Grid atmospheric modeling suitable for beidou PPP-b2b service and PPP-rtk positioning method and system

Abstract

This invention discloses a grid atmospheric modeling and PPP-RTK positioning method and system applicable to BeiDou PPP-B2b service. The server combines broadcast ephemeris data with PPP-B2b corrections, and generates high-precision satellite orbit clock errors through benchmark unification processing. A non-differential, non-combined model that takes into account specific satellite clock error deviations is constructed using regional reference station network data to estimate the uncorrected phase delay compatible with these deviations. Based on this delay and orbit clock errors, ambiguity is fixed stepwise, regional ionospheric and tropospheric delays are extracted, and a grid atmospheric enhancement product is generated through a two-layer modeling strategy of fitting and adding residuals. The phase delay and atmospheric product are broadcast to the user terminal. Upon receiving the data, the user terminal recovers the atmospheric corrections, incorporates them as constraints into the observation equations, and performs rapid ambiguity fixing in conjunction with the phase delay to achieve positioning. This invention achieves rapid ambiguity fixing for PPP-B2b service in dynamic scenarios, significantly shortening convergence time and improving positioning accuracy.

Description

Technical Field

[0001] This invention belongs to the field of high-precision positioning technology of Global Navigation Satellite System (GNSS), and more specifically, it relates to a PPP-RTK positioning method that generates regional atmospheric and phase deviation information based on the characteristics of BeiDou PPP-B2b service, and performs gridded ionosphere and troposphere modeling to achieve rapid ambiguity fixation of PPP-B2b. Background Technology

[0002] In modern precision navigation and positioning applications, real-time, high-precision positioning services have become a core requirement for many industries. Currently, the main methods for achieving high-precision positioning include Real-Time Kinematic (RTK) and Precise Point Positioning (PPP). Traditional RTK and PPP technologies typically rely heavily on terrestrial mobile communication networks to transmit base station observation data or precision correction information. However, in oceans, deserts, high altitudes, and remote areas with weak communication infrastructure, terrestrial network signals are often difficult to cover, limiting the application scope of these technologies. The PPP-B2b service provided by the BeiDou-3 Global Navigation Satellite System (BDS-3), as a real-time precise positioning method broadcast via satellite-based links, has demonstrated enormous potential in high-precision applications in my country and surrounding regions due to its significant advantages, such as not requiring terrestrial network communication and having wide coverage. However, existing PPP-B2b service corrections primarily focus on satellite orbit and clock bias products, lacking high-precision phase deviation and atmospheric delay information for regional users. This limits the positioning accuracy of user terminals in dynamic environments and typically results in initial convergence times of tens of minutes, making it difficult to meet the demands of highly dynamic and time-sensitive scenarios such as autonomous driving and drone swarms. Furthermore, considering the satellite-based broadcasting characteristics of PPP-B2b services, it is necessary to establish a high-precision correction information model with low data broadcast volume and low transmission burden to achieve efficient and reliable positioning services. Summary of the Invention

[0003] To improve the convergence efficiency and positioning accuracy of BeiDou PPP-B2b service in dynamic applications, a grid atmospheric modeling and PPP-RTK positioning method suitable for BeiDou PPP-B2b service is proposed. This method constructs a grid atmospheric and wide-area phase deviation model, fully utilizing the inherent advantages of grid-based modeling in reducing communication link load and desensitizing location information. Combined with the technical characteristics of PPP-B2b and PPP-RTK, it drives rapid ambiguity fixation, providing users with stable, reliable, and high-precision positioning results.

[0004] According to one aspect of this specification, a grid atmospheric modeling and PPP-RTK positioning method suitable for BeiDou PPP-B2b service is provided, applied to the server side, comprising: By combining broadcast ephemeris with PPP-B2b orbit and clock bias corrections, high-precision satellite orbits and clock biases are generated after benchmark unification processing. Using observation data from a regional reference station network, a non-differential, non-combined, precise single-point positioning model that takes into account specific satellite clock biases is constructed, and the uncorrected phase delay that is compatible with the specific satellite clock biases is estimated based on this model. Based on the uncorrected phase delay and the high-precision satellite orbit and clock error, the ambiguity of the reference station network is fixed step by step, the regional ionospheric delay and tropospheric delay are extracted, and grid atmospheric enhancement products are generated through grid modeling. The uncorrected phase delay and the gridded atmospheric enhancement product are broadcast to the user terminal to enable rapid ambiguity fixation and positioning calculation at the user terminal.

[0005] As a further technical solution, the generation of high-precision satellite orbits and clock biases through benchmark unification processing includes: The satellite orbit position generated by the broadcast ephemeris is corrected according to the PPP-B2b orbit correction number to obtain the corrected satellite orbit position. The satellite clock bias generated by the broadcast ephemeris is corrected according to the PPP-B2b clock bias correction to obtain the corrected satellite clock bias. By converting the clock bias frequency reference, the corrected satellite clock bias is unified to the frequency reference used by the PPP-B2b service.

