A method for wide-area tropospheric modeling fusing navigation satellites and weather models

Abstract

The application discloses a wide-area troposphere modeling method fusing navigation satellites and meteorological models, and relates to the technical field of satellite positioning. The server method comprises the following steps: in a global navigation satellite ground observation station distribution missing area, after obtaining first troposphere zenith wet delay through meteorological data provided by a global numerical weather forecast model, the Akima interpolation algorithm is used for interpolation to generate second troposphere zenith wet delay synchronous with the global navigation satellite ground observation station; after a virtual reference station meeting the requirements is constructed according to a preset principle, third troposphere zenith wet delay corresponding to the second troposphere zenith wet delay is obtained; fourth troposphere zenith wet delay estimated by the global navigation satellite ground observation station and the third troposphere zenith wet delay are fused, the improved troposphere polynomial fitting model is modeled by using the fused troposphere zenith wet delay and the improved troposphere polynomial fitting model, target coefficients of the improved troposphere polynomial fitting model suitable for satellite-side broadcasting are determined, and the target coefficients are used by a user side.

Description

Technical Field

[0001] This invention relates to the field of satellite positioning technology, and relates to, but is not limited to, a wide-area tropospheric modeling method that integrates navigation satellites and meteorological models. Background Technology

[0002] Accurate modeling of tropospheric delay is crucial for high-precision applications of Global Navigation Satellite Systems (GNSS) in geodetic positioning, navigation, and timing. The troposphere, especially its wet delay component, introduces signal delay into GNSS Precise Point Positioning (PPP), which, if not effectively mitigated, significantly reduces positioning accuracy. However, the sparse spatial distribution of GNSS ground observation stations limits the construction of high-resolution tropospheric delay fields, particularly in areas with complex terrain, near coastlines, or lacking sufficient observation infrastructure. This issue underscores the importance of spatial resolution and accuracy in tropospheric delay estimation.

[0003] In related technologies, the total zenith delay is calculated from observation data of global navigation satellite ground observation stations, and then the zenith dry delay calculated by meteorological parameters and models is subtracted to obtain the zenith wet delay. However, this method depends on the quality of GNSS observation data and data processing strategies. When global navigation satellite ground observation stations are sparse or data is interrupted, the results cannot be obtained, and it is difficult to calculate independently and accurately in areas without meteorological sensors.

[0004] Therefore, how to calculate the zenith wet delay in different regions more accurately and effectively has become an urgent problem to be solved. Summary of the Invention

[0005] In view of this, the present invention provides a wide-area tropospheric modeling method that integrates navigation satellites and meteorological models, solving the problem that related technologies cannot accurately and effectively calculate the zenith wet delay in different regions.

[0006] According to a first aspect of the present invention, a wide-area tropospheric modeling method integrating navigation satellites and meteorological models is provided, applied to a server, comprising:

[0007] In areas where the distribution of global navigation satellite ground observation stations is missing, the first tropospheric zenith wet delay is obtained through meteorological data provided by the global numerical weather prediction model. The first tropospheric zenith wet delay is then interpolated using the Akima interpolation algorithm to generate a second tropospheric zenith wet delay that is synchronized with the global navigation satellite ground observation stations.

[0008] A virtual reference station that meets the requirements is constructed according to the preset principles, and the third tropospheric zenith wet delay corresponding to the virtual reference station is obtained through the second tropospheric zenith wet delay.

[0009] The fourth tropospheric zenith wet delay and the third tropospheric zenith wet delay estimated by the global navigation satellite ground observation station are fused to obtain the fused tropospheric zenith wet delay; and the fused tropospheric zenith wet delay and the improved tropospheric polynomial fitting model are used to model and determine the target coefficients of the improved tropospheric polynomial fitting model suitable for satellite broadcasting.

[0010] The target coefficients are broadcast via satellite for use by users.

[0011] Optionally, the improved tropospheric polynomial fitting model is expressed by the following formula:

[0012] ;

[0013] In the above formula, For the fused tropospheric zenith wet delay, The water vapor scale factor. , , , , and All are variable parameters. For elevation, This represents the latitude deviation corresponding to the ground observation stations of global navigation satellites. This represents the longitude deviation corresponding to the ground observation station of the global navigation satellite system.

