A method and system for GNSS multi-frequency precise point positioning stochastic model

Abstract

The application discloses a GNSS multi-frequency precise point positioning random model method and system, which comprises the following steps: solving multi-frequency ionosphere-free combination difference values according to multi-frequency phase and pseudo-range observation values; removing the influence of ambiguity and inter-frequency clock bias deviation in the determined phase and pseudo-range ionosphere-free combination difference values to obtain residual values of multi-frequency phase and pseudo-range ionosphere-free combination difference values respectively; calculating the root mean square errors of the phase and pseudo-range ionosphere-free combination difference values respectively based on the residual values, taking the root mean square errors as standard deviations, determining the weight ratio of the phase and pseudo-range observation values, and constructing a random model for precise point positioning; and accurately describing the precision relationship between the pseudo-range and phase observation values in precise point positioning, shortening the convergence time of GNSS precise point positioning and improving the result precision.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of satellite navigation and positioning technology, and in particular to a GNSS multi-frequency precise point positioning random model method and system. BACKGROUND

[0002] Global Navigation Satellite System (GNSS) is a satellite positioning system that uses BDS (BeiDou Navigation Satellite System), GPS (Global Positioning System), GLONASS (Global Navigation Satellite System), and GALILEO (Galileo Satellite Navigation System) to provide real-time, all-weather navigation and positioning services worldwide, in combination with regional differential and wide-area augmentation systems.

[0003] In GNSS precise point positioning data processing, least squares is used to solve the position, phase ambiguity, troposphere delay, and receiver clock bias parameters. The least squares solution is related to the function model and random model used. An appropriate random model is a prerequisite for high-precision GNSS precise point positioning data processing. The random model is generally composed of variance factors and variances. In current GNSS precise point positioning, the variances corresponding to phase and pseudorange observations generally use a constant ratio, such as 1:10000. This ignores the differences in GNSS phase and pseudorange observation accuracy in different environments and at different times. There are also zero baseline and short baseline GNSS phase and pseudorange observation relative accuracy evaluations, which are difficult to meet the needs of precise point positioning using single station observations. SUMMARY

[0004] This section is intended to summarize some aspects of the embodiments of the present application and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of the specification to avoid obscuring the purpose of this section, the abstract, and the title, and such simplifications or omissions cannot be used to limit the scope of the present application.

[0005] In view of the above existing problems, the present application is proposed.

[0006] Therefore, the present application provides a GNSS multi-frequency precise point positioning random model method to solve the problem of not considering the influence of time and space environment or not being suitable for single station precise point positioning scenarios.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] In a first aspect, the present invention provides a method for a GNSS multi-frequency precise single-point positioning stochastic model, comprising:

[0009] Based on the observed values ​​of multi-frequency phase and pseudorange, the multi-frequency non-ionospheric combination difference is solved;

[0010] Based on the determined phase and pseudorange non-ionospheric combination difference, the effects of ambiguity and inter-frequency clock bias are removed to obtain the residuals of the multi-frequency phase and pseudorange non-ionospheric combination difference values.

[0011] Based on the residuals, the root mean square error of the phase and pseudorange combined difference values ​​without ionosphere is calculated respectively, and the root mean square error is used as the standard deviation to determine the weight ratio of the phase and pseudorange observations, and a stochastic model for precise single-point positioning is constructed.

[0012] As a preferred embodiment of the GNSS multi-frequency precise single-point positioning stochastic model method described in this invention, the multi-frequency non-ionospheric combination difference is expressed as:

[0013] DIF(L1,L2,L5)=L1 / L2-L1 / L5

[0014] DIF(P1,P2,P5) = P1 / P2 - P1 / P5

[0015] In the formula, DIF(L1,L2,L5) is the difference between the phase ionosphere-free delay combinations L1 / L2 and L1 / L5; DIF(P1,P2,P5) is the difference between the pseudorange ionosphere-free delay combinations P1 / P2 and P1 / P5.

