Beidou B2b constant deviation estimation method with unified reference of whole network

Abstract

The invention discloses a whole network reference unified Beidou B2b constant deviation estimation method, which comprises the following steps of: firstly, performing Beidou B2b clock error reference transformation by applying Beidou B2b service DCB information to realize constant deviation parameterization separation; secondly, adding a single-station virtual reference, and constructing a constant deviation full-rank estimation model based on a single station; then, the constant deviation information of the whole network is integrated, the reference difference of a plurality of single station solutions is eliminated, and B2b constant deviation estimation with the unified reference of the whole network is achieved; and finally, under the constant deviation constraint of the unified reference, the performance of terminal positioning convergence speed, precision and the like is improved. According to the method, constant deviation estimation based on the whole network is realized, the stability and availability of Beidou B2b constant deviation estimation are improved, the problems of to-be-estimated parameter hopping and even filtering divergence possibly caused when a user side adopts constant deviations of different references are avoided, and then rapid convergence and instantaneous horizontal decimeter-level positioning of a terminal user are guaranteed.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of GNSS (Global Navigation Satellite System) positioning and navigation, and relates to Beidou B2b service real-time precise point positioning (PPP), in particular to a Beidou B2b constant bias estimation method with unified network reference. BACKGROUND

[0002] Precise point positioning can achieve centimeter-level positioning globally, and is an important technical approach to provide high-precision positioning, navigation and timing (PNT) services. At present, real-time PPP is mainly based on the real-time broadcast of SSR correction numbers according to the RTCM protocol, but it still has limited application in weak communication scenarios due to the need for stable ground network support. In order to get rid of the dependence on ground network and further expand the application of PPP in desert, ocean and other scenarios, the mode based on satellite-based augmentation is gradually favored, such as the Beidou system B2b augmentation service, which provides free and high-precision location services for users in China and its surrounding areas.

[0003] Although the PPP-B2b service currently has the ability to provide high-precision positioning services in the Asia-Pacific region, there are still some problems to be solved. Since the generation of PPP-B2b products only uses local reference stations, there is a constant satellite constant bias in the PPP-B2b clock correction number, which cannot be ignored and will reduce the quality of pseudorange observations, thereby affecting the convergence time and other performances of PPP. Although this constant bias can be extracted by a single station, there may be a problem of inconsistent reference between different stations.

[0004] In order to obtain a constant bias with unified reference, the application proposes a Beidou B2b constant bias estimation method with unified network reference, and constructs a multi-frequency PPP model with additional constant bias constraints. Based on the model, terminal single-epoch positioning is carried out. SUMMARY

[0005] To solve the above problems, the application discloses a Beidou B2b constant bias estimation method with unified network reference, which realizes the extraction of constant bias with unified network reference through the strategy of single station estimation and network unification.

[0006] To achieve the above purpose, the technical scheme of the application is as follows:

[0007] A Beidou B2b constant bias estimation method with unified network reference, comprising the following steps:

[0008] (1) The constant bias parameter is separated by performing reference transformation on the clock correction number of the Beidou B2b service;

[0009] (2) Construct a single-station-based B2b constant bias full-rank estimation model by adding a virtual reference;

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

[0011] (4) PPP fast positioning based on constant bias constraint under unified reference.

[0012] Further, in step (1), the process of separating the constant bias parameter includes:

[0013] The station receives the orbit and clock correction of the Beidou B2b service in real time. The clock correction is strongly related to the satellite end hardware delay, so the clock itself will contain the reference of the satellite end hardware delay. The reference of the clock is related to the frequency, and in general, the reference of the clock is based on a certain specific double-frequency ionosphere-free combination (L1 / L2 frequency points of the GPS system and B2I / B6I frequency points of Beidou), which can be represented as,

[0014]

[0015] In the formula, the superscript s represents different satellites, c is the speed of light in vacuum, is the satellite clock difference based on the double-frequency ionosphere-free combination, t s is the real satellite clock difference; is the hardware delay of the Beidou B2I / B6I double-frequency ionosphere-free combination, and the specific form is,

[0016]

[0017] In the formula, and are the hardware delays of the Beidou B2I and B6I frequency points, and α and β are ionosphere-free combination coefficients, which satisfy 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 with the above-mentioned clock difference, the clock difference reference of the Beidou B2b service has two differences, one is that the reference of the B2b clock difference correction is the B6I single frequency point, not the B2I / B6I double-frequency ionosphere-free combination, and the other is that the B2b clock difference correction also introduces a satellite-related constant bias, which can be specifically represented as,

[0021]

[0022] In the formula, is the satellite clock bias based on B2b service, g s is the constant bias of clock.