[0014] Optionally, the step of modeling using the fused tropospheric zenith wet delay and the improved tropospheric polynomial fitting model to determine the target coefficients of the improved tropospheric polynomial fitting model suitable for satellite-end broadcasting includes:

[0015] Based on the fused tropospheric zenith wet delay, the least squares method is used for parameter estimation. During the estimation process, outliers are removed by median detection to obtain the initial tropospheric polynomial fitting model.

[0016] When the initial tropospheric polynomial fitting model meets the preset conditions, the coefficients of the initial tropospheric polynomial fitting model are used as the target coefficients for the current period; otherwise, the coefficients of the initial tropospheric polynomial fitting model from the previous period are used as the target coefficients.

[0017] Optionally, before fusing the fourth tropospheric zenith wet delay and the third tropospheric zenith wet delay estimated by global navigation satellite ground observation stations to obtain the fused tropospheric zenith wet delay, the method further includes:

[0018] By utilizing the non-differential non-combination precise single-point positioning ambiguity fixing technique, the fourth tropospheric zenith wet delay of the global navigation satellite ground observation station is estimated in real time.

[0019] Optionally, the preset principles include: setting virtual reference stations based on the distribution of global navigation satellite ground observation stations and terrain features, ensuring that the distance between the virtual reference station and the global navigation satellite ground observation station does not exceed a fixed threshold, and maintaining the height difference between different global navigation satellite ground observation stations within a preset height.

[0020] Optionally, the step of interpolating the first tropospheric zenith wet delay using the Akima interpolation algorithm to generate a second tropospheric zenith wet delay synchronized with the global navigation satellite ground observation station includes:

[0021] The Akima interpolation algorithm is used to interpolate the first tropospheric zenith wet delay at discrete times in both spatial and temporal dimensions, resulting in the second tropospheric zenith wet delay that matches the observation time of the global navigation satellite ground observation station.

[0022] According to a second aspect of the present invention, a wide-area tropospheric modeling method integrating navigation satellites and meteorological models is provided, applied to a user terminal, the method comprising:

[0023] Receive target coefficients broadcast from the satellite;

[0024] The local zenith wet delay is calculated using the target coefficients received, based on the latitude, longitude, and elevation of the user's location.

[0025] According to a third aspect of the present invention, an electronic device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus; the memory is used to store at least one executable instruction, wherein the executable instruction causes the processor to perform an operation corresponding to the method described in the first or second aspect.

[0026] According to the scheme provided in the embodiments of the present invention, in areas where the distribution of global navigation satellite ground observation stations is missing, a first tropospheric zenith wet delay is obtained through meteorological data provided by a global numerical weather prediction model. The first tropospheric zenith wet delay is then interpolated using the Akima interpolation algorithm to generate a second tropospheric zenith wet delay synchronized with the global navigation satellite ground observation stations. A virtual reference station meeting the requirements is constructed according to preset principles, and a third tropospheric zenith wet delay corresponding to the virtual reference station is obtained through the second tropospheric zenith wet delay. The fourth tropospheric zenith wet delay estimated by the global navigation satellite ground observation stations and the third tropospheric zenith wet delay are fused to obtain the fused tropospheric zenith wet delay. The fused tropospheric zenith wet delay and an improved tropospheric polynomial fitting model are used for modeling to determine the target coefficients of the improved tropospheric polynomial fitting model suitable for satellite broadcasting. The target coefficients are then broadcast via satellite for user access. In this process, in areas lacking ground-based global navigation satellite (GNSS) observation stations, corresponding virtual reference stations are constructed according to pre-defined principles. Based on numerical meteorological models, the tropospheric zenith wet delay of these virtual reference stations is obtained. By fusing the estimated tropospheric zenith wet delay from GNSS ground-based observation stations with the corresponding tropospheric zenith wet delay from the virtual reference stations, and combining this with an improved tropospheric polynomial fitting model, target coefficients suitable for satellite broadcasting are generated. These target coefficients are then broadcast from the satellite for user access, enabling users to conveniently and in real-time obtain high-precision tropospheric delay correction information. This significantly improves the convergence speed and accuracy of GNSS positioning in different regions, particularly in areas with complex terrain, coastal areas, and high-altitude mountainous regions that are not covered by GNSS ground-based observation stations. Attached Figure Description