[0006] As a further technical solution, the construction of a non-differential, non-combined, precise single-point positioning model that takes into account satellite-specific clock bias includes: introducing a satellite-specific clock bias correction term into the observation equation of the non-differential, non-combined, precise single-point positioning to absorb the satellite-specific clock bias in the PPP-B2b product.

[0007] As a further technical solution, the estimation of the uncorrected phase delay compatible with the specific clock bias of the satellite includes: Based on the non-differential non-combined precise single-point positioning model that takes into account the specific clock bias of the satellite, the wide-lane floating-point ambiguity and narrow-lane floating-point ambiguity of each reference station corresponding to each satellite are extracted. The specific clock bias of the satellite is jointly estimated with the wide-lane uncorrected phase delay and the narrow-lane uncorrected phase delay at the satellite end; Based on the wide-lane floating-point ambiguity and narrow-lane floating-point ambiguity of each reference station, the uncorrected phase delay of the wide-lane and the uncorrected phase delay of the narrow-lane at the satellite end are obtained by least squares estimation.

[0008] As a further technical solution, the stepwise ambiguity fixing of the reference station network and the extraction of regional ionospheric delay and tropospheric delay include: Based on the wide-lane uncorrected phase delay in the uncorrected phase delay, the wide-lane floating-point ambiguity of each reference station is fixed to an integer. Using the fixed wide-lane ambiguity as a constraint, the narrow-lane floating-point ambiguity is refined, and combined with the uncorrected phase delay of the narrow-lane, the refined narrow-lane floating-point ambiguity is fixed to an integer to obtain the fixed base station solution. Based on the base station solution after the ambiguity is fixed stepwise, the ionospheric delay and tropospheric delay of each base station are extracted. The extracted ionospheric delay is compensated for by receiver-side hardware delay to obtain a clean ionospheric delay.

[0009] As a further technical solution, the receiver-side hardware delay compensation processing for the extracted ionospheric delay includes: By constructing an inter-station single-difference model to eliminate the influence of satellite-end hardware delay in the ionosphere, an estimation equation for receiver-end hardware delay is established. The constrained least squares method is used to solve the receiver hardware delay of each reference station; Based on the calculated receiver hardware delay, the extracted ionospheric delay is corrected to obtain a clean ionospheric delay.

[0010] As a further technical solution, the generation of grid-based atmospheric enhancement products through grid modeling includes: A two-layer modeling strategy of fitting term plus residual term is adopted. Based on the location of each reference station and its corresponding ionospheric delay and tropospheric delay, the polynomial fitting coefficient of the regional atmospheric delay is calculated. The fitted atmospheric delay value of each reference station is calculated based on the polynomial fitting coefficients, and the atmospheric delay residual of each reference station is obtained based on the atmospheric delay value extracted from each reference station and the fitted atmospheric delay value. Using spatial interpolation, the atmospheric delay residuals of each reference station are mapped onto preset grid nodes to obtain the fitting residuals of each grid node. The polynomial fitting coefficients and the fitting residuals of each grid node are used as part of the grid atmospheric enhancement product.

[0011] According to one aspect of the present invention, a positioning system suitable for BeiDou PPP-B2b service is provided, comprising a server and a user terminal; The server is used to combine broadcast ephemeris data with PPP-B2b orbit and clock bias corrections, and generate high-precision satellite orbits and clock biases through benchmark unification processing; using regional benchmark station network observation data, it constructs a non-differential, non-combined, precise single-point positioning model that takes into account specific satellite clock biases, and estimates the uncorrected phase delay compatible with the specific satellite clock biases based on this model; based on the uncorrected phase delay and the high-precision satellite orbits and clock biases, it performs stepwise ambiguity fixation on the benchmark station network, extracts regional ionospheric delay and tropospheric delay, and generates grid atmospheric enhancement products through grid modeling; and broadcasts the uncorrected phase delay and the grid atmospheric enhancement products to the user terminal; The user terminal is used to receive and fuse PPP-B2b satellite-based corrections, the uncorrected phase delay, and the grid atmospheric enhancement product to perform rapid ambiguity fixing and positioning calculation.

[0012] As a further technical solution, the user terminal performs rapid ambiguity fixing and positioning calculation, including: Using the same method as the server-side generation of high-precision satellite orbits and clock biases, high-precision satellite orbits and clock biases for user terminals are generated based on broadcast ephemeris and PPP-B2b corrections. Based on the polynomial fitting coefficients and grid node fitting residuals in the received grid atmospheric enhancement product, the atmospheric correction at the user's location is recovered. A non-differential, non-combined, precise single-point positioning model is constructed, and the recovered atmospheric corrections are added as constraints to the observation equations. Based on the received uncorrected phase delay, the ambiguity parameters are corrected, and the ambiguity is fixed using the least squares ambiguity decorrelation adjustment method.

