSS satellite clock error prediction is introduced to constrain the process of real-time satellite clock error estimation using the Kalman filter method, thereby obtaining a GNSS real-time satellite clock error product, including:

[0063] Determine GNSS ultra-rapid predicted satellite clock errors;

[0064] Based on the GNSS ultra-fast prediction of satellite clock error, the real-time satellite clock error is estimated using the Kalman filter method to determine the state estimation value and obtain the GNSS real-time satellite clock error product.

[0065] In an embodiment of the present invention, the existing IGS analysis center begins to provide multi-system ultra-fast satellite orbit and clock products, which are updated once an hour. Frequent updates reduce the accuracy loss of satellite clock errors, which provides an opportunity to add ultra-fast predicted satellite clock errors to constrain real-time GNSS satellite clock error estimation, thereby accelerating the convergence speed of GNSS real-time satellite clock error estimation.

[0066] In an embodiment of the present invention, some parameters in the Kalman filter method are determined based on an error equation in matrix form, including:

[0067] The design matrix and the difference vector between the observed and calculated values ​​in the Kalman filter method are determined based on the matrix error equation. The difference vector between the observed and calculated values ​​is the difference vector between the pseudorange / carrier phase observations and the calculated pseudorange / carrier phase values ​​in the matrix error equation.

[0068] In an embodiment of the present invention, based on the GNSS ultra-rapid predicted satellite clock error, a Kalman filter method is used to estimate the real-time satellite clock error to determine the state estimate value and obtain the GNSS real-time satellite clock error product, including:

[0069] ;

[0070] ;

[0071] ;

[0072] ;

[0073] in, yes The inverse matrix of is the square of the GNSS ultra-rapid prediction satellite clock error accuracy; It is GNSS ultra-rapid predicted satellite clock errors for epochs; is the Kalman filter gain matrix; It is The design matrix of epochs; superscript is the transpose operation of the matrix; It is The weight matrix associated with the altitude angle of each epoch; It is The state variance matrix of epochs; It is The estimated value of the state for each epoch; It is The difference vector between the pseudorange / carrier phase observation value and the pseudorange / carrier phase calculation value for each epoch.

[0074] In the traditional GNSS real-time satellite clock error estimation model, the initial satellite clock error comes from the previous epoch or broadcast ephemeris, and its variance is set to a large value and estimated as white noise. Therefore, the initial satellite clock error contributes very little to the real-time satellite clock error estimate.

[0075] In the embodiment of the present invention, the GNSS real-time satellite clock error estimation is performed using the GNSS ultra-rapid prediction satellite clock error constraint. The satellite clock error in is derived from the ultra-rapid predicted satellite clock error. Based on the accuracy of the GNSS ultra-rapid predicted satellite clock error, Set to an appropriate value, such as the square of the GNSS ultra-rapid predicted satellite clock error accuracy. Predicted satellite clock error information helps strengthen the observation model, especially the GNSS satellite clock error estimation model during convergence, thereby accelerating the convergence of the GNSS real-time satellite clock error estimation.

[0076] In an embodiment of the present invention, GNSS ultra-fast predicted satellite clock errors are introduced to constrain the process of real-time satellite clock error estimation using the Kalman filter method. Since the accuracy of GNSS ultra-fast predicted satellite clock errors is relatively high, the convergence of GNSS real-time satellite clock error estimation is accelerated, and a highly reliable GNSS real-time satellite clock error product is obtained.

[0077] Based on the same inventive concept, an embodiment of the present invention further provides a device for accelerating the convergence of GNSS real-time satellite clock error estimation, see Figure 2 , Figure 2 1 is a schematic structural diagram of a device for accelerating the convergence of GNSS real-time satellite clock error estimation provided by an embodiment of the present invention, the device comprising:

[0078] A construction module 201 is used to construct an error equation corresponding to each GNSS satellite observed by each measuring station;

[0079] A combining module 202 is configured to combine the error equations corresponding to multiple GNSS satellites observed by multiple stations to obtain an error equation in matrix form;

[0080] The constraint module 203 is used to introduce the GNSS ultra-fast predicted satellite clock error to constrain the process of estimating the real-time satellite clock error using the Kalman filter method to obtain the GNSS real-time satellite clock error product; some parameters in the Kalman filter method are determined based on the error equation in matrix form.

[0081] In an embodiment of the present invention, GNSS ultra-fast predicted satellite clock error is introduced to constrain the process of estimating real-time satellite clock error using the Kalman filter method. Since the GNSS ultra-fast predicted satellite clock error has high accuracy, the convergence of the GNSS real-time satellite clock error is accelerated, and a highly reliable GNSS real-time satellite clock error product is obtained.