[0027] 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 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, wherein:

[0028] Figure 1 A flowchart illustrating a wide-area tropospheric modeling method integrating navigation satellites and meteorological models provided for the implementation of this invention. Figure 1 ;

[0029] Figure 2 A flowchart illustrating a wide-area tropospheric modeling method integrating navigation satellites and meteorological models provided for the implementation of this invention. Figure 2 ;

[0030] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0031] 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. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention. Based on the examples in the present invention, all other examples obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] In the following description, references are made to “some examples”, which describe a subset of all possible embodiments. However, it is understood that “some examples” may be the same subset or different subset of all possible embodiments and may be combined with each other without conflict.

[0033] It should be noted that the terms "first, second, third" used in the examples of this invention are only used to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, third" can be interchanged in a specific order or sequence where permitted, so that the examples of this invention described herein can be implemented in an order other than that illustrated or described herein.

[0034] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which these embodiments of the invention pertain. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0035] Figure 1 A flowchart illustrating a wide-area tropospheric modeling method integrating navigation satellites and meteorological models provided in this embodiment of the invention. Figure 1 The wide-area tropospheric modeling method that integrates navigation satellites and meteorological models provided in this embodiment of the invention can be executed by an electronic device, which can be a server.

[0036] A wide-area tropospheric modeling method integrating navigation satellites and meteorological models, applied to the server side, includes the following steps S101 to S104:

[0037] S101. In areas where the distribution of global navigation satellite ground observation stations is missing, the first tropospheric zenith wet delay is obtained through meteorological data provided by the global numerical weather prediction model. The first tropospheric zenith wet delay is then interpolated using the Akima interpolation algorithm to generate a second tropospheric zenith wet delay synchronized with the global navigation satellite ground observation stations.

[0038] In embodiments of this invention, in GNSS applications, tropospheric delay refers to the additional propagation time of a satellite signal as it passes through the troposphere (i.e., from the Earth's surface to an altitude of approximately 8-18 km) due to the refraction effect of air. The refractive index of air is slightly greater than that of a vacuum, resulting in a decrease in the speed of electromagnetic waves and a longer propagation path, thus causing delay. Tropospheric delay includes dry delay and wet delay. Dry delay is mainly caused by the refraction effect resulting from the pressure of dry air molecules, while wet delay is caused by the refraction effect caused by water vapor molecules in the air. Tropospheric zenith wet delay is the equivalent representation of wet delay in the zenith direction, specifically referring to the delay component caused by water vapor when the signal passes vertically through the atmosphere. Areas lacking global navigation satellite ground observation station coverage refer to areas that cannot be covered by global navigation satellite ground observation stations, such as those with complex terrain, along coastlines, and in high-altitude mountainous areas. The global numerical weather prediction model refers to the Global Forecast System (GFS), developed and operated under the leadership of the Environmental Forecasting Center, which provides meteorological data including air pressure, temperature, humidity, and atmospheric density to the server in real time at preset intervals, such as every 3 hours. The server performs integral calculations based on the acquired meteorological data to obtain the first tropospheric zenith wet delay. The Akima interpolation algorithm is a smooth piecewise cubic polynomial interpolation method. Since GFS data is discrete-grid and discrete-time, while the location and observation time of the global navigation satellite ground observation station are usually arbitrary, spatiotemporal interpolation is required to obtain the second tropospheric zenith wet delay synchronized with the global navigation satellite ground observation station. This global navigation satellite ground observation station is an actual station with an ambiguity fixation rate higher than 95%.

[0039] S102. Construct a virtual reference station that meets the requirements according to the preset principles, and obtain the third tropospheric zenith wet delay corresponding to the virtual reference station through the second tropospheric zenith wet delay.

