A method for estimating constant bias of compass b2b of whole network reference unification

Abstract

The application discloses a Beidou B2b constant bias estimation method with unified network reference, comprising the following steps: firstly, the Beidou B2b clock difference reference transformation is carried out by applying the Beidou B2b service DCB information, and the constant bias parameterization separation is realized; secondly, the single-station virtual reference is added, and a constant bias full-rank estimation model based on a single station is constructed; then, the constant bias information of the whole network is integrated, the reference difference of multiple single-station solutions is eliminated, and the B2b constant bias estimation with unified network reference is realized; finally, under the constraint of the constant bias with unified reference, the performance such as the terminal positioning convergence speed and precision is improved. The application realizes the constant bias estimation based on the whole network, improves the stability and availability of the Beidou B2b constant bias estimation, avoids the jump of the estimated parameter and even the filter divergence problem caused by the constant bias with different references of the user end, and further guarantees the rapid convergence and instantaneous horizontal decimeter-level positioning of the terminal user.

Description

Technical Field

[0001] This invention belongs to the field of GNSS (Global Navigation Satellite System) positioning and navigation technology, and relates to BeiDou B2b service real-time precise point positioning (PPP), specifically to a BeiDou B2b constant deviation estimation method with unified network reference. Background Technology

[0002] Precise point positioning (PPP) can achieve centimeter-level positioning globally and is an important technology for providing high-precision positioning, navigation, and timing (PNT) services. Currently, real-time PPP mainly broadcasts SSR correction data in real time based on the RTCM protocol; however, it requires stable terrestrial network support, limiting its application in weak communication scenarios. To reduce reliance on terrestrial networks and further expand PPP applications in desert, ocean, and other scenarios, satellite-based augmentation models are gaining popularity, such as the BeiDou system's B2b augmentation service, which provides free, high-precision location services to users in China and surrounding areas.

[0003] While the PPP-B2b service currently possesses the capability to provide high-precision positioning and other services in the Asia-Pacific region, several issues remain to be addressed. Because PPP-B2b products are generated using only local reference stations, the PPP-B2b clock corrections exhibit a constant satellite constant bias. This bias is non-negligible and degrades the quality of pseudorange observations, thus affecting PPP convergence time and other performance aspects. Although this constant bias can be extracted using a single station, inconsistencies in reference standards may exist between different stations.

[0004] To obtain a consistent constant deviation for the entire BeiDou B2b network, this invention proposes a method for estimating the constant deviation of the entire network based on a unified benchmark, and constructs a multi-frequency PPP model with additional constant deviation constraints. Based on this model, single-epoch positioning of terminals is carried out. Summary of the Invention

[0005] To address the aforementioned issues, this invention discloses a BeiDou B2b constant deviation estimation method with unified network-wide benchmarks. Through a strategy of single-station estimation and network-wide unification, the constant deviations with consistent network-wide benchmarks are extracted.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] A method for estimating the constant deviation of BeiDou B2b with unified network benchmarks includes the following steps:

[0008] (1) By performing a reference transformation on the clock error correction of BeiDou B2b service, the constant deviation parameter is separated;

[0009] (2) By adding a virtual benchmark, a full-rank estimation model of B2b constant deviation based on a single station is constructed;

[0010] (3) Eliminate the benchmark differences of multiple single-station solutions and perform B2b constant deviation estimation for network benchmark unification;

[0011] (4) Rapid PPP positioning based on the deviation constraint of unified reference constant.

