A method and device for B2b-PPP positioning based on Beidou-3

Abstract

The application discloses a B2b-PPP positioning method and equipment based on Beidou No.3, and relates to the technical field of satellite navigation high-precision positioning. The B2b-PPP positioning method based on Beidou No.3 comprises the following steps: acquiring state update PPP parameters; performing prior robustness processing and posterior robustness processing on the state update PPP parameters to obtain positioning calculation correlation data; and calculating a PPP smooth solution based on the positioning calculation correlation data. The technical scheme of the embodiment of the application improves the positioning precision of a sea ship, effectively improves the sailing safety, and promotes the development of intelligent sailing.

Description

A B2b-PPP positioning method and device based on BeiDou-3 Technical Field

[0001] This invention relates to the field of high-precision positioning technology for satellite navigation, and in particular to a B2b-PPP positioning method and device based on BeiDou-3. Background Technology

[0002] The BeiDou-3 satellite navigation system officially began providing navigation and positioning services in 2020. A key feature of the BeiDou-3 system is its B2b-PPP (B2b Precise Point Positioning) enhancement signal, which provides real-time, decimeter-level accuracy to designated areas. This precise point positioning service effectively eliminates reliance on ground communication networks and reference stations. Navigation and positioning terminals require only a single receiver to achieve precise positioning at any location. This advantage effectively expands the range of BeiDou's high-precision positioning services and compensates for the limited coverage of ground-based RTK (Real-time kinematic) positioning services, providing a new technological means for precise navigation and positioning of ships at sea.

[0003] B2b-PPP has significant advantages in positioning modes; however, PPP is highly sensitive to the quality of observations. In the uncertain observation environment of maritime ship applications, occasional interference or obstruction can lead to a deterioration in observation quality, ultimately affecting PPP convergence time and positioning accuracy. Summary of the Invention

[0004] This invention provides a B2b-PPP positioning method and equipment based on BeiDou-3 to solve the problem that abnormal observations seriously affect the positioning accuracy of B2b-PPP when positioning ships at sea.

[0005] According to one aspect of the present invention, a B2b-PPP positioning method based on BeiDou-3 is provided, comprising:

[0006] Get the status update PPP parameters;

[0007] The PPP parameters for state updates are subjected to prior robustness processing and posterior robustness processing to obtain the location solution associated data.

[0008] The PPP smoothing solution is calculated based on the location-based solution association data.

[0009] According to another aspect of the present invention, a B2b-PPP positioning device based on BeiDou-3 is provided, comprising:

[0010] The data acquisition module is used to acquire PPP status update parameters;

[0011] The robustness processing module is used to perform prior robustness processing and posterior robustness processing on the PPP parameters for state updates to obtain the location solution associated data;

[0012] The location solution calculation module is used to calculate the PPP smoothing solution based on the location solution association data.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and a memory communicatively connected to said at least one processor;

[0015] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the B2b-PPP positioning method based on BeiDou-3 as described in any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the B2b-PPP positioning method based on BeiDou-3 as described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the B2b-PPP positioning method based on BeiDou-3 as described in any embodiment of the present invention.

[0018] The technical solution of this invention obtains the state update PPP parameters, performs prior robustness processing and posterior robustness processing on the state update PPP parameters to obtain positioning solution association data, and then calculates the PPP smooth solution based on the positioning solution association data. This solution combines prior and posterior robustness to achieve gross error detection. Combined with the traditional B2b-PPP method, it effectively removes outlier observations and prevents them from participating in the positioning solution, thereby avoiding contamination of the state estimation and improving the robustness and stability of the solution results. This solves the problem that outlier observations seriously affect the positioning accuracy of B2b-PPP when positioning ships at sea, improves the positioning accuracy of ships at sea, effectively enhances navigation safety, and promotes the development of intelligent navigation.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 is a flowchart of a B2b-PPP positioning method based on BeiDou-3 provided in Embodiment 1 of the present invention;

[0022] Figure 2 is a flowchart of a B2b-PPP positioning method based on BeiDou-3 provided in Embodiment 2 of the present invention;

[0023] Figure 3 is a flowchart of the pre-test robustness treatment process;

[0024] Figure 4 is a schematic diagram of a B2b-PPP positioning device based on BeiDou-3 provided in Embodiment 3 of the present invention;

[0025] Figure 5 shows a schematic diagram of the structure of an electronic device that can be used to implement an embodiment of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "target," "initial," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] Example 1

[0029] Figure 1 is a flowchart of a B2b-PPP positioning method based on BeiDou-3 provided in Embodiment 1 of the present invention. This embodiment is applicable to the precise positioning of ships at sea. The method can be executed by a B2b-PPP positioning device based on BeiDou-3, which can be implemented in hardware and / or software and can be configured in an electronic device. As shown in Figure 1, the method includes:

[0030] Step 110: Obtain the status update PPP parameters.