[0040] In embodiments of the present invention, the preset principle is to set up virtual reference stations based on the distribution and terrain features of global navigation satellite ground observation stations. The distance between the virtual reference station and the global navigation satellite ground observation station does not exceed a fixed threshold, and the height difference between different global navigation satellite ground observation stations is kept within a preset height. By constructing a satisfactory virtual reference station in an area where the distribution of global navigation satellite ground observation stations is missing according to the above preset principle, the third tropospheric zenith wet delay corresponding to the virtual reference station can be interpolated by spatial interpolation and elevation correction of the second tropospheric zenith wet delay.

[0041] S103. The fourth tropospheric zenith wet delay and the third tropospheric zenith wet delay estimated by the global navigation satellite ground observation station are fused to obtain the fused tropospheric zenith wet delay; and the fused tropospheric zenith wet delay and the improved tropospheric polynomial fitting model are used to model and determine the target coefficients of the improved tropospheric polynomial fitting model suitable for satellite broadcasting.

[0042] In embodiments of the present invention, the fourth and third tropospheric zenith wet delays estimated using ground observation stations of global navigation satellites are fused. The fusion method can be a Kalman filter algorithm or an optimal interpolation algorithm, etc., and is not limited here. Then, the fused tropospheric zenith wet delay and an improved tropospheric polynomial fitting model are used for modeling to determine the target coefficients of the improved tropospheric polynomial fitting model suitable for satellite-end broadcasting. The improved tropospheric polynomial fitting model is a quadratic polynomial incorporating an exponential function to describe the variation of the tropospheric zenith wet delay in the horizontal and vertical directions; the exponential function characterizes the altitude-related variation of the zenith wet delay. The improved tropospheric polynomial fitting model is as follows:

[0043] ;

[0044] In the above formula, For the fused tropospheric zenith wet delay, The water vapor scale factor. , , , , and All are variable parameters. For elevation, This represents the latitude deviation corresponding to the ground observation stations of global navigation satellites. This represents the longitude deviation corresponding to the ground observation station of the global navigation satellite system.

[0045] Among them, the improved tropospheric polynomial fitting model The elevation is the global navigation satellite ground observation station elevation on the server side and the user elevation on the user side.

[0046] Among them, after the improved tropospheric polynomial fitting model is successfully modeled, it can be updated after a preset time. After the preset time, steps S101 to S103 are re-executed to obtain new target coefficients.

[0047] S104. The target coefficients are broadcast via satellite for use by users.

[0048] In an embodiment of the present invention, after obtaining the target coefficients, the target coefficients are broadcast via satellite so that the user terminal can quickly calculate the local zenith wet delay using these target coefficients, thereby further improving positioning accuracy.

[0049] It is understood that, in the embodiments of the present invention, in areas where the distribution of global navigation satellite ground observation stations is missing, a first tropospheric zenith wet delay is obtained through meteorological data provided by a global numerical weather prediction model, and the first tropospheric zenith wet delay is interpolated using the Akima interpolation algorithm to generate a second tropospheric zenith wet delay synchronized with the global navigation satellite ground observation station; a virtual reference station that meets the requirements is constructed according to preset principles, and a third tropospheric zenith wet delay corresponding to the virtual reference station is obtained through the second tropospheric zenith wet delay; the fourth tropospheric zenith wet delay estimated by the global navigation satellite ground observation station and the third tropospheric zenith wet delay are fused to obtain the fused tropospheric zenith wet delay; and the fused tropospheric zenith wet delay and an improved tropospheric polynomial fitting model are used for modeling to determine the target coefficients of the improved tropospheric polynomial fitting model suitable for satellite broadcasting; the target coefficients are broadcast via satellite for use by users. In this process, virtual reference stations are constructed in areas lacking global navigation satellite ground observation stations (GNSS stations) according to preset principles. The zenith wet delay of these virtual reference stations is obtained using a numerical meteorological model. By fusing the estimated tropospheric zenith wet delay from GNSS stations with that from the virtual reference stations, and combining this with an improved tropospheric polynomial fitting model, target coefficients suitable for satellite broadcasting are generated. By broadcasting these target coefficients from the satellite, users can conveniently and in real-time obtain high-precision tropospheric delay correction information, significantly improving the convergence speed and accuracy of GNSS positioning in different regions, particularly in areas with complex terrain, coastal areas, and high-altitude mountainous regions that are not covered by GNSS stations.