[0013] According to one aspect of the present invention, a server device is provided, including a processor and a memory, the memory storing a computer program, wherein the processor executes the computer program to implement the grid atmospheric modeling and PPP-RTK positioning method applicable to BeiDou PPP-B2b service.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: First, according to the technical solution of this invention, by employing a non-differential, non-combination processing method that takes into account specific deviations of the product satellite, phase deviation products compatible with PPP-B2b can be separated in real time, thereby achieving reliable ambiguity fixation for PPP-B2b. Compared with traditional PPP-B2b positioning methods, this invention has significant advantages, effectively eliminating the interference of hardware delay deviations implicit in ambiguity parameters on phase recovery and significantly improving the accuracy of positioning solutions.

[0015] Secondly, by constructing and broadcasting high-precision gridded atmospheric enhancement information adapted to PPP-B2b, this invention can effectively constrain the atmospheric environment at the user end, thereby improving the positioning accuracy and convergence efficiency of PPP-B2b. Compared with traditional enhancement methods based on two-way communication of terrestrial networks, the advantages of this invention are: on the one hand, the gridded model has the characteristics of less data volume and lower transmission burden, which can better adapt to the bandwidth limitations of PPP-B2b satellite-based links; on the other hand, the gridded enhancement scheme supports broadcast transmission, and users can achieve atmospheric correction interpolation locally without uploading personal location information, which has a strong privacy protection capability. This invention utilizes the spatial correlation of regional reference station networks to extract fine atmospheric delay information and provide it to users, achieving instantaneous high-precision positioning in dynamic environments.

[0016] The technical solution of this invention effectively overcomes the problems of slow convergence speed and insufficient positioning accuracy of existing PPP-B2b services in dynamic applications, improving both service availability and timeliness. These technical effects and advantages make this technical solution of significant value and broad application prospects in practical applications. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the grid atmospheric modeling and PPP-RTK positioning method applicable to BeiDou PPP-B2b service provided in an embodiment of the present invention.

[0019] Figure 2 A schematic diagram of grid atmospheric modeling provided for an embodiment of the present invention. Detailed Implementation

[0020] This invention provides a grid atmospheric modeling and PPP-RTK positioning method applicable to BeiDou PPP-B2b service. First, at the server end, by combining broadcast ephemeris data with PPP-B2b orbit clock corrections, a high-precision satellite orbit and clock bias reference consistent with existing PPP-B2b services is generated. Next, at the server end, an undifferentiated, non-combined PPP model considering satellite-specific clock biases (SSCBs) in PPP-B2b is used to estimate the uncorrected phase delay (UPD) compatible with SSCBs. Then, at the server end, regional ionospheric and tropospheric delays are extracted based on reference station network information, and a low-bandwidth-occupying grid atmospheric enhancement product is generated through grid modeling. Finally, at the user end, the PPP-B2b satellite-based corrections and the enhancement product broadcast by the server are deeply fused to achieve rapid ambiguity fixation, thereby improving positioning accuracy and convergence efficiency.

[0021] The terms “comprising” and “having”, and any variations thereof, in the specification, claims, and accompanying drawings of this invention are intended to cover a non-exclusive inclusion, such as a process, method, system, product, or apparatus that includes a series of steps or units, not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. In addition, the technical features of the various embodiments or individual embodiments provided by the present invention can be arbitrarily combined to form new technical solutions. Such combinations are not bound by the order of steps and / or structural composition patterns, but must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0023] This invention provides a grid atmospheric modeling and PPP-RTK positioning method suitable for BeiDou PPP-B2b service. It aims to establish and generate a grid atmospheric model and phase deviation information adapted to PPP-B2b service, solving the problems of long convergence time and difficulty in achieving instantaneous centimeter-level positioning in dynamic application scenarios of existing PPP-B2b services. This method, by fully considering the error characteristics of PPP-B2b products, generates a refined regional grid atmospheric model and phase deviation product consistent with the PPP-B2b benchmark at the server end and broadcasts it to the user end, thereby significantly improving user positioning performance.

[0024] This embodiment provides a grid atmospheric modeling and PPP-RTK positioning method applicable to BeiDou PPP-B2b service, such as... Figure 1 As shown, the overall approach is as follows: On the server side, real-time broadcast ephemeris and PPP-B2b orbital clock corrections are first acquired. Through coordinate transformation and benchmark unification processing, a high-precision satellite orbital clock reference is reconstructed. Subsequently, using observation data from the regional reference station network, a non-differential, non-combined PPP model that considers satellite-specific clock bias (SSCB) is constructed. This model is used to estimate uncorrected phase delay (UPD) products compatible with SSCB characteristics in real time, achieving ambiguity fixation for the reference stations. To eliminate the impact of receiver hardware delay (DCB) on ionospheric modeling, the receiver-side bias is accurately estimated and compensated to obtain clean atmospheric delay information. Based on this, ionospheric and tropospheric delays are estimated using a fixed solution. A regional grid atmospheric refinement model is established using polynomial fitting and residual spatial interpolation methods. Finally, a PPP-RTK augmentation product containing UPD and grid atmospheric corrections is constructed and broadcast to users. On the user side, while the receiver acquires the PPP-B2b satellite-based corrections, the augmentation product is injected into the observation equations to offset atmospheric delays and eliminate phase biases, achieving rapid ambiguity fixation and obtaining centimeter-level positioning solutions.