[0031] The state-updated PPP parameters can be the current PPP parameters after the state update. The state-updated PPP parameters can include, but are not limited to, basic state parameters (receiver position, receiver clock bias), observation-related parameters (carrier phase ambiguity, pseudorange bias, etc.), error correction parameters (tropospheric delay parameters, ionospheric delay parameters), dynamic enhancement parameters (receiver velocity, receiver clock drift), and other auxiliary parameters (phase winding correction, tidal correction parameters).

[0032] In this embodiment of the invention, the state update PPP parameters can be determined according to the traditional B2b-PPP algorithm process.

[0033] Step 120: Perform prior robustness processing and posterior robustness processing on the state update PPP parameters to obtain the location solution association data.

[0034] Prior robustness processing can be used to screen or weight observed data before it is formally incorporated into the model calculation, using pre-designed testing methods or weighting functions to eliminate or weaken the influence of gross errors. Posterior robustness processing can be used after the model calculation is completed, by analyzing the statistical characteristics of residuals or innovation vectors to reverse-correct model parameters or weights, thereby eliminating the abnormal influences that were not fully processed by prior robustness. Location-related data can be data related to the calculation of the PPP floating-point solution.

[0035] Accordingly, when calculating the location solution association data based on the state update PPP parameters in the traditional B2b-PPP algorithm process, a prior robustness processing and a posteriori robustness processing steps can be added. That is, the state update PPP parameters are subjected to prior robustness processing and a posteriori robustness processing to obtain the location solution association data used to calculate the location.

[0036] Step 130: Calculate the PPP smoothing solution based on the location solution association data.

[0037] Among them, the PPP smooth solution can be the decimeter-level positioning result of the target, so as to achieve the decimeter-level positioning of the target.

[0038] In this embodiment of the invention, a PPP floating-point solution can be calculated based on the positioning solution association data, and then a PPP smooth solution can be calculated based on the PPP floating-point solution to achieve decimeter-level positioning of ships at sea.

[0039] The technical solution of this invention obtains the state update PPP parameters, performs prior robustness processing and posterior robustness processing on the state update PPP parameters to obtain positioning solution association data, and then calculates the PPP smooth solution based on the positioning solution association data. This solution combines prior and posterior robustness to achieve gross error detection. Combined with the traditional B2b-PPP method, it effectively removes outlier observations and prevents them from participating in the positioning solution, thereby avoiding contamination of the state estimation and improving the robustness and stability of the solution results. This solves the problem that outlier observations seriously affect the positioning accuracy of B2b-PPP when positioning ships at sea, improves the positioning accuracy of ships at sea, effectively enhances navigation safety, and effectively promotes the development of intelligent navigation.

[0040] Example 2

[0041] Figure 2 is a flowchart of a B2b-PPP positioning method based on BeiDou-3 provided in Embodiment 2 of the present invention. This embodiment is a specific implementation based on the above embodiment, providing a priori robustness processing and posterior robustness processing of the state update PPP parameters to obtain the positioning solution association data. As shown in Figure 2, the method includes:

[0042] Step 210: Obtain the status update PPP parameters.

[0043] Step 220: Calculate the PPP prior residuals based on the state-updated PPP parameters, and perform outlier removal and residual optimization on the state-updated PPP parameters based on the PPP prior residuals to obtain the PPP prior robust parameters.

[0044] The PPP prior residual can be the PPP residual calculated before prior robustness processing. The PPP residual is used to characterize the difference between the observed values ​​and the mathematical model predictions. For example, PPP residual = actual observed value (e.g., pseudorange, carrier phase) - model prediction value (theoretical value corrected for satellite orbit, clock error, atmospheric delay, etc.). The PPP prior robustness parameter can be the result of prior robustness processing for state-updated PPP parameters. Residual optimization processing can include, but is not limited to, stochastic model optimization, filtering algorithm feedback, and multi-system fusion methods.