[0012] Furthermore, in step (1), the process of separating the constant deviation parameter by performing a reference transformation on the clock error correction of the BeiDou B2b service includes:

[0013] The station receives real-time corrections for orbit and clock bias from the BeiDou B2b service. The clock bias correction is strongly correlated with satellite hardware latency; therefore, the clock bias itself contains a reference related to satellite hardware latency. This reference for the clock bias is frequency-dependent. Typically, the clock bias reference is based on a specific dual-frequency non-ionospheric combination (the L1 / L2 frequencies of the GPS system and the B2I / B6I frequencies of BeiDou), which can be expressed as follows:

[0014]

[0015] In the formula, the superscript 's' represents different satellites, and 'c' is the speed of light in a vacuum. For satellite clock bias based on dual-frequency ionospheric combination, t s This represents the actual satellite clock bias; The hardware delay for the BeiDou B2I / B6I dual-frequency ionospheric-free combination is specifically in the following form:

[0016]

[0017] In the formula, and For the hardware delay of the BeiDou B2I and B6I frequencies, α and β are the ionosphere-free combination coefficients, satisfying the following relationship.

[0018]

[0019] In the formula, f2 and f6 are the frequencies of the B2I and B6I frequency points of the BDS system.

[0020] However, compared to the clock biases mentioned above, the clock bias benchmark for BeiDou B2b service differs in two aspects: first, the benchmark for B2b clock bias corrections is the B6I single-frequency point, rather than the B2I / B6I dual-frequency ionospheric combination; second, the B2b clock bias corrections also introduce satellite-related constant biases, which can be specifically expressed as follows:

[0021]

[0022] In the formula, For satellite clock bias based on B2b services, g s This represents the clock error constant deviation.

[0023] To address the issue of inconsistent clock references in B2b services, the differential code bias (DCB) information broadcast using Type 3 in B2b services can be used to convert the clock reference.

[0024]

[0025] In the formula, It is the B2b clock bias after converting the reference to the B2I / B6I ionosphere-free combination, D 26 The DCB between the B2I and B6I frequency points in the Type 3 type used can be represented as follows:

[0026] Equation (5) shows that after the benchmark conversion, the clock skew of B2b services... Compared with conventional clock difference There is still a constant deviation g s .

[0027] Furthermore, in step (2), the process of constructing a full-rank estimation model of the B2b constant deviation based on a single station by adding a virtual benchmark includes:

[0028] For reference stations with precisely known coordinates, after correcting the orbit and clock errors using PPP-B2b services, an ionospheric-free combined equation based on BeiDou B2I / B6I dual-frequency observation data is constructed.

[0029]

[0030] In the formula, T r For the zenith tropospheric wet delay, For the corresponding projection function, For receiver clock bias, and λ IF These represent the ambiguity and wavelength of the non-ionospheric combination, respectively.

[0031] Substituting equation (5) into equation (6), we can obtain a single-station estimation model that includes constant bias.

[0032]

[0033] In the model described in formula (7), the constant deviation g s and receiver clock difference A strong correlation also exists; therefore, a virtual solution benchmark needs to be selected to separate the two. In this invention, the satellite with the highest initial epoch elevation angle is selected as the benchmark to construct a single-station full-rank constant deviation estimation model.

[0034]

[0035] In the formula, ref represents the reference satellite, and g ref and These represent the constraint values ​​and constraint variances of the solution baseline, respectively.

[0036] It should be noted that for continuous observation arcs, the constant deviation can be used as a constant estimate. Therefore, only the strong constraint shown in Equation (8) is added to the initial epoch, and the reference for subsequent epochs is transmitted through Kalman filtering.

[0037] Furthermore, in step (3), the process of eliminating the benchmark differences among multiple single-station solutions and performing B2b constant deviation estimation for network-wide benchmark unification includes:

[0038] In a reference network, satellite visibility varies across different stations, potentially leading to inconsistencies in the constant deviations obtained from different reference stations. To improve the consistency of the network-wide constant deviation estimates, a least squares method is used to unify the constant deviation benchmark for the entire network. For a given reference station r and satellite s, the constant deviation of the single-station solution benchmark is considered. This can be further refined as follows:

[0039]

[0040] In the formula, The constant deviation benchmark for station r, This represents the constant deviation for ensuring consistency with the overall network benchmark.