[0025] The specific implementation process of this method includes: Step 1: On the server side, combine the broadcast ephemeris with the PPP-B2b orbit and clock correction values ​​to generate a high-precision satellite orbit and clock reference that is consistent with the existing PPP-B2b service.

[0026] Specifically, the broadcast ephemeris received by the server in real time should correspond to CNAV1 and LNAV navigation messages for BeiDou and GPS, respectively. Combined with the B2b corrections broadcast by BDS-3, a high-precision satellite orbit and clock bias reference consistent with the existing PPP-B2b service is generated. The satellite orbit calculation formula is as follows: , in, This is the corrected satellite orbit position; Satellite orbital positions generated for broadcast ephemeris; These are the orbital corrections for the PPP-B2b satellite in the Earth-centered Earth-fixed system. The calculation method for the satellite orbital corrections is as follows: , in, and These are the satellite position and velocity vectors generated from the broadcast ephemeris; The correction components of the PPP-B2b track in the radial, tangential, and normal directions are commonly referred to as RAC correction components. This represents the transformation matrix in the RAC direction.

[0027] The specific calculation formula for satellite clock bias is as follows: , in, The corrected satellite clock bias; Satellite clock biases generated for broadcast ephemeris; is the PPP-B2b clock error correction; c is the speed of light.

[0028] Furthermore, by converting the clock bias frequency reference, the satellite clock bias reference is unified, as follows: , in, Clock bias based on the B1I / B3I ionosphere-free combination; Clock bias based on B3I frequency; and These are the frequencies of BDS-3 B1I and B3I, respectively. DCB information provided for PPP-B2b.

[0029] Step 2: On the server side, estimate the uncorrected phase delay (UPD) compatible with SSCB by using a non-differential, non-combined PPP model that takes into account the satellite-specific clock bias (SSCB) in PPP-B2b.

[0030] Using regional reference station network data, a non-differential, non-combined PPP observation equation is established. To address the satellite-specific clock bias (SSCB) present in PPP-B2b products, an SSCB correction term is introduced into the observation equation. Specifically, the non-differential, non-combined observation equation is expressed as follows: , in and These represent the observed pseudorange and carrier phase values ​​of satellite s and receiver r at the i-th frequency, respectively, minus the calculated (OMC) values. Let x be the unit vector from the receiver to the satellite, and let x represent the position increment vector of the receiver relative to its prior position. and These represent the receiver and satellite clock errors after absorbing hardware delays, respectively; Indicates a specific clock bias deviation of the satellite; This represents the ionospheric delay after absorbing hardware delay at the first frequency, and is expressed by a scaling factor. Mapped to other frequencies; For the zenith tropospheric wet delay, the mapping function is: ; Indicates ambiguity, corresponding to wavelength. ; and These represent the sum of pseudorange and carrier phase observation noise and multipath error, respectively.

[0031] Considering the stability characteristics of specific clock biases in satellites, this bias is incorporated into the ambiguity parameter, with the specific expression being: .

[0032] Combining a non-differential, non-combined PPP model that takes into account specific satellite clock biases, the uncorrected phase delay (UPD) of each satellite is further extracted, including wide-lane UPD and narrow-lane UPD. Considering the wavelength of the ambiguity, following the principle of starting with the simplest approach, the wider-lane ambiguity with a longer wavelength is first constructed, and then the integer-cycle wider-lane ambiguity is used as a constraint to obtain a more accurate narrow-lane ambiguity. The specific formula is as follows: , in, and These are the corresponding floating-point and integer ambiguities, respectively; and These are the UPDs corresponding to the receiver and the satellite, respectively. These respectively represent wide alleys and narrow alleys; This indicates the fixed width ambiguity; These represent the frequencies of the signals at frequency points i and j, respectively. denoted as linear coefficients composed of different frequencies.

[0033] Further incorporating specific clock biases from the satellite end into the satellite-end UPD for joint estimation yields: .

[0034] Based on this, assuming there are n reference network stations and m available satellites at each station, the UPD can be obtained through least squares estimation, as shown in the following formula: , in, , represents the coefficient matrix of the receiver UPD of each satellite system. In the matrix, the column element corresponding to the receiver UPD is 1, and the other elements are 0; This represents the coefficient matrix of satellite UPD, where the element corresponding to satellite UPD is -1 and the rest are 0.

[0035] Step 3: On the server side, extract the regional ionospheric and tropospheric delays based on the reference station network information, and generate a low-bandwidth-occupying grid atmospheric enhancement product through grid modeling.

[0036] Based on the high-precision phase deviation product obtained in step 2, combined with the high-precision satellite orbits and clock biases after unification, the PPP ambiguity fixation (PPP-AR) of the reference station network can be achieved, thereby extracting high-precision tropospheric and ionospheric information. Since the directly extracted ionospheric information contains the receiver hardware delay (DCB) of different reference stations, in order to ensure the consistency of broadcast products, it is first necessary to compensate for the receiver hardware deviation of the stations.