[0045] In this embodiment of the invention, the PPP residual, i.e. the PPP prior residual, can be calculated based on the state-updated PPP parameters. If the PPP prior residual is greater than a preset residual threshold, it indicates that there is a high probability of abnormal observations. Then, cluster analysis is performed on the state-updated PPP parameters to identify outliers in the state-updated PPP parameters. The identified outliers are then removed, and the state-updated PPP parameters after removing outliers are subjected to residual optimization processing to obtain the PPP prior robust parameters.

[0046] In an optional embodiment of the present invention, outlier removal and residual optimization are performed on the state update PPP parameters based on the PPP prior residuals to obtain PPP prior robust parameters. This may include: removing pseudorange outliers and phase outliers in the state update PPP parameters when the PPP prior residuals exceed the limit and the residual statistical index exceeds the limit to obtain initial outlier screening data; and performing residual optimization on the initial outlier screening data based on the Kalman filter algorithm to obtain PPP prior robust parameters.

[0047] Among these, pseudorange outliers can be anomalous pseudoranges. Phase outliers can be anomalous phases. Residual statistical indicators can be indicators reflecting the statistical characteristics of residuals. For example, residual statistical indicators can include, but are not limited to, the mean and standard deviation of the PPP prior residuals. The outlier screening data can be the parameters remaining after removing outliers from the state-updated PPP parameters.

[0048] In this embodiment of the invention, if the PPP prior residual is greater than a preset residual threshold, it indicates that the PPP prior residual has exceeded the limit; if the residual statistical index is greater than a preset statistical index threshold, it indicates that the residual statistical index has exceeded the limit. When the PPP prior residual exceeds the limit and the residual statistical index exceeds the limit, cluster analysis is performed on the state-updated PPP parameters to identify pseudorange outliers and phase outliers in the state-updated PPP parameters. Then, the pseudorange outliers and phase outliers are removed to obtain initial outlier screening data. Based on the Kalman filter algorithm, residual optimization processing is performed on the initial outlier screening data to obtain the PPP prior robust parameters.

[0049] In an optional embodiment of the present invention, removing pseudorange and phase outliers from the state update PPP parameters to obtain preliminary outlier screening data may include: determining the redundancy of positioning data; determining the number of gross errors to be removed based on the redundancy of positioning data; and removing pseudorange and phase outliers from the state update PPP parameters based on the number of gross errors to obtain preliminary outlier screening data.

[0050] Among them, the redundancy of positioning data can be used to describe the degree of duplicate or redundant information in the PPP parameters for status updates. The number of gross errors removed can be the number of pseudorange outliers and phase outliers removed.

[0051] In this embodiment of the invention, the data redundancy of the state update PPP parameters can be used as the location data redundancy. Based on the location data redundancy, the number of gross error removals can be adaptively output to remove pseudorange outliers and phase outliers in the state update PPP parameters, thereby obtaining the initial screening data of outliers.

[0052] Optionally, a machine learning model can be used to learn the relationship between the redundancy of location data and the number of gross errors to be removed, thereby outputting a number of gross errors to be removed that is adapted to the current redundancy of location data.

[0053] In an optional embodiment of the present invention, before removing pseudorange outliers and phase outliers from the state update PPP parameters to obtain initial outlier screening data, the method may further include: determining outliers in the state update PPP parameters based on a clustering analysis strategy; and determining pseudorange outliers and phase outliers based on the outliers in the state update PPP parameters.

[0054] In this embodiment of the invention, the state update PPP parameters can be parsed based on a clustering analysis strategy to determine outliers in the state update PPP parameters. Then, the pseudoranges in the aforementioned outliers are taken as pseudorange outliers, and the phases in the aforementioned outliers are taken as phase outliers.

[0055] Figure 3 shows the flowchart of the pre-hoc robustness processing. As shown in Figure 3, the PPP prior residual is first calculated, and then it is determined whether the residual (i.e., the PPP prior residual) exceeds the limit. If the residual does not exceed the limit, Kalman filtering is performed on the state update PPP parameters. If the residual exceeds the limit, the mean and standard deviation of the PPP prior residual are further used to identify potential gross errors, that is, the mean and average of the pseudorange residual are calculated, and the mean and average of the phase residual are calculated. It is then determined whether the calculated mean and average exceed the limit. If they do not exceed the limit, Kalman filtering is further performed. If the aforementioned mean and average exceed the limit, pseudorange outliers and phase outliers are removed based on a clustering analysis strategy. After outlier removal, the reconstructed observation equation is calculated, and subsequent Kalman filtering is performed based on the reconstructed observation equation. The preset residual threshold used to determine whether the residual exceeds the limit is determined by prior residual characteristic analysis and statistical analysis of a large amount of experimental data.