[0041] Suppose a network consists of n reference stations, each of which observes k... i With (i = 1, 2, ..., n) satellites, and the network observing a total of k different satellites, the following observation model can be constructed based on multiple single-station solutions.

[0042]

[0043] In the formula, L i This represents the single-station solution of the constant deviation for the i-th (i = 1, 2, ..., n) reference station; X1 and X2 represent the constant deviation reference for n stations and the constant deviation for k satellites, respectively; A i and B i These are the design matrices for the i-th reference station, with corresponding dimensions k. i ×n and k i×k dimensions, where A i The element in the i-th column of the matrix is ​​1, and all other elements are 0. i In each row of the matrix, all elements are 0 except for the element corresponding to the satellite index, which has a value of 1. i The specific forms of X1 and X2 are as follows:

[0044]

[0045] The least squares model of the above formula (10) has a rank deficiency of 1. Therefore, an additional benchmark needs to be introduced to separate the station constant deviation benchmark and the satellite constant deviation estimate. For a certain epoch, the satellite that has been tracked the most times is selected as the benchmark, and the following pseudo-observation equation is added.

[0046]

[0047] In the formula, j represents the reference satellite; v is the virtual constraint value corresponding to the constant deviation.

[0048] It is important to note that the reference star selected may change at different epochs. To ensure the continuity of the constant bias, the virtual observation value of the reference star can be any value for the first epoch; while for subsequent epochs, taking advantage of the time-domain stability of the constant bias, the value of the reference star in the previous epoch can be selected as a constraint. In addition, to improve the stability of the overall network constant bias, multiple iterations can be performed to remove outliers with large post-hoc residuals.

[0049] Furthermore, in step (4), the PPP rapid positioning process based on the unified reference constant deviation constraint includes:

[0050] Using the constant deviation of the whole network benchmark extracted in step 3, a multi-frequency PPP positioning model constrained by constant deviation is constructed based on the original observation values ​​of the base frequency.

[0051] First, the multi-frequency PPP model based on BeiDou B2b services is as follows:

[0052]

[0053] In the formula, and These are the fundamental frequency pseudorange and carrier observations, respectively. To correct for hardware deviations at the satellite end, For the distance between the stars, For ionospheric delay, γ i ψ is the corresponding ionospheric amplification factor. r,i Due to hardware deviation at the receiver end, and λ i These are the fundamental frequency ambiguity and the corresponding wavelength, respectively.

[0054] Further additional constant deviation constraints are as follows.

[0055]

[0056] In the formula, and These represent the constant deviation constraint value and the constraint equation, respectively. Considering that the precision of the constant deviation is approximately on the order of nanoseconds, and to avoid the influence of excessively small variance on other parameters, this invention adopts a relatively loose constraint strength, with a variance of 0.3. 2 m 2 .

[0057] Based on the observation model constructed by formulas (13) and (14), under the constant deviation constraint of the whole network benchmark consistency, the advantages of Beidou multi-frequency signals are brought into play, and rapid positioning can be achieved by fixing the multi-frequency step ambiguity.

[0058] The beneficial effects of this invention include:

[0059] This invention proposes a BeiDou B2b constant deviation estimation method with a unified network reference. Building upon single-station constant deviation estimation, it leverages the advantages of the reference station network to eliminate the problem of inconsistent constant deviation references between single-station solutions. This avoids potential parameter jumps and even filter divergence issues that may occur when user terminals use constant deviations from different references. The proposed method improves the stability and usability of BeiDou B2b constant deviation estimation. Under the constraint of a unified reference constant deviation, users can achieve rapid convergence and instantaneous horizontal decimeter-level positioning, enhancing the convergence and positioning performance of PPP. Attached Figure Description

[0060] Figure 1 This is a flowchart illustrating the implementation of the method described in this invention;

[0061] Figure 2 This is a comparison of the effects of the single-station solution with constant deviation and the whole-network solution of this invention;

[0062] Figure 3 This is a comparison of the convergence performance of the constant deviation constraint PPP of this invention and the conventional PPP;

[0063] Figure 4 This is a comparison of the instantaneous PPP horizontal positioning accuracy before and after the constant deviation constraint of the present invention. Detailed Implementation

[0064] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0065] like Figure 1As shown in the figure, this embodiment discloses a method for estimating the constant deviation of BeiDou B2b with unified network benchmarks. The specific steps are as follows:

[0066] Step (1): Separate the constant deviation parameter by performing a reference transformation on the clock bias correction of BeiDou B2b service.