[0016] As a preferred embodiment of the GNSS multi-frequency precise single-point positioning stochastic model method described in this invention, the residual formula for the phase-corresponding ionospheric combination difference is expressed as:

[0017]

[0018] In the formula, the superscript 'r' represents the station number, the subscript 's' represents the observed satellite, 'g' represents the corresponding navigation system, and 'j' represents the observation epoch. The residual of the difference in phase of multi-frequency non-ionospheric combinations, ifcb g,s This refers to the IFCB corresponding to the satellite.

[0019] As a preferred embodiment of the GNSS multi-frequency precise single-point positioning stochastic model method described in this invention, the residual formula for the pseudorange corresponding to the ionospheric combination difference is expressed as:

[0020]

[0021] wherein r in superscript represents station number, s in subscript represents observed satellite, g represents corresponding navigation system, and j represents observed epoch, residual of difference value of multi-frequency ionosphere-free combination of pseudorange observation, ifcb g,s and ifcb g,r respectively are IFCB corresponding to satellite and receiver.

[0022] As a preferred scheme of the method for GNSS multi-frequency precise point positioning random model, wherein the formula of calculating root mean square error is expressed as:

[0023]

[0024]

[0025] wherein, respectively are root mean square errors of phase observation and pseudorange observation, and m represents observed epoch number.

[0026] As a preferred scheme of the method for GNSS multi-frequency precise point positioning random model, wherein the weight ratio of phase observation is expressed as:

[0027]

[0028] wherein, is weight corresponding to phase observation, and θ j is elevation angle of satellite to station.

[0029] As a preferred scheme of the method for GNSS multi-frequency precise point positioning random model, wherein the weight ratio of pseudorange observation is expressed as:

[0030]

[0031] wherein, is weight corresponding to pseudorange observation, and θ j is elevation angle of satellite to station.

[0032] In a second aspect, the application provides a system for GNSS multi-frequency precise point positioning random model, comprising:

[0033] An acquisition module is configured to solve multi-frequency ionosphere-free combination difference value according to multi-frequency phase and pseudorange observation values.

[0034] A calculation module is configured to remove influence of ambiguity and inter-frequency clock bias deviation based on the determined phase, pseudorange ionosphere-free combination difference value, and obtain residual of multi-frequency phase, pseudorange ionosphere-free combination difference value, respectively.

[0035] constructing a random model for precise point positioning based on the phase and pseudorange observation values.

[0036] In a third aspect, the present application provides a computing device, comprising:

[0037] a memory and a processor;

[0038] The memory is configured to store computer-executable instructions, and the processor is configured to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the GNSS multi-frequency precise point positioning random model method.

[0039] In a fourth aspect, the present application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the GNSS multi-frequency precise point positioning random model method.

[0040] The present application has the following beneficial effects: The present application breaks through the defects of traditional GNSS pseudorange and phase observation constant variance for constructing a GNSS precise point positioning random model, and adopts multi-frequency GNSS to calculate phase and pseudorange observation variances, thereby constructing a GNSS precise point positioning random model for estimating changes in space-time environment, so as to improve the positioning accuracy of precise point positioning. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor. Among them:

[0042] Figure 1 A basic flowchart of a GNSS multi-frequency precise point positioning random model method provided by an embodiment of the present application;

[0043] Figure 2 A root mean square error graph of phase and pseudorange observation values corresponding to different satellites in a GNSS multi-frequency precise point positioning random model method provided by an embodiment of the present application;

[0044] Figure 3 A precise point positioning position result graph in a GNSS multi-frequency precise point positioning random model method provided by an embodiment of the present application;

[0045] Figure 4 A precise point positioning position change over time result map of a GNSS multi-frequency precise point positioning random model method provided for an embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to make the above objectives, features and advantages of the present application more apparent, specific embodiments of the present application are described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0047] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced without the specific details, other than those described herein, and it is understood that the present application is not limited to the embodiments described herein. In other instances, well-known methods, procedures, components, and networks have not been described in detail as not to unnecessarily obscure aspects of the present application.