[0056] When the standard deviation and mean exceed preset thresholds, it indicates the possible existence of outlier observations. Based on a clustering analysis strategy, observations with anomalous characteristics are considered outliers in the clusters. By calculating the standard deviation of different observation combinations, the combination with the smallest variance is selected for solution. That is, the combination with the smallest variance in the initial outlier screening data is then used for subsequent Kalman filtering. Simultaneously, by incorporating information such as the redundancy of the location data, the number of outlier removals is adaptively adjusted to ensure the robustness and effectiveness of the function model.

[0057] Step 230: Perform posterior robustness processing on the PPP prior robustness parameters to obtain the location solution association data.

[0058] In this embodiment of the invention, a posterior robustness algorithm can be selected to perform posterior robustness processing on the PPP prior robustness parameters to obtain the location solution association data.

[0059] In an optional embodiment of the present invention, performing posterior robustness processing on the PPP prior robustness parameters to obtain location solution association data may include: calculating the PPP posterior residual based on the PPP prior robustness parameters; and performing posterior robustness processing on the PPP prior robustness parameters when the PPP posterior residual exceeds the limit to obtain location solution association data.

[0060] Among them, the PPP posterior residual can be used to determine whether to perform posterior robustness processing on the PPP prior robust parameters.

[0061] In this embodiment of the invention, the PPP residual of the PPP prior robust parameter can be calculated to obtain the PPP posterior residual. If the PPP posterior residual is greater than a preset residual threshold, it indicates that the PPP posterior residual exceeds the limit. Then, the PPP prior robust parameter is subjected to posterior robust processing to obtain the location solution association data.

[0062] In an optional embodiment of the present invention, performing posterior robustness processing on the PPP prior robustness parameters to obtain location solution association data may include: performing posterior robustness processing on the PPP prior robustness parameters based on a robust estimation robustness algorithm to obtain location solution association data.

[0063] In this embodiment of the invention, the prior robustness parameters of PPP can be processed by posterior robustness based on a robust estimation robustness algorithm, and the result of the posterior robustness processing can be used as the location solution association data.

[0064] For example, when observing complex environments, although outliers cannot be completely avoided, robust estimation algorithms can be used to obtain optimal parameter estimates to minimize their impact. In robust estimation algorithms, parameter estimates are first calculated using the least squares method based on the mathematical model: ;in, These are parameter estimates; Design a matrix for the observation equation; It is a symmetric two-factor equivalent weight matrix; This represents the difference between the observed value and the approximate value.

[0065] The parameter estimates are iteratively updated using robust least squares until the th... Next and first The absolute value of the difference between the estimates obtained in the next iteration is less than or equal to the iteration threshold. Then stop iterating:

[0066] ;

[0067] Among them, superscript Indicates the first Next iteration, superscript Indicates the first The next iteration; This is the iteration threshold.

[0068] based on Solve for the final parameter estimate, based on Solve for the posterior residuals of PPP. This represents the post-hoc residual of PPP.

[0069] The weighted iterative method, as an important approach to robust estimation algorithms, relies heavily on the weight function. It's important to note that robust estimation algorithms utilize symmetric two-factor equivalent weight matrices. Replace the power array This demonstrates a connection with the power structure. The same correlation is specifically represented by its two-factor equivalent weight matrix as follows:

[0070] ;

[0071] in, Representation of weight array The Line number List the elements; weight matrix The weight matrix of the observations; Indicates the first Line number List an adaptive shrinkage factor, and satisfy... To more accurately describe the precision of observations, a shrinkage factor is used in practical applications. It can be finely divided into: ;

[0072] in, These are two constant threshold values. Represents the standardized residual. Represents the cofactor matrix The diagonal elements, Denotes the posterior variance factor. Indicates the number of redundant observations. express The i-th element.

[0073] Step 240: Calculate the PPP smoothing solution based on the location solution association data.