[0067] The station receives real-time corrections for orbit and clock bias from the BeiDou B2b service. The clock bias correction is strongly correlated with satellite hardware latency; therefore, the clock bias itself contains a reference related to satellite hardware latency. This reference for the clock bias is frequency-dependent. Typically, the clock bias reference is based on a specific dual-frequency non-ionospheric combination (the L1 / L2 frequencies of the GPS system and the B2I / B6I frequencies of BeiDou), which can be expressed as follows:

[0068]

[0069] In the formula, the superscript 's' represents different satellites, and 'c' is the speed of light in a vacuum. For satellite clock bias based on dual-frequency ionospheric combination, t s This represents the actual satellite clock bias; The hardware delay for the BeiDou B2I / B6I dual-frequency ionospheric-free combination is specifically in the following form:

[0070]

[0071] In the formula, and For the hardware delay of the BeiDou B2I and B6I frequencies, α and β are the ionosphere-free combination coefficients, satisfying the following relationship.

[0072]

[0073] In the formula, f2 and f6 are the frequencies of the B2I and B6I frequency points of the BDS system.

[0074] However, compared to the clock biases mentioned above, the clock bias benchmark for BeiDou B2b service differs in two aspects: first, the benchmark for B2b clock bias corrections is the B6I single-frequency point, rather than the B2I / B6I dual-frequency ionospheric combination; second, the B2b clock bias corrections also introduce satellite-related constant biases, which can be specifically expressed as follows:

[0075]

[0076] In the formula, For satellite clock bias based on B2b services, g s This represents the clock error constant deviation.

[0077] To address the issue of inconsistent clock references in B2b services, the DCB information broadcast using Type 3 in the B2b service can be used to convert the clock reference.

[0078]

[0079] In the formula, It is the B2b clock bias after converting the reference to the B2I / B6I ionosphere-free combination, D 26 The DCB between the B2I and B6I frequency points in the Type 3 type used can be represented as follows:

[0080] Equation (5) shows that after the benchmark conversion, the clock skew of B2b services... Compared with conventional clock difference There is still a constant deviation g s .

[0081] Step (2): Add a virtual benchmark and construct a full-rank estimation model of B2b constant deviation based on a single station.

[0082] For reference stations with precisely known coordinates, after correcting the orbit and clock errors using PPP-B2b services, an ionospheric-free combined equation based on BeiDou B2I / B6I dual-frequency observation data is constructed.

[0083]

[0084] In the formula, T r For the zenith tropospheric wet delay, For the corresponding projection function, For receiver clock bias, and λ IF These represent the ambiguity and wavelength of the non-ionospheric combination, respectively.

[0085] Substituting equation (5) into equation (6), we can obtain a single-station estimation model that includes constant bias.

[0086]

[0087] In the model described in formula (7), the constant deviation g s and receiver clock difference A strong correlation also exists; therefore, a virtual solution benchmark needs to be selected to separate the two. In this invention, the satellite with the highest initial epoch elevation angle is selected as the benchmark to construct a single-station full-rank constant deviation estimation model.

[0088]

[0089] In the formula, ref represents the reference satellite, and g ref and These represent the constraint values ​​and constraint variances of the solution baseline, respectively.