[0050] In some embodiments of the present invention, the modeling in S103 using the fused tropospheric zenith wet delay and the improved tropospheric polynomial fitting model, and determining the target coefficients of the improved tropospheric polynomial fitting model suitable for satellite broadcasting, can be achieved through S1031 to S1032, as explained by the following steps.

[0051] S1031. Based on the fused tropospheric zenith wet delay, the least squares method is used for parameter estimation. During the estimation process, outliers are removed by median detection to obtain the initial tropospheric polynomial fitting model.

[0052] In some embodiments of the present invention, the fused tropospheric zenith wet delay includes the fourth tropospheric zenith wet delay estimated by the global navigation satellite ground observation station and the third tropospheric zenith wet delay corresponding to the virtual reference station. The improved tropospheric polynomial fitting model includes multiple parameters. The fused tropospheric zenith wet delay is substituted into the improved tropospheric polynomial fitting model, and the least squares method is used to estimate the values ​​of unknown parameters. During the estimation process, abnormal tropospheric zenith wet delays are removed by median detection. The remaining fused tropospheric zenith wet delays are then substituted into the improved tropospheric polynomial fitting model, and the least squares method is used to estimate the values ​​of unknown parameters to obtain the initial tropospheric polynomial fitting model.

[0053] S1032. When the initial tropospheric polynomial fitting model meets the preset conditions, the coefficients of the initial tropospheric polynomial fitting model are used as the target coefficients for the current period; otherwise, the coefficients of the initial tropospheric polynomial fitting model of the previous period are used as the target coefficients.

[0054] In some embodiments of the present invention, when determining whether an initial tropospheric polynomial fitting model meets the requirements after obtaining the initial tropospheric polynomial fitting model, the deviation between the calculated local zenith wet delay and the actual local zenith wet delay can be obtained first. Then, the root mean square error (RMSE) of the deviation is used for evaluation. If the RMSE value meets the preset accuracy, the modeling is successful in the current moment. The coefficients of the initial tropospheric polynomial fitting model are then obtained and broadcast as target coefficients via satellite for user access. If the RMSE value does not meet the preset accuracy, the modeling fails in the current moment. In this case, the coefficients of the initial tropospheric polynomial fitting model successfully modeled in the previous cycle are used as target coefficients and broadcast via satellite for user access.

[0055] In some embodiments of the present invention, before fusing the fourth tropospheric zenith wet delay and the third tropospheric zenith wet delay estimated by the global navigation satellite ground observation station in step S103 to obtain the fused tropospheric zenith wet delay, step S10 is also included, which is described by the following steps.

[0056] S10. Using the non-difference and non-combination precise single-point positioning ambiguity fixing technology, the fourth tropospheric zenith wet delay of the global navigation satellite ground observation station is estimated in real time.

[0057] In some embodiments of the present invention, the non-difference, non-combination precise single-point positioning ambiguity fixing technique can specifically be as follows: First, the wide-lane floating-point ambiguity is calculated using carrier phase observations and pseudorange observations corresponding to different frequencies, specifically calculated using the following formula:

[0058] ;

[0059] In the above formula, The frequency of the first carrier signal. The frequency of the second carrier signal. For the receiver end (i.e., the user end, and the same applies thereafter). r In the Each observation epoch for the satellite end s exist The first carrier phase observation value, For the receiver end r In the Each observation epoch for the satellite end s exist The second carrier phase observation value, For wide-lane wavelength, For the receiver end at the first Each observation epoch for the satellite end s exist The first pseudorange observation after satellite differential code bias correction, For the receiver end r at the first Each observation epoch for the satellite end s exist The second pseudorange observation value after satellite differential code bias correction is displayed. For the receiver end r In the Each observation epoch for the satellite end s The fuzziness of the wide alley floating point, WL It is Kuanxiang.