[0048] Secondly, the "one embodiment" or "embodiment" referred to herein can include specific features, structures or characteristics in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or alternative embodiment that excludes other embodiments.

[0049] The present application is described in detail with reference to the accompanying drawings. In the detailed description of the embodiments of the present application, the cross-sectional view of the device structure is partially enlarged without the general proportion for the convenience of description, and the schematic diagram is only an example, which should not limit the scope of protection of the present application herein. In addition, three-dimensional spatial dimensions including length, width and depth should be included in actual manufacture.

[0050] Meanwhile, in the description of the present application, it should be noted that the terms "up, down, in and out" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the indicated device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first, second or third" are only for the purpose of description, and cannot be understood as indicating or implying relative importance.

[0051] Unless otherwise defined, the terms "mounting, connecting, associating" in the present application should be interpreted broadly, for example: it can be fixed connection, detachable connection or integral connection; it can also be mechanical connection, electrical connection or direct connection, it can also be indirectly connected through intermediate medium, or it can be the internal communication of two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0052] Embodiment 1

[0053] Reference Figure 1 For an embodiment of the present application, a GNSS multi-frequency precise point positioning random model method is provided, as shown in Figure 1 , comprising:

[0054] S1: according to the observation values of multi-frequency phase and pseudo-range, the multi-frequency ionosphere-free combination difference is solved;

[0055] Further, using a multi-frequency receiver for observation, multi-frequency phase observations L1, L2, L5 and multi-frequency pseudo-range observations P1, P2, P5 are obtained.

[0056] It should be noted that considering the influence of receiver, time and space environment on the observation value, the actual observation value is used to determine the weight ratio of each satellite phase and pseudo-range.

[0057] S2: based on the determined phase, pseudo-range ionosphere-free combination difference, the influence of ambiguity and inter-frequency clock bias deviation is removed, and the residual of the multi-frequency phase, pseudo-range ionosphere-free combination difference is obtained respectively;

[0058] Further, the phase and pseudo-range multi-frequency observation values of 1, 2 frequency bands and 1, 5 frequency bands of each epoch are respectively composed into ionosphere-free combinations, and the difference of the two frequency band combinations is solved and expressed as:

[0059] DIF(L1,L2,L5)=L1 / L2-L1 / L5

[0060] DIF(P1,P2,P5)=P1 / P2-P1 / P5

[0061] In the formula, DIF(L1,L2,L5) is the difference value of phase ionosphere-free delay combination L1 / L2, L1 / L5; DIF(P1,P2,P5) is the difference value of pseudo-range ionosphere-free delay combination P1 / P2, P1 / P5.

[0062] Further, the residual formula of phase corresponding ionosphere-free combination difference value is expressed as:

[0063]

[0064] wherein r in superscript represents station number, s in subscript represents observed satellite, g represents corresponding navigation system, and j represents observation epoch, is the residual of the difference of the multi-frequency ionosphere-free combination of phase, g,s is the IFCB corresponding to the satellite.

[0065] Further, the residual formula of the pseudo-range corresponding to the ionosphere-free combination difference is:

[0066]

[0067] wherein r in superscript represents station number, s in subscript represents observed satellite, g represents corresponding navigation system, and j represents observation epoch, is the residual of the difference of the multi-frequency ionosphere-free combination of pseudo-range, g,s is the IFCB corresponding to the satellite and the receiver respectively. g,r is the IFCB corresponding to the satellite and the receiver respectively.

[0068] S3: based on the residuals, the root mean square errors of the phase and pseudo-range ionosphere-free combination difference values are calculated respectively, and the root mean square errors are taken as standard deviations to determine the weight ratio of the phase and pseudo-range observation values, and a random model for precise point positioning is constructed.