[0082] Optionally, the construction module 201 is specifically configured to:

[0083] For each GNSS satellite observed by each station, a dual-frequency ionospheric-free combination method is used to determine the error equation corresponding to each GNSS satellite observed by each station.

[0084] Optionally, the error equation corresponding to each GNSS satellite observed by each station includes:

[0085] ;

[0086] ;

[0087] in, is the posterior residual of the pseudorange observation; is the posterior residual of the carrier phase observation; It is a receiver Receiver clock error; It is a GNSS satellite Satellite clock error; is the mapping function; represents the zenith tropospheric wet delay; is the difference between the observed pseudorange and the calculated pseudorange; is the difference between the observed and calculated carrier phase values; It is a dual-frequency ionospheric-free wavelength; It is dual-frequency ionospheric ambiguity-free.

[0088] Optional error equation in matrix form, including:

[0089] ;

[0090] in, is the posterior 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 parameters; is the difference vector between the pseudorange / carrier phase observation and the pseudorange / carrier phase calculation.

[0091] Optionally, some parameters in the Kalman filter method are determined based on the error equation in matrix form, including:

[0092] The design matrix and the difference vector between the observed and calculated values ​​in the Kalman filter method are determined based on the error equation in matrix form.

[0093] Optionally, the constraint module 203 is specifically configured to:

[0094] Determine the GNSS ultra-rapid predicted satellite clock error; based on the GNSS ultra-rapid predicted satellite clock error, use the Kalman filter method to estimate the real-time satellite clock error to determine the state estimate value and obtain the GNSS real-time satellite clock error product.

[0095] Optionally, the constraint module 203 uses a Kalman filter method to estimate the real-time satellite clock error based on the GNSS ultra-rapid predicted satellite clock error to determine the state estimate value and obtain the GNSS real-time satellite clock error product, including:

[0096] ;

[0097] ;

[0098] ;

[0099] ;

[0100] in, yes The inverse matrix of is the square of the GNSS ultra-rapid prediction satellite clock error accuracy; It is GNSS ultra-rapid predicted satellite clock errors for epochs; is the Kalman filter gain matrix; It is The design matrix of epochs; superscript is the transpose operation of the matrix; It is The weight matrix associated with the altitude angle of each epoch; It is The state variance matrix of epochs; It is The estimated value of the state for each epoch; It is The difference vector between the pseudorange / carrier phase observation value and the pseudorange / carrier phase calculation value for each epoch.

[0101] It should be noted that the terms "first," "second," and the like are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in sequences 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. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention.

[0102] In the description of this specification, the reference terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" mean that the specific features or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations 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 any suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification.

[0103] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art can understand and implement other variations of the disclosed embodiments by viewing the drawings and the disclosed content. In the description of the present invention, the word "comprising" does not exclude other components or steps, "one" or "a" does not exclude multiple situations, and "multiple" means two or more, unless otherwise clearly and specifically defined. In addition, certain measures are recorded in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0104] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0105] It should be noted that the device of an embodiment of the present invention is a device that applies the above-mentioned method for accelerating the convergence of GNSS real-time satellite clock error estimation. All embodiments of the above-mentioned method for accelerating the convergence of GNSS real-time satellite clock error estimation are applicable to the device and can achieve the same or similar beneficial effects.

[0106] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should be considered to fall within the scope of protection of the present invention.

Claims

1. A method for accelerating the convergence of GNSS real-time satellite clock error estimation, characterized in that: The method comprises: Construct the error equation corresponding to each GNSS satellite observed by each station; Combine the error equations corresponding to multiple GNSS satellites observed by multiple stations to obtain the error equation in matrix form; The GNSS ultra-rapid satellite clock error prediction is introduced to constrain the process of real-time satellite clock error estimation using the Kalman filter method, thereby obtaining a GNSS real-time satellite clock error product; some parameters in the Kalman filter method are determined based on the matrix-form error equation; The introduction of the GNSS ultra-fast predicted satellite clock error constrains the process of estimating the real-time satellite clock error using the Kalman filter method to obtain the GNSS real-time satellite clock error product, including: Determine GNSS ultra-rapid predicted satellite clock errors; Based on the GNSS ultra-rapid predicted satellite clock error, a real-time satellite clock error estimation is performed using a Kalman filter method to determine a state estimate value and obtain a GNSS real-time satellite clock error product; The method of estimating the real-time satellite clock error based on the GNSS ultra-rapid predicted satellite clock error using the Kalman filter method to determine the state estimation value and obtain the GNSS real-time satellite clock error product includes: ； ； ； ； in, yes The inverse matrix of is the square of the GNSS ultra-rapid prediction satellite clock error accuracy; It is GNSS ultra-rapid predicted satellite clock errors for epochs; is the Kalman filter gain matrix; It is The design matrix of epochs; superscript is the transpose operation of the matrix; It is The weight matrix associated with the altitude angle of each epoch; It is The state variance matrix of epochs; It is The estimated value of the state for each epoch; It is The difference vector between the pseudorange / carrier phase observation value and the pseudorange / carrier phase calculation value for each epoch.