[0090] It should be noted that for continuous observation arcs, the constant deviation can be used as a constant estimate. Therefore, only the strong constraint shown in Equation (8) is added to the initial epoch, and the reference for subsequent epochs is transmitted through Kalman filtering.

[0091] Step (3): Eliminate the benchmark differences among multiple single-station solutions and perform B2b constant deviation estimation for network-wide benchmark unification.

[0092] In a reference network, satellite visibility varies across different stations, potentially leading to inconsistencies in the constant deviations obtained from different reference stations. To improve the consistency of the network-wide constant deviation estimates, a least squares method is used to unify the constant deviation benchmark for the entire network. For a given reference station r and satellite s, the constant deviation of the single-station solution benchmark is considered. This can be further refined as follows:

[0093]

[0094] In the formula, The constant deviation benchmark for station r, This represents the constant deviation for ensuring consistency with the overall network benchmark.

[0095] Suppose a network consists of n reference stations, each of which observes k... i With (i = 1, 2, ..., n) satellites, and the network observing a total of k different satellites, the following observation model can be constructed based on multiple single-station solutions.

[0096]

[0097] In the formula, L i This represents the single-station solution of the constant deviation for the i-th (i = 1, 2, ..., n) reference station; X1 and X2 represent the constant deviation reference for n stations and the constant deviation for k satellites, respectively; A i and B i These are the design matrices for the i-th reference station, with corresponding dimensions k. i ×n and k i ×k dimensions, where A i The element in the i-th column of the matrix is ​​1, and all other elements are 0. i In each row of the matrix, all elements are 0 except for the element corresponding to the satellite index, which has a value of 1. i The specific forms of X1 and X2 are as follows:

[0098]

[0099] The least squares model of the above formula (10) has a rank deficiency of 1. Therefore, an additional benchmark needs to be introduced to separate the station constant deviation benchmark and the satellite constant deviation estimate. For a certain epoch, the satellite that has been tracked the most times is selected as the benchmark, and the following pseudo-observation equation is added.

[0100]

[0101] In the formula, j represents the reference satellite; v is the virtual constraint value corresponding to the constant deviation.

[0102] It is important to note that the reference star selected may change at different epochs. To ensure the continuity of the constant bias, the virtual observation value of the reference star can be any value for the first epoch; while for subsequent epochs, taking advantage of the time-domain stability of the constant bias, the value of the reference star in the previous epoch can be selected as a constraint. In addition, to improve the stability of the overall network constant bias, multiple iterations can be performed to remove outliers with large post-hoc residuals.

[0103] Step (4): Rapid PPP positioning based on unified reference constant deviation constraints

[0104] Using the constant deviation of the whole network benchmark extracted in step 3, a multi-frequency PPP positioning model constrained by constant deviation is constructed based on the original observation values ​​of the base frequency.

[0105] First, the multi-frequency PPP model based on BeiDou B2b services is as follows:

[0106]

[0107] In the formula, and These are the fundamental frequency pseudorange and carrier observations, respectively. To correct for hardware deviations at the satellite end, For the distance between the stars, For ionospheric delay, γ i ψ is the corresponding ionospheric amplification factor. r,i Due to hardware deviation at the receiver end, and λ i These are the fundamental frequency ambiguity and the corresponding wavelength, respectively.

[0108] Further additional constant deviation constraints are as follows.

[0109]

[0110] In the formula, and These represent the constant deviation constraint value and the constraint equation, respectively. Considering that the precision of the constant deviation is approximately on the order of nanoseconds, and to avoid the influence of excessively small variance on other parameters, this invention adopts a relatively loose constraint strength, with a variance of 0.3.2 m 2 .

[0111] Based on the observation model constructed by formulas (13) and (14), under the constant deviation constraint of the whole network benchmark consistency, the advantages of Beidou multi-frequency signals are brought into play, and rapid positioning can be achieved by fixing the multi-frequency step ambiguity.