[0074] To verify the effectiveness of the above robustness strategy, static and dynamic experimental data were used to evaluate the optimized results. Without quality control using the BeiDou-3-based B2b-PPP positioning method, the positioning curve fluctuates in the static mode due to introduced gross errors; in the dynamic mode, weak filtering leads to reconvergence at certain times. By employing a robust pre- and post-test robustness algorithm, the convergence speed is accelerated to some extent, and the phenomena of flying points and reconvergence are suppressed. Furthermore, the improvement in B2b-PPP convergence time and positioning accuracy is significant in different scenarios.

[0075] The technical solution of this invention obtains state-updated PPP parameters, calculates PPP prior residuals based on these parameters, and removes outliers from the state-updated PPP parameters based on these residuals to obtain PPP prior robust parameters. Then, posterior robustness processing is applied to these PPP prior robust parameters to obtain positioning solution association data. PPP smoothing is then calculated based on this positioning solution association data. This solution combines prior and posterior robustness to achieve gross error detection. Combined with the traditional B2b-PPP method, it effectively removes outlier observations, preventing them from participating in the positioning solution and thus avoiding contamination of state estimation. This improves the robustness and stability of the solution results, solves the problem of outlier observations severely affecting the positioning accuracy of B2b-PPP when locating ships at sea, improves the accuracy of ship positioning at sea, effectively enhances navigation safety, and effectively promotes the development of intelligent navigation.

[0076] Example 3

[0077] Figure 4 is a schematic diagram of a B2b-PPP positioning device based on BeiDou-3 provided in Embodiment 3 of the present invention. As shown in Figure 4, the device includes:

[0078] Data acquisition module 310 is used to acquire PPP status update parameters;

[0079] The robustness processing module 320 is used to perform prior robustness processing and posterior robustness processing on the state update PPP parameters to obtain the location solution association data.

[0080] The location solution calculation module 330 is used to calculate the PPP smooth solution based on the location solution association data.

[0081] The technical solution of this invention obtains the state update PPP parameters, performs prior robustness processing and posterior robustness processing on the state update PPP parameters to obtain positioning solution association data, and then calculates the PPP smooth solution based on the positioning solution association data. This solution combines prior and posterior robustness to achieve gross error detection. Combined with the traditional B2b-PPP method, it effectively removes outlier observations and prevents them from participating in the positioning solution, thereby avoiding contamination of the state estimation and improving the robustness and stability of the solution results. This solves the problem that outlier observations seriously affect the positioning accuracy of B2b-PPP when positioning ships at sea, improves the positioning accuracy of ships at sea, effectively enhances navigation safety, and effectively promotes the development of intelligent navigation.

[0082] Optionally, the robustness processing module 320 is used to calculate the PPP prior residual based on the state-updated PPP parameters, and based on the PPP prior residual, to perform outlier removal and residual optimization processing on the state-updated PPP parameters to obtain the PPP prior robust parameters; and to perform posterior robustness processing on the PPP prior robust parameters to obtain the location solution association data.

[0083] Optionally, the robustness processing module 320 includes a prior robustness unit, used to remove pseudorange outliers and phase outliers in the state update PPP parameters when the PPP prior residual exceeds the limit and the residual statistical index exceeds the limit, to obtain initial outlier screening data; and to perform residual optimization processing on the initial outlier screening data based on the Kalman filter algorithm to obtain the PPP prior robustness parameters.

[0084] Optionally, the robustness processing module 320 includes a posterior robustness unit, used to calculate the PPP posterior residual based on the PPP prior robustness parameters; when the PPP posterior residual exceeds the limit, to perform posterior robustness processing on the PPP prior robustness parameters to obtain the location solution association data.

[0085] Optionally, the robustness processing module 320 is used to perform posterior robustness processing on the PPP prior robustness parameters based on a robust estimation robustness algorithm to obtain the location solution association data.

[0086] Optionally, a priori robust unit is used to determine the redundancy of the positioning data; based on the redundancy of the positioning data, the number of gross errors to be removed is determined, and based on the number of gross errors to be removed, pseudorange outliers and phase outliers in the state update PPP parameters are removed to obtain the initial screening data of outliers.

[0087] Optionally, the B2b-PPP positioning device based on BeiDou-3 further includes an outlier determination module, used to determine outliers in the state update PPP parameters based on a clustering analysis strategy; and to determine the pseudorange outliers and the phase outliers based on the outliers in the state update PPP parameters.