[0023] To solve the problem of inconsistent clock bias reference for B2b service, the differential code bias (DCB) information of Type3 type broadcast in B2b service can be used to convert the clock bias reference,

[0024]

[0025] where, is the B2b clock bias after the reference conversion to B2I / B6I ionosphere-free combination, D 26 is the DCB between B2I and B6I frequency points in Type3 type, which can be expressed as

[0026] It can be found from equation (5) that after the reference conversion, the clock bias of B2b service still has a constant bias g . s .

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

[0028] For a reference station with accurately known coordinates, after the orbit and clock bias are corrected by PPP-B2b service, an ionosphere-free combination equation based on Beidou B2I / B6I dual-frequency observation data is constructed,

[0029]

[0030] where, T r is the zenith tropospheric wet delay, is the corresponding projection function, is the receiver clock bias, and λ IF are the ionosphere-free combination ambiguity and wavelength, respectively.

[0031] The formula (5) is brought into the formula (6) to obtain a single-station estimation model containing a constant bias,

[0032]

[0033] In the model described in equation (7), the constant bias g s and the receiver clock bias There is a strong correlation, and for this reason, a virtual solution reference needs to be selected to separate the two. In the present application, the satellite with the highest elevation angle at the initial epoch is selected as the reference to construct a single-station full-rank constant bias estimation model,

[0034]

[0035] In the formula, ref represents the reference satellite, g ref and respectively represent the constraint value and constraint variance of the solution reference.

[0036] It should be noted that for continuous observation arcs, the constant bias can be estimated as a constant, and therefore, only the strong constraint shown in formula (8) is added at the initial epoch, and the reference at subsequent epochs is passed through Kalman filtering.

[0037] Further, in step (3), the process of eliminating the reference differences of multiple single-station solutions and performing B2b constant bias estimation for the entire network reference unification includes:

[0038] In a reference network, the satellites observed by different stations may differ, and therefore, the constant biases obtained by different reference stations may have inconsistent references. In order to improve the consistency of the constant bias estimation of the entire network, the least squares method is used to unify the constant bias reference of the entire network. For a reference station r and a satellite s, the constant bias of the single-station solution is considered which can be further refined as,

[0039]

[0040] In the formula, is the constant bias reference of the station r, and is the constant bias consistent with the entire network reference.

[0041] Suppose that for a network consisting of n reference stations, each reference station observes k i satellites, and the network observes a total of k different satellites, based on multiple single-station solutions, the following observation model can be constructed,

[0042]

[0043] In the formula, L i represents the constant bias single-station solution of the i-th (i = 1, 2, …, n) reference station; X1 and X2 respectively represent the constant bias reference of the n stations and the constant bias of the k satellites; A i and B i are the design matrices of the i-th reference station, with corresponding dimensions of k i × n and k iwhere A is a k x n matrix, where A i The i-th column of matrix B has 1 in the diagonal and 0 elsewhere. i Each row of matrix L has 1 in the diagonal corresponding to the satellite index and 0 elsewhere. i The specific forms of X1 and X2 are as follows,

[0044]

[0045] The rank deficiency of the least squares model of the above formula (10) is 1, and therefore an additional reference is needed to separate the reference station constant bias reference and the satellite constant bias estimate. For a certain epoch, select the satellite with the most tracking times as the reference, and add the following pseudo-observation equation,

[0046]

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

[0048] It should be noted that the reference star selected in different epochs may change. To ensure the continuity of the constant bias, for the first epoch, the virtual observation value of the reference star can be taken as any value; and for subsequent epochs, the value of the reference star in the last epoch can be selected as the constraint using the time-domain stability characteristics of the constant bias. In addition, to improve the stability of the network-wide constant bias, multiple iterations can be performed to eliminate abnormal values with large posterior residuals.

[0049] Further, in step (4), the PPP rapid positioning process based on the unified reference constant bias constraint comprises:

[0050] The network-wide constant bias consistent with the reference is extracted in step 3, and a multi-frequency PPP positioning model with constant bias constraint is constructed based on the original observation values of the basic frequencies.

[0051] First, the multi-frequency PPP model based on the Beidou B2b service is,

[0052]

[0053] In the formula, and are the basic frequency pseudorange and carrier observation values, respectively, is the satellite end hardware bias correction, is the station-star distance, is the ionospheric delay, γ i is the corresponding ionospheric amplification factor, ψ r,i is the receiver end hardware bias, and λ i are the basic frequency ambiguity and the corresponding wavelength, respectively.