[0069] Further, the formula for calculating the root mean square error is:

[0070]

[0071]

[0072] wherein, are the root mean square errors of the phase and pseudo-range observation values respectively, and m represents the number of observed epochs.

[0073] Further, the weight ratio of the phase observation value is:

[0074]

[0075] wherein, is the weight corresponding to the phase observation value, and θ j is the elevation angle of the satellite to the station.

[0076] Further, the weight ratio of the pseudo-range observation value is:

[0077]

[0078] wherein, is the weight corresponding to the pseudo-range observation value, and θ j is the elevation angle of the satellite to the station.

[0079] It should be noted that the least squares is used in the GNSS precise point positioning data processing to solve the position, phase ambiguity, troposphere delay and receiver clock difference parameters. The least squares solution is related to the function model and random model. The appropriate random model is the premise of high-precision GNSS precise point positioning data processing.

[0080] The above is a schematic scheme of the GNSS multi-frequency precise point positioning random model method of the embodiment. It should be noted that the technical scheme of the GNSS multi-frequency precise point positioning random model system belongs to the same concept as the technical scheme of the GNSS multi-frequency precise point positioning random model method described above. The technical scheme of the GNSS multi-frequency precise point positioning random model system in the embodiment is not described in detail. Please refer to the description of the technical scheme of the GNSS multi-frequency precise point positioning random model method described above.

[0081] The GNSS multi-frequency precise point positioning random model system in the embodiment includes:

[0082] The acquisition module is configured to solve the multi-frequency ionosphere-free combination difference value according to the observation values of the multi-frequency phase and pseudo-range.

[0083] The calculation module is configured to remove the influence of the ambiguity and inter-frequency clock bias from the determined phase and pseudo-range ionosphere-free combination difference value, and obtain the residual of the multi-frequency phase and pseudo-range ionosphere-free combination difference value, respectively.

[0084] The construction module is configured to calculate the root mean square error of the phase and pseudo-range ionosphere-free combination difference value based on the residual, and determine the weight ratio of the phase and pseudo-range observation value by taking the root mean square error as the standard deviation, and construct the random model for precise point positioning.

[0085] The embodiment also provides a computing device suitable for the GNSS multi-frequency precise point positioning random model, which includes:

[0086] The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions to implement the GNSS multi-frequency precise point positioning random model method proposed in the above embodiment.

[0087] The embodiment also provides a storage medium having a computer program stored thereon. The program is executed by the processor to implement the GNSS multi-frequency precise point positioning random model method proposed in the above embodiment.

[0088] The storage medium proposed in this embodiment and the method for implementing a random model for GNSS multi-frequency precise single-point positioning proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0089] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0090] Example 2

[0091] Reference Figures 2-4 As an embodiment of the present invention, a method for constructing a GNSS precise single-point positioning stochastic model using multi-frequency observations is provided. To verify its beneficial effects, real data is provided for scientific demonstration.

[0092] This experiment used multi-frequency observations from the JFNG station over three days (March 1-3, 2022, GPS time) to construct a stochastic model for precise single-point positioning in GNSS.

[0093] For each satellite's phase and pseudorange observations, calculate the respective multi-frequency ionospheric combination difference DIF(L1,L2,L5) and DIF(P1,P2,P5); the residuals and root mean square errors of the multi-frequency ionospheric combination difference are shown in the figure. Figure 2 As shown, the left column represents the root mean square error of the phase observations, and the right column represents the root mean square error of the pseudorange observations, in meters. The random model is numbered to compare it with other random models: #1 is a constant random model with a phase to pseudorange observation weight ratio of 100:1; #2 is the random model described in this patent. Precise single-point positioning of GNSS was performed using both random models, and the positioning errors are as follows: Figure 3 As shown, the average convergence time is as follows Figure 4 As shown;

[0094] It can be seen that the method for constructing a GNSS precise point positioning random model by using multi-frequency observation can make up for the defects that the existing random model does not consider time and space environment changes or is not suitable for single station precise point positioning scenarios, and is helpful to improve GNSS precise point positioning accuracy and parameter convergence.