[0060] Furthermore, the wide-lane integer ambiguity is calculated using the wide-lane floating-point ambiguity and the wide-lane phase deviation at the receiver and satellite ends, specifically using the following formula:

[0061] ;

[0062] In the above formula, For the receiver end r For satellite end s The wide-lane floating-point ambiguity, this value can be Expected value For the receiver end r For satellite end s The width of the integer ambiguity, For the receiver end r Wide lane phase deviation, For satellite end s Wide lane phase deviation.

[0063] Furthermore, the narrow-lane floating-point ambiguity is calculated based on the floating-point ambiguity of the ionosphere-free combination, the wide-lane integer ambiguity, and different frequencies. The specific calculation formula is as follows:

[0064] ;

[0065] In the above formula, For the receiver end r For satellite end s ionosphere IF Combined floating-point ambiguity, NL It is a narrow alley. For the receiver end r For satellite end s Narrow alley floating point fuzziness, The wavelength without an ionosphere For the receiver end r For satellite end s The calculation process for the wide-lane integer ambiguity is described below. For the wavelength of the narrow alley, c It is the speed of light.

[0066] in:

[0067] ;

[0068] In the above formula, and For floating-point ambiguity, Specifically, the receiver end r First carrier signal frequency Above, to the satellite end s floating-point ambiguity, Specifically, the receiver end r Second carrier signal frequency Above, to the satellite end s The floating-point ambiguity.

[0069] Specifically, by using precise single-point positioning floating-point solutions, the pseudorange and carrier phase observations corresponding to the first and second carrier signal frequencies acquired at the receiver are jointly estimated to obtain the solution. and .

[0070] Furthermore, the narrow-lane integer ambiguity is calculated using the narrow-lane floating-point ambiguity and the narrow-lane phase deviation at the receiver and satellite ends, specifically using the following formula:

[0071] ;

[0072] In the above formula, For the receiver end r For satellite end s The integer ambiguity of the narrow alley, For the receiver end r Narrow alley phase deviation, For satellite end s Narrow alley phase deviation.

[0073] Furthermore, the ionospheric-free integer ambiguity is calculated using the narrow-lane integer ambiguity and the wide-lane integer ambiguity, and then input into the joint observation model of pseudorange and carrier phase, as follows:

[0074] ;

[0075] In the above formula, For the receiver end r In the Each observation epoch for the satellite end s pseudorange observations, For the receiver end r In the Each observation epoch for the satellite end s The carrier phase observations, where x, y, and z are the receiver coordinates. For the receiver end r The clock bias estimate relative to the system time, For the receiver end r For satellite end s Integer ambiguity of ionosphere-free combinations IF It has no ionosphere. T For transpose, For the fourth tropospheric zenith wet delay, , and Pointing the receiver end to the satellite end s The components of the unit vector of the line of sight direction on the x, y, and z coordinate axes. Indicates the relationship with the first n Ionospheric sensitivity factors related to the frequency of a carrier signal For the receiver end r satellite end s The direction of the wet troposphere mapping function, Based on the frequency of the first carrier signal, from the receiver end r to satellite end s The tilted ionospheric delay, The carrier signal frequency is The wavelength of the carrier signal in a vacuum.

[0076] In the joint observation model of pseudorange and carrier phase, the carrier phase observation values ​​are integer carrier phase observation values ​​with fixed ambiguity after bias correction. The receiver coordinates, the clock difference of the receiver relative to the system time, and the fourth tropospheric zenith wet delay are solved as unknowns to finally obtain the fourth tropospheric zenith wet delay.

[0077] In some embodiments of the present invention, the second tropospheric zenith wet delay synchronized with the global navigation satellite ground observation station is generated by interpolating the first tropospheric zenith wet delay using the Akima interpolation algorithm in S101, which can be implemented in S1011, as described in the following steps.

[0078] S1011. The first