[0112] Figure 1 This is a basic flowchart of the present invention;

[0113] Figure 2 A comparison of the performance of the single-station solution with constant deviation and the whole-network solution of this invention reveals the following: In terms of the stability of constant deviation, the conventional single-station solution exhibits a significant convergence process in the initial stage of each arc segment, with a mean standard deviation of 0.9 nanoseconds. In contrast, the whole-network solution of this invention obtains a more stable constant deviation sequence in the initial stage, with a standard deviation of only 0.4 nanoseconds, demonstrating better stability. Regarding the duration of the constant deviation time series, the whole-network solution covers a longer arc segment than the single-station solution, meaning it has a longer usable arc segment per day, resulting in better availability.

[0114] Figure 3 The convergence effect of PPP with constant deviation constraint of the present invention and conventional PPP is compared: it can be found that after adopting the constant deviation constraint of the whole network solution of the present invention, the positioning accuracy in the initial stage is higher and the PPP positioning converges faster.

[0115] Figure 4 The comparison of instantaneous PPP horizontal positioning accuracy before and after the constant deviation constraint of the present invention is as follows: It can be seen that the horizontal positioning accuracy of the conventional method is only 0.844m, while the accuracy is improved to 0.2070m after adopting the constant deviation constraint of the whole network solution of the present invention, realizing instantaneous PPP horizontal decimeter-level positioning.

[0116] It should be noted that the above content merely illustrates the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. For those skilled in the art, various improvements and modifications can be made without departing from the principle of the present invention, and all such improvements and modifications fall within the scope of protection of the claims of the present invention.

Claims

1. A method for estimating the constant deviation of BeiDou B2b with unified network benchmarks, characterized in that: Includes the following steps: (1) By performing a reference transformation on the clock error correction of Beidou B2b service, the constant deviation parameter is separated; The specific process includes: The station receives real-time orbit and clock bias corrections from the BeiDou B2b service. These clock bias corrections are strongly correlated with satellite hardware latency, and the clock bias itself contains a reference related to this latency. This reference is frequency-dependent and based on a specific dual-frequency, ionosphere-free combination, expressed as follows: （1）； In the formula, superscript Representing different satellites, The speed of light in a vacuum. The satellite clock bias is based on a dual-frequency, ionospherically-free combination. This represents the actual satellite clock bias; The hardware delay for the BeiDou B2I / B6I dual-frequency ionospheric-free combination is specifically in the following form: （2）； wherein and are the BeiDou B2I and B6I frequency hardware delays, and are the ionosphere-free combination coefficients, satisfying the following relationship, （3）； wherein and are the frequencies of BDS system B2I and B6I frequencies; Compared to the clock biases mentioned above, the clock bias reference for BeiDou B2b service differs in two aspects: first, the B2b clock bias correction reference is based on the B6I single-frequency point, rather than the B2I / B6I dual-frequency ionospheric combination; second, the B2b clock bias correction also incorporates satellite-related constant biases, specifically expressed as follows: （4）； wherein is a satellite clock bias based on B2b service, is a clock constant bias; To address the issue of inconsistent clock references in B2b services, DCB information broadcast using Type 3 in B2b services is used to convert the clock references. （5）； In the formula, This involves converting the reference to the B2b clock bias after converting the B2I / B6I ionosphere-free combination. The DCB between the B2I and B6I frequency points in the Type 3 type used is represented as follows: ; (2) By adding a virtual benchmark, a full-rank estimation model of B2b constant deviation based on a single station is constructed; (3) Eliminate the benchmark differences of multiple single-station solutions and perform B2b constant deviation estimation for network benchmark unification; (4) Rapid PPP positioning based on the deviation constraint of unified reference constant.