[0095] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and all should be covered in the scope of the claims of the present application.

Claims

1. A method for a GNSS multi-frequency precise single-point positioning stochastic model, characterized in that, include: Based on the observed values ​​of multi-frequency phase and pseudorange, the multi-frequency non-ionospheric combination difference is solved; Based on the determined phase and pseudorange non-ionospheric combination difference, the effects of ambiguity and inter-frequency clock bias are removed to obtain the residuals of the multi-frequency phase and pseudorange non-ionospheric combination difference values. Based on the residuals, the root mean square error of the phase and pseudorange combined difference values ​​without ionosphere is calculated respectively, and the root mean square error is used as the standard deviation to determine the weight ratio of the phase and pseudorange observations, and a stochastic model for precise single-point positioning is constructed. The multi-frequency non-ionosphere combination difference is expressed as: DIF(L1,L2,L5)=L1 / L2-L1 / L5 DIF(P1,P2,P5) = P1 / P2 - P1 / P5 In the formula, DIF(L1,L2,L5) is the difference between the phase ionosphere-free delay combinations L1 / L2 and L1 / L5; DIF(P1,P2,P5) is the difference between the pseudorange ionosphere-free delay combinations P1 / P2 and P1 / P5. The residual formula for the phase-free ionosphere combination difference is expressed as: In the formula, the superscript 'r' represents the station number, the subscript 's' represents the observed satellite, 'g' represents the corresponding navigation system, and 'j' represents the observation epoch. The residual of the difference in phase of multi-frequency non-ionospheric combinations, ifcb g,s For the IFCB corresponding to the satellite; The residual formula for the pseudo-range non-ionosphere combination difference is expressed as follows: In the formula, the superscript 'r' represents the station number, the subscript 's' represents the observed satellite, 'g' represents the corresponding navigation system, and 'j' represents the observation epoch. The residual of the difference between multiple frequency ionospheric combinations of pseudorange observations, ifcb g,s With ifcb g,r These are the IFCBs corresponding to the satellite and the receiver, respectively. The root mean square error formula is expressed as follows: In the formula, denoted as root mean square errors for phase observations and pseudorange observations, respectively, and m represents the number of epochs observed.

2. The method for GNSS multi-frequency precise single-point positioning stochastic model as described in claim 1, characterized in that: The weighting ratio of the phase observations is expressed as follows: In the formula, The weights corresponding to the phase observations, θ j The elevation angle of the satellite to the station.

3. The method for GNSS multi-frequency precise single-point positioning stochastic model as described in claim 2, characterized in that: The weight ratio of the pseudorange observations is expressed as follows: In the formula, The weights corresponding to the pseudorange observations, θ j The elevation angle of the satellite to the station.

4. A system for a GNSS multi-frequency precise single-point positioning stochastic model, characterized in that, The method of claim 1 is used, comprising: The acquisition module is used to solve for the multi-frequency non-ionospheric combination difference based on the observations of multi-frequency phase and pseudorange; The calculation module is used to remove the influence of ambiguity and inter-frequency clock difference based on the determined phase and pseudorange non-ionospheric combination difference values, and obtain the residuals of the multi-frequency phase and pseudorange non-ionospheric combination difference values ​​respectively. A construction module is used to calculate the root mean square error of the phase and pseudorange combined difference values ​​without ionosphere based on the residuals, and to determine the weight ratio of the phase and pseudorange observations by using the root mean square error as the standard deviation, thereby constructing a stochastic model for precise single-point positioning.

5. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the GNSS multi-frequency precise single-point positioning stochastic model method according to any one of claims 1 to 3.

6. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the GNSS multi-frequency precise single-point positioning stochastic model method according to any one of claims 1 to 3.

Images