[0037] Specifically, an inter-station single-difference model is constructed to eliminate the influence of satellite-end hardware delay in the ionosphere, and the following DCB estimation equation is established: , Where p and q represent different measuring stations. This is the initial ionospheric delay of the station extracted through ambiguity fixation. The pseudorange hardware delay at the i-th frequency point of the station receiver; The coefficient term consists of frequency points i and j; This represents the difference in initial ionospheric delay at different stations. The constrained least squares method is used to solve for the DCB at each station, with the sum of the DCBs of all reference stations set to zero as a constraint to address the rank deficiency problem of the normal equation. This leads to the corrected pure ionospheric delay. .

[0038] Specifically, the corrected pure ionospheric delay can be expressed as: , in, This represents the residual amount after DCB compensation at the receiver. This represents the original ionospheric delay, excluding hardware delay. This represents the DCB calculated value between different frequency points of the station receiver.

[0039] Furthermore, using the processed atmospheric delay information, a two-layer modeling strategy of fitting terms + residual terms is employed to balance the large-scale atmospheric change trend with the fitting accuracy of local areas. The grid atmospheric modeling method is as follows: Figure 2 As shown. Specifically, the polynomial fitting coefficients of the grid model are first calculated using reference station and atmospheric delay information. The polynomial coefficients can be estimated using the least squares method, as follows: , in represents the atmospheric delay extracted by the base station r; k is the number of effective reference stations; 0, 1, and 2 are the orders of the polynomial fitting equations, which are selected based on k. These are polynomial coefficients; and These represent the latitude and longitude of the base station, respectively. and These represent the latitude and longitude of the center point of the region, respectively.

[0040] Furthermore, the atmospheric delay residual is obtained by subtracting the polynomial fitted value from the extracted atmospheric delay value. Using spatial interpolation, the residuals of discrete stations are mapped onto preset grid nodes, as shown in the following formula: , in, This indicates the atmospheric delay value used in the fitting process; This represents the atmospheric delay value calculated using polynomial coefficients; This represents the fitting residual at the base station; Indicates the base station The distance between the grid node g; This represents the fitting residual of grid node g.

[0041] After the residuals from the base station are incorporated into the grid points, the grid point information, polynomial fitting coefficients, and grid point residuals are broadcast to the users. The grid point information varies depending on the server-side conditions. This method only broadcasts the fitting coefficients and residuals, effectively reducing the amount of data broadcast, alleviating the transmission burden, and facilitating the broadcasting of server-side information.

[0042] After generating the uncorrected phase delay and gridded atmospheric enhancement products, the server performs product quality checks. These checks include: verifying the temporal stability of the wide-lane and narrow-lane uncorrected phase delays, eliminating satellite-end products with jumps or instability; and detecting gross errors in the fitting residuals of the gridded atmospheric enhancement products to ensure that the grid node residual values ​​are within reasonable threshold ranges. Only products that pass the checks can be broadcast to the user terminal to ensure the reliability of the broadcast data.

[0043] Step 4: On the user end, deeply integrate PPP-B2b satellite-based correction data with the enhanced products broadcast by the server to achieve rapid ambiguity fixation, thereby improving positioning accuracy and convergence efficiency.

[0044] First, using the same method as in step 1, a corrected high-precision orbit and clock difference product is generated, and a user-side PPP-B2b non-differential, non-combined model is constructed. The specific formula is as follows: .

[0045] To estimate user terminal position, clock bias, ambiguity, and other state parameters in real time, a Kalman filter is used for recursive calculation. The state vector includes position, receiver clock bias, ionosphere, troposphere, and ambiguity parameters. The recursive process comprises two core components: time-updated state prediction and measurement updates based on observation data.

[0046] Specifically, state prediction utilizes the state estimate and its uncertainties from the previous moment to deduce the prior state at the current moment through a system dynamics model. The specific formula is as follows: , in, and Let k be the state prediction vector and its covariance matrix at time k. This is the state transition matrix, which describes the relationship between state parameters and time. is the process noise matrix, used to characterize the system model error; T represents the matrix transpose.

[0047] After obtaining the GNSS observations at the current moment, the measurement update process calculates the observation residuals and optimally corrects the prior state using Kalman gain, thereby obtaining the posterior state estimate and its covariance. The specific update formula is as follows: , in, Design a matrix for observation; To observe the noise covariance matrix; The Kalman gain determines the weight of the observation residuals on the state correction. This is the difference between the actual observed value and the theoretical observed value calculated based on the predicted state, i.e., the observation residual vector.

[0048] Based on this, the enhanced products generated in steps 1-3 are added as constraints to the Kalman filter measurement update equation.

[0049] Specifically, for grid-based atmospheric enhancement products, ambiguity is searched and fixed using external atmospheric correction constraints and phase deviation products. First, the user needs to recover the high-precision atmospheric correction using grid node coordinates, polynomial coefficients, and grid residual information. The specific formula is as follows: , in, This indicates the atmospheric delay obtained by the user. This represents the atmospheric delay calculated using the fitting coefficients; This represents the fitting residuals around the four grid nodes of the user distribution; while This indicates the distance between the user and the grid node.