[0088] The B2b-PPP positioning device based on BeiDou-3 provided in the embodiments of the present invention can execute the B2b-PPP positioning method based on BeiDou-3 provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0089] Example 4

[0090] Figure 5 illustrates a schematic diagram of an electronic device that can be used to implement embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, and servers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, receivers, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0091] As shown in Figure 5, the electronic device 10 includes at least one processor 11 and a memory, such as ROM 12 or RAM 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer programs stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An I / O interface 15 is also connected to the bus 14. The ROM 12 is a read-only memory, the RAM 13 is a random access memory, and the I / O interface 15 is an input / output interface.

[0092] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0093] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the B2b-PPP positioning method based on BeiDou-3.

[0094] In some embodiments, the BeiDou-3-based B2b-PPP positioning method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the BeiDou-3-based B2b-PPP positioning method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the BeiDou-3-based B2b-PPP positioning method by any other suitable means (e.g., by means of firmware).

[0095] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0096] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0097] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0098] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0099] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0100] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS servers, such as high management difficulty and weak business scalability.

[0101] This application also discloses a computer program product, which includes a computer program that, when executed by a processor, implements the B2b-PPP positioning method based on BeiDou-3 provided in any embodiment of this application. This program product shares the same inventive concept as the B2b-PPP positioning method based on BeiDou-3 disclosed in the embodiments of this application, and therefore will not be described in detail here.

[0102] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0103] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A B2b-PPP positioning method based on BeiDou-3, characterized in that, include: Get the status update parameters of the Precise Point Positioning Service (PPP); The PPP parameters for state updates are subjected to prior robustness processing and posterior robustness processing to obtain the location solution associated data. Based on the location-based associated data, calculate the PPP smoothing solution; The process involves performing prior and posterior robustness processing on the state update PPP parameters to obtain location solution association data. This includes: calculating the PPP prior residual based on the state update PPP parameters; performing outlier removal and residual optimization processing on the state update PPP parameters based on the PPP prior residual to obtain PPP prior robustness parameters; performing posterior robustness processing on the PPP prior robustness parameters to obtain the location solution association data; and performing outlier removal and residual optimization processing on the state update PPP parameters based on the PPP prior residual to obtain PPP prior robustness parameters, including: when the PPP prior residual exceeds a limit and the residual statistical index exceeds a limit, processing the state update PPP parameters... The pseudorange and phase outliers in the state-updated PPP parameters are removed to obtain initial outlier screening data. The residual statistics include the mean and standard deviation of the PPP prior residuals. Based on a Kalman filter algorithm, residual optimization processing is performed on the initial outlier screening data to obtain the PPP prior robust parameters. The process of removing pseudorange and phase outliers from the state-updated PPP parameters to obtain initial outlier screening data includes: determining the redundancy of the positioning data; determining the number of gross errors to be removed based on the redundancy; and removing pseudorange and phase outliers from the state-updated PPP parameters based on the number of gross errors to obtain the initial outlier screening data.

2. The B2b-PPP positioning method based on BeiDou-3 according to claim 1, characterized in that, The process of performing posterior robustness processing on the PPP prior robustness parameters to obtain the location solution association data includes: calculating the PPP posterior residual based on the PPP prior robustness parameters; and performing posterior robustness processing on the PPP prior robustness parameters when the PPP posterior residual exceeds the limit to obtain the location solution association data.

3. The B2b-PPP positioning method based on BeiDou-3 according to claim 2, characterized in that, The location calculation association data is obtained by performing posterior robustness processing on the PPP prior robustness parameters based on a robust estimation robustness algorithm.

4. The B2b-PPP positioning method based on BeiDou-3 according to claim 1, characterized in that, Before removing pseudorange and phase outliers from the state-updated PPP parameters to obtain initial outlier screening data, the method further includes: determining outliers in the state-updated PPP parameters based on a clustering analysis strategy; and determining the pseudorange and phase outliers based on the outliers in the state-updated PPP parameters.

5. An electronic device, characterized in that, The electronic device includes: at least one processor, and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to execute the B2b-PPP positioning method based on BeiDou-3 as described in any one of claims 1-4.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the B2b-PPP positioning method based on BeiDou-3 as described in any one of claims 1-4.

7. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the B2b-PPP positioning method based on BeiDou-3 according to any one of claims 1-4.

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