2. The method for estimating the deviation of BeiDou B2b constants with unified network benchmarks as described in claim 1, characterized in that: In step (2), the process of constructing a full-rank estimation model of the B2b constant deviation based on a single station by adding a virtual benchmark includes: For reference stations with precisely known coordinates, after correcting the orbit and clock errors using PPP-B2b services, an ionospheric-free combined equation based on BeiDou B2I / B6I dual-frequency observation data is constructed. （6）； wherein is the zenith tropospheric wet delay, is the corresponding projection function, is the receiver clock error, and are the ionosphere-free combined ambiguity and wavelength, respectively. Substituting equation (5) into equation (6), we obtain a single-station estimation model that includes constant bias. （7）； In the model described in formula (7), the constant deviation and receiver clock difference A strong correlation exists, so a virtual solution benchmark is selected to separate the two; the satellite with the highest initial epoch elevation angle is selected as the benchmark to construct a single-station full-rank constant deviation estimation model. （8）； In the formula, Indicates the reference satellite. and These represent the constraint values ​​and constraint variances of the solution baseline, respectively. For continuous observation arcs, the constant deviation is used as a constant estimate, and only the strong constraint shown in Equation (8) is added to the initial epoch. The reference for subsequent epochs is passed through Kalman filtering.

3. The method of claim 2, wherein the method comprises: determining the BeiDou B2b constant bias of each satellite in the constellation based on the BeiDou B2b code phase of each satellite in the constellation. In step (3), the process of eliminating the benchmark differences among multiple single-station solutions and performing B2b constant deviation estimation for network-wide benchmark unification includes: The least squares method is used to unify the constant deviation benchmark for the entire network; for a certain reference station and satellite Taking into account the constant deviation of the single-station solution benchmark To further refine this, （9）； wherein is the constant bias reference for the station is the constant bias reference for the station is the constant bias reference for the station Suppose for a given... A network of reference stations, each of which observes... 1 satellite, of which The network observed a total of Based on multiple single-station solutions from different satellites, the following observation model is constructed. （10）； In the formula, Indicates the first The constant deviation of each reference station is the single-station solution, where ; and They represent The constant deviation benchmark for each station and The constant deviation of each satellite; and They are the first The corresponding design matrices for each reference station have the following dimensions: and Dimension, among which, The first of the matrix The first element is 1, and all other elements are 0. In each row of the matrix, all elements are 0 except for the one corresponding to the satellite index, which is 1. , and The specific form is as follows: （11）； The least squares model of the above formula (10) has a rank deficiency of 1. An additional benchmark is introduced to separate the station constant deviation benchmark and the satellite constant deviation estimate. For a certain epoch, the satellite that is tracked the most times is selected as the benchmark, and the following pseudo-observation equation is added. （12）； In the formula, denotes a reference satellite; is a virtual constraint value for the corresponding constant bias; To ensure the continuity of the constant bias, the virtual observation value of the reference star is set to any value for the first epoch; while for subsequent epochs, the value of the reference star in the previous epoch is selected as a constraint, taking advantage of the time-domain stability of the constant bias; multiple iterations are performed to eliminate outliers with large post-hoc residuals.

4. The method of claim 3, wherein the method comprises: determining the B2b constant bias of each satellite in the constellation based on the B2b constant bias of the reference satellite and the B2b constant bias of each satellite in the constellation. In step (4), the PPP rapid positioning process based on the unified reference constant deviation constraint includes: Using the constant deviation of the whole network benchmark extracted in step 3, a multi-frequency PPP positioning model constrained by constant deviation is constructed based on the original observation values ​​of the base frequency. First, the multi-frequency PPP model based on BeiDou B2b services is as follows: （13）； In the formula, and These are the fundamental frequency pseudorange and carrier observations, respectively. To correct for hardware deviations at the satellite end, For the distance between the stars, For ionospheric delay, This is the corresponding ionospheric amplification factor. Due to hardware deviation at the receiver end, and These are the fundamental frequency ambiguity and the corresponding wavelength, respectively. Further additional constant deviation constraints are as follows. （14）； In the formula, and respectively represent constant bias constraint values and constraint equations; take the variance as ; The observation model is constructed based on formulas (13) and (14).

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