[0050] After recovering the corresponding tropospheric and ionospheric atmospheric information, the convergence of the observation equations is accelerated by using the user-end atmospheric constraint equations. The specific constraint equations are as follows: , in, and These are the user-end zenith tropospheric wet delay and ionospheric skew delay corrections obtained from n reference stations, respectively; and The difference between the correction and the atmospheric delay is a zero-mean white noise process with a variance of . and N represents a normal distribution; These are the estimated values ​​for the tropospheric zenith wet delay and the ionospheric slant delay at the user end, respectively. These represent the differences between the estimated and corrected values ​​of the tropospheric zenith wet delay and the ionospheric oblique delay, respectively, which correspond to the a priori residuals of the atmospheric constraint equations. The aforementioned atmospheric constraints can effectively mitigate the influence of atmospheric errors and accelerate ambiguity convergence.

[0051] For phase deviation products, the ambiguities of the wide lane and narrow lane are gradually fixed using the least squares ambiguity reduction adjustment method. The fixed ambiguities are then used as the constraint equations for virtual observation information, as detailed below: , in, and These represent the fixed ambiguity and UPD after inter-satellite single difference, respectively, eliminating the influence of receiver-side UPD; Let V be the variance of the virtual observation equation. This represents the floating-point blur after inter-satellite single difference; This represents the prior residual of the fuzzy constraint equation.

[0052] Based on the same inventive concept as the foregoing embodiments, this embodiment of the invention also provides a positioning system suitable for BeiDou PPP-B2b services, including a server and a user terminal.

[0053] The server is used to combine broadcast ephemeris data with PPP-B2b orbit and clock bias corrections, and generate high-precision satellite orbits and clock biases through benchmark unification processing. The specific generation method of satellite orbits and clock biases is described in step 1 of the previous method embodiment, and will not be repeated here.

[0054] The server is also used to construct a non-differential, non-combined, precise point positioning model that takes into account satellite-specific clock biases using regional reference station network observation data, and to estimate the uncorrected phase delay compatible with the satellite-specific clock biases based on this model. This model incorporates a satellite-specific clock bias correction term to absorb the satellite-specific clock biases in the PPP-B2b product. The estimation process for the uncorrected phase delay is described in step 2 of the previous method embodiment, including jointly estimating the satellite-specific clock biases and the satellite-end UPD, and obtaining the wide-lane UPD and narrow-lane UPD through least squares.

[0055] The server is also used to perform stepwise ambiguity fixing on the reference station network based on the uncorrected phase delay and the high-precision satellite orbit and clock bias, extract regional ionospheric delay and tropospheric delay, and generate gridded atmospheric enhancement products through grid modeling. The stepwise ambiguity fixing adopts a method of first fixing the wide-lane ambiguity and then fixing the narrow-lane ambiguity; the specific process is described in step 3 of the previous method embodiment. After extracting the atmospheric delay, the server performs hardware delay compensation processing on the receiver end to obtain a clean ionospheric delay.

[0056] The generation of the gridded atmospheric enhancement product employs a two-layer modeling strategy involving both fitting terms and residual terms. Specifically, the server calculates polynomial fitting coefficients based on the location of each reference station and its corresponding ionospheric and tropospheric delays; it then calculates the fitted atmospheric delay value for each reference station based on these coefficients, obtaining the atmospheric delay residual for each station; finally, it maps these residuals to preset grid nodes using spatial interpolation techniques, obtaining the fitting residuals for each grid node. The polynomial fitting coefficients and the fitting residuals for each grid node constitute a part of the gridded atmospheric enhancement product.

[0057] The server is also used to broadcast the uncorrected phase delay and the gridded atmospheric enhancement product to the user terminal. The server is also used to perform product quality checks on the uncorrected phase delay and gridded atmospheric enhancement product before broadcasting. The checks include verifying the time stability of the UPD product and the reasonableness of the residuals in the atmospheric enhancement product. If the checks fail (e.g., a jump in the UPD of a certain satellite or abnormal residuals in the grid nodes), the server can choose not to broadcast the abnormal portion of the corresponding product, or correct the atmospheric model and regenerate it.

[0058] The user terminal is used to receive and fuse PPP-B2b satellite-based corrections, the uncorrected phase delay, and the gridded atmospheric enhancement product to perform rapid ambiguity fixing and positioning calculation. Specifically, the user terminal performs the following steps: First, using the same method as the server for generating high-precision satellite orbits and clock biases, high-precision satellite orbits and clock biases for user terminals are generated based on broadcast ephemeris and PPP-B2b corrections.

[0059] Secondly, based on the polynomial fitting coefficients and grid node fitting residuals in the received grid atmospheric enhancement product, the atmospheric correction at the user's location is recovered. The user terminal then uses the grid node coordinates, polynomial coefficients, and grid residual information to calculate the atmospheric correction using a formula. Restore high-precision atmospheric corrections, among which This indicates the atmospheric delay obtained by the user. This represents the atmospheric delay calculated using the fitting coefficients; This represents the fitting residuals around the four grid nodes of the user distribution; while This indicates the distance between the user and the grid node.