[0054] Further additional constant bias constraints are as follows,

[0055]

[0056] In the formula, And respectively represent the constant bias constraint value and the constraint equation. The accuracy considering the constant bias is about nanosecond, and in order to avoid the influence of too small variance on the remaining parameters, the application adopts a relatively loose constraint strength, and the variance is 0.3 2 m 2 .

[0057] Based on the observation model constructed based on formulas (13) and (14), under the constant bias constraint of the whole network reference, the advantages of Beidou multi-frequency signals are exerted, and multi-frequency step ambiguity fixing can be realized to achieve rapid positioning.

[0058] The beneficial effects of the application include:

[0059] The Beidou B2b constant bias estimation method proposed in the application is based on single-station constant bias estimation, exerts the advantages of the reference station network, eliminates the problem of inconsistent constant bias reference between single-station solutions, and avoids the problem of jump of parameters to be estimated and even filter divergence caused by different constant bias references at the user end. The method improves the stability and usability of Beidou B2b constant bias estimation. Under the constraint of the unified reference constant bias, the user can achieve rapid convergence and instantaneous horizontal decimeter-level positioning, and the performance of PPP such as convergence and positioning is improved. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 is the flow chart of the method described in the application;

[0061] Figure 2 is the effect comparison of the single-station solution of the constant bias and the whole network solution of the application;

[0062] Figure 3 is the convergence effect comparison of the constant bias constrained PPP and the conventional PPP of the application;

[0063] Figure 4 is the instantaneous PPP horizontal positioning accuracy comparison before and after the constant bias constraint of the application. DETAILED DESCRIPTION

[0064] The application will be further illustrated below in combination with the drawings and specific embodiments, and it should be understood that the following specific embodiments are only used to illustrate the application and not to limit the scope of the application.

[0065] As 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 error 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, the network observes a total of k different satellites. Based on multiple single-station solutions, the following observation model can be constructed.

[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 is 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 value 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; (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 (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: The station receives real-time orbit and clock corrections from the BeiDou B2b service. These clock corrections are strongly correlated with satellite hardware latency; therefore, the clock error itself contains a reference related to satellite hardware latency. This reference is frequency-dependent and based on a specific dual-frequency, ionosphere-free combination, expressed as follows: 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: 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. In the formula, f2 and f6 are the frequencies of the B2I and B6I frequency points of the BDS system; 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 incorporate satellite-related constant biases, specifically expressed as follows: In the formula, For satellite clock bias based on B2b services, g s This represents the deviation of the clock constant. 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. 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 is represented as follows: Equation (5) reveals that, after benchmark conversion, the clock skew of B2b services... Compared with conventional clock difference There is still a constant deviation g s .

3. 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. In the formula, T r For the zenith tropospheric wet delay, For the corresponding projection function, For receiver clock bias, and λ IF These are the ambiguity and wavelength of the non-ionospheric combination, respectively. Substituting equation (5) into equation (6), we obtain a single-station estimation model that includes constant bias. In the model described in formula (7), the constant deviation g s and receiver clock difference There is also a strong correlation; therefore, a virtual solution benchmark needs to be selected to separate the two. Finally, 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. 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. For continuous observation arcs, the constant deviation is 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 passed through Kalman filtering.

4. The method for estimating the deviation of BeiDou B2b constants with unified network benchmarks as described in claim 1, characterized in that: 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 specific reference station r and satellite s, the constant deviation of the single-station solution benchmark is taken into account. To further refine this, In the formula, The constant deviation benchmark for station r, The constant deviation for ensuring consistency of the entire network benchmark; Suppose a network consists of n reference stations, each of which observes k... i With (i = 1, 2, ..., n) satellites, the network observes a total of k different satellites. Based on multiple single-station solutions, the following observation model is constructed. 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 Each element in each row of the matrix is ​​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: The least squares model of the above formula (10) has a rank deficiency of 1. Therefore, 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 has been tracked the most times is selected as the benchmark, and the following pseudo-observation equation is added. In the formula, j represents the reference satellite; v is the virtual constraint value corresponding to the constant deviation; To ensure the continuity of the constant bias, the virtual observation value of the reference star is taken as arbitrary 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; in addition, to improve the stability of the constant bias of the entire network, multiple iterations are required to remove outliers with large post-verification residuals.

5. The method for estimating the deviation of BeiDou B2b constants with unified network benchmarks as described in claim 1, characterized in that: 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: 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. Further additional constant deviation constraints are as follows. In the formula, and These represent the constant deviation constraint value and the constraint equation, respectively. Considering the nanosecond-level precision of the constant deviation, and to avoid the influence of excessively small variance on other parameters, a relatively loose constraint strength is adopted, with a variance of 0.

3. 2 m 2 ; 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 is achieved by fixing the multi-frequency step ambiguity.

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