2. The method for accelerating the convergence of GNSS real-time satellite clock error estimation according to claim 1, characterized in that: Construct the error equation corresponding to each GNSS satellite observed by each station, including: For each GNSS satellite observed by each station, a dual-frequency ionospheric-free combination method is used to determine the error equation corresponding to each GNSS satellite observed by each station.

3. The method for accelerating the convergence of GNSS real-time satellite clock error estimation according to claim 2, characterized in that: The error equation corresponding to each GNSS satellite observed by each station includes: ； ； in, is the posterior residual of the pseudorange observation; is the posterior residual of the carrier phase observation; It is a receiver Receiver clock error; It is a GNSS satellite Satellite clock error; is the mapping function; represents the zenith tropospheric wet delay; is the difference between the observed pseudorange and the calculated pseudorange; is the difference between the observed and calculated carrier phase values; It is a dual-frequency ionospheric-free wavelength; It is dual-frequency ionospheric ambiguity-free.

4. The method for accelerating the convergence of GNSS real-time satellite clock error estimation according to claim 1, characterized in that: The error equation in matrix form includes: ； in, is the posterior 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 parameters; is the difference vector between the pseudorange / carrier phase observation and the pseudorange / carrier phase calculation.

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

6. A device for accelerating the convergence of GNSS real-time satellite clock error estimation, characterized in that: The device comprises: A construction module is used to construct the error equation corresponding to each GNSS satellite observed by each station; The joint module is used to combine the error equations corresponding to multiple GNSS satellites observed by multiple stations to obtain the error equation in matrix form; A constraint module is used to introduce GNSS ultra-rapid predicted satellite clock errors to constrain the process of estimating real-time satellite clock errors using the Kalman filter method, thereby obtaining a GNSS real-time satellite clock error product; some parameters in the Kalman filter method are determined based on the error equation in matrix form; The constraint module is specifically used to: Determine GNSS ultra-rapid predicted satellite clock errors; Based on the GNSS ultra-rapid predicted satellite clock error, a real-time satellite clock error estimation is performed using a Kalman filter method to determine a state estimate value and obtain a GNSS real-time satellite clock error product; The constraint module uses the Kalman filter method to estimate the real-time satellite clock error based on the GNSS ultra-rapid predicted satellite clock error to determine the state estimate value and obtain the GNSS real-time satellite clock error product, including: ； ； ； ； in, yes The inverse matrix of is the square of the GNSS ultra-rapid prediction satellite clock error accuracy; It is GNSS ultra-rapid predicted satellite clock errors for epochs; is the Kalman filter gain matrix; It is The design matrix of epochs; superscript is the transpose operation of the matrix; It is The weight matrix associated with the altitude angle of each epoch; It is The state variance matrix of epochs; It is The estimated value of the state for each epoch; It is The difference vector between the pseudorange / carrier phase observation value and the pseudorange / carrier phase calculation value for each epoch.

7. The device for accelerating the convergence of GNSS real-time satellite clock error estimation according to claim 6, characterized in that: The building blocks are specifically used for: For each GNSS satellite observed by each station, a dual-frequency ionospheric-free combination method is used to determine the error equation corresponding to each GNSS satellite observed by each station.

8. The device for accelerating the convergence of GNSS real-time satellite clock error estimation according to claim 7, characterized in that: The error equation corresponding to each GNSS satellite observed by each station includes: ； ； in, is the posterior residual of the pseudorange observation; is the posterior residual of the carrier phase observation; It is a receiver Receiver clock error; It is a GNSS satellite Satellite clock error; is the mapping function; represents the zenith tropospheric wet delay; is the difference between the observed pseudorange and the calculated pseudorange; is the difference between the observed and calculated carrier phase values; It is a dual-frequency ionospheric-free wavelength; It is dual-frequency ionospheric ambiguity-free.

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