[0060] Next, a non-differential, non-combined precise single-point positioning model is constructed, and the recovered atmospheric corrections are added as constraints to the observation equations. Kalman filtering is used at the user end for recursive solution, and the state vector includes position, receiver clock error, ionosphere, troposphere, and ambiguity parameters. External atmospheric correction constraints accelerate the convergence of the observation equations.

[0061] Finally, based on the received uncorrected phase delay, the ambiguity parameters are corrected, and the ambiguity is fixed using the least squares ambiguity decorrelation adjustment method. Specifically, the user terminal corrects the wide-lane floating-point ambiguity and narrow-lane floating-point ambiguity based on the received wide-lane and narrow-lane uncorrected phase delays; the corrected wide-lane ambiguity is fixed as an integer, and used as a constraint to fix the narrow-lane ambiguity as an integer; the fixed wide-lane and narrow-lane ambiguities are added as virtual observations to the observation equation for the final position calculation, obtaining a centimeter-level positioning solution.

[0062] Based on the same inventive concept as any of the foregoing embodiments, this embodiment of the invention also provides a server-side device, including a processor and a memory. The memory stores a computer program configured to be executed by the processor. When the processor executes the computer program, it implements the steps executed by the server in the aforementioned grid atmospheric modeling and PPP-RTK positioning method applicable to BeiDou PPP-B2b service.

[0063] Specifically, when the processor executes the computer program, it performs the following steps: combining broadcast ephemeris with PPP-B2b orbit and clock bias corrections, and generating high-precision satellite orbits and clock biases through benchmark unification processing; using regional benchmark station network observation data, constructing a non-differential, non-combined, precise single-point positioning model that takes into account specific satellite clock biases, and estimating the uncorrected phase delay compatible with the specific satellite clock biases based on the model; based on the uncorrected phase delay and the high-precision satellite orbit and clock biases, performing stepwise ambiguity fixation on the benchmark station network, extracting regional ionospheric delay and tropospheric delay, and generating grid atmospheric enhancement products through grid modeling; and broadcasting the uncorrected phase delay and the grid atmospheric enhancement products to the user terminal.

[0064] The server-side equipment can be deployed in the data processing center. By receiving broadcast ephemeris and PPP-B2b correction data, and combining them with observation data from the regional reference station network, it generates high-precision augmentation products and broadcasts them to the user terminal, providing users with high-precision positioning services.

[0065] In summary, the present invention first generates a high-precision satellite orbit and clock bias reference that is consistent with existing PPP-B2b services; then, on the server side, it implements a non-differential, non-combined PPP model that can take into account the error characteristics of PPP-B2b products, generating a gridded atmospheric and phase enhancement product that is highly compatible with the satellite-based service system, thereby achieving rapid ambiguity fixation on the user side.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A grid atmospheric modeling and PPP-RTK positioning method suitable for BeiDou PPP-B2b service, characterized in that, Applied to the server side, including: By combining broadcast ephemeris with PPP-B2b orbit and clock bias corrections, high-precision satellite orbits and clock biases are generated after benchmark unification processing. Using observation data from a regional reference station network, a non-differential, non-combined, precise single-point positioning model that takes into account specific satellite clock biases is constructed, and the uncorrected phase delay that is compatible with the specific satellite clock biases is estimated based on this model. Based on the uncorrected phase delay and the high-precision satellite orbit and clock error, the ambiguity of the reference station network is fixed step by step, the regional ionospheric delay and tropospheric delay are extracted, and grid atmospheric enhancement products are generated through grid modeling. The uncorrected phase delay and the gridded atmospheric enhancement product are broadcast to the user terminal to enable rapid ambiguity fixation and positioning calculation at the user terminal.

2. The grid atmospheric modeling and PPP-RTK positioning method applicable to BeiDou PPP-B2b service according to claim 1, characterized in that, The process of generating high-precision satellite orbits and clock biases through benchmark unification includes: The satellite orbit position generated by the broadcast ephemeris is corrected according to the PPP-B2b orbit correction number to obtain the corrected satellite orbit position. The satellite clock bias generated by the broadcast ephemeris is corrected according to the PPP-B2b clock bias correction to obtain the corrected satellite clock bias. By converting the clock bias frequency reference, the corrected satellite clock bias is unified to the frequency reference used by the PPP-B2b service.

3. The grid atmospheric modeling and PPP-RTK positioning method applicable to BeiDou PPP-B2b service according to claim 1, characterized in that, The construction of the non-differential, non-combined precise single-point positioning model that takes into account satellite-specific clock bias includes: introducing a satellite-specific clock bias correction term into the observation equation of the non-differential, non-combined precise single-point positioning to absorb the satellite-specific clock bias in the PPP-B2b product.

4. The grid atmospheric modeling and PPP-RTK positioning method applicable to BeiDou PPP-B2b service according to claim 3, characterized in that, The estimation of the uncorrected phase delay compatible with the specific clock bias of the satellite includes: Based on the non-differential non-combined precise single-point positioning model that takes into account the specific clock bias of the satellite, the wide-lane floating-point ambiguity and narrow-lane floating-point ambiguity of each reference station corresponding to each satellite are extracted. The specific clock bias of the satellite is jointly estimated with the wide-lane uncorrected phase delay and the narrow-lane uncorrected phase delay at the satellite end; Based on the wide-lane floating-point ambiguity and narrow-lane floating-point ambiguity of each reference station, the uncorrected phase delay of the wide-lane and the uncorrected phase delay of the narrow-lane at the satellite end are obtained by least squares estimation.

5. The grid atmospheric modeling and PPP-RTK positioning method applicable to BeiDou PPP-B2b service according to claim 1, characterized in that, The stepwise ambiguity fixing of the reference station network and the extraction of regional ionospheric and tropospheric delays include: Based on the wide-lane uncorrected phase delay in the uncorrected phase delay, the wide-lane floating-point ambiguity of each reference station is fixed to an integer. Using the fixed wide-lane ambiguity as a constraint, the narrow-lane floating-point ambiguity is refined, and combined with the uncorrected phase delay of the narrow-lane, the refined narrow-lane floating-point ambiguity is fixed to an integer to obtain the fixed base station solution. Based on the base station solution after the ambiguity is fixed stepwise, the ionospheric delay and tropospheric delay of each base station are extracted. The extracted ionospheric delay is compensated for by receiver-side hardware delay to obtain a clean ionospheric delay.

6. The grid atmospheric modeling and PPP-RTK positioning method applicable to BeiDou PPP-B2b service as described in claim 5, is characterized in that, The process of compensating for receiver-side hardware delay in the extracted ionospheric delay includes: By constructing an inter-station single-difference model to eliminate the influence of satellite-end hardware delay in the ionosphere, an estimation equation for receiver-end hardware delay is established. The constrained least squares method is used to solve the receiver hardware delay of each reference station; Based on the calculated receiver hardware delay, the extracted ionospheric delay is corrected to obtain a clean ionospheric delay.

7. The grid atmospheric modeling and PPP-RTK positioning method applicable to BeiDou PPP-B2b service according to claim 5, characterized in that, The generation of grid-based atmospheric enhancement products through grid modeling includes: A two-layer modeling strategy of fitting term plus residual term is adopted. Based on the location of each reference station and its corresponding ionospheric delay and tropospheric delay, the polynomial fitting coefficient of the regional atmospheric delay is calculated. The fitted atmospheric delay value of each reference station is calculated based on the polynomial fitting coefficients, and the atmospheric delay residual of each reference station is obtained based on the atmospheric delay value extracted from each reference station and the fitted atmospheric delay value. Using spatial interpolation, the atmospheric delay residuals of each reference station are mapped onto preset grid nodes to obtain the fitting residuals of each grid node. The polynomial fitting coefficients and the fitting residuals of each grid node are used as part of the grid atmospheric enhancement product.

8. A positioning system suitable for BeiDou PPP-B2b services, characterized in that, Including server-side and user-side; The server is used to combine broadcast ephemeris data with PPP-B2b orbit and clock bias corrections, and generate high-precision satellite orbits and clock biases through benchmark unification processing; using regional benchmark station network observation data, it constructs a non-differential, non-combined, precise single-point positioning model that takes into account specific satellite clock biases, and estimates the uncorrected phase delay compatible with the specific satellite clock biases based on this model; based on the uncorrected phase delay and the high-precision satellite orbits and clock biases, it performs stepwise ambiguity fixation on the benchmark station network, extracts regional ionospheric delay and tropospheric delay, and generates grid atmospheric enhancement products through grid modeling; and broadcasts the uncorrected phase delay and the grid atmospheric enhancement products to the user terminal. The user terminal is used to receive and fuse PPP-B2b satellite-based corrections, the uncorrected phase delay, and the grid atmospheric enhancement product to perform rapid ambiguity fixing and positioning calculation.

9. The positioning system for BeiDou PPP-B2b service according to claim 8, characterized in that, The user terminal performs rapid ambiguity fixing and localization calculation, including: Using the same method as the server-side generation of high-precision satellite orbits and clock biases, high-precision satellite orbits and clock biases for user terminals are generated based on broadcast ephemeris and PPP-B2b corrections. Based on the polynomial fitting coefficients and grid node fitting residuals in the received grid atmospheric enhancement product, the atmospheric correction at the user's location is recovered. A non-differential, non-combined, precise single-point positioning model is constructed, and the recovered atmospheric corrections are added as constraints to the observation equations. Based on the received uncorrected phase delay, the ambiguity parameters are corrected, and the ambiguity is fixed using the least squares ambiguity decorrelation adjustment method.

10. A server-side device, characterized in that, It includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the grid atmospheric modeling and PPP-RTK positioning method applicable to BeiDou PPP-B2b service as described in any one of claims 1 to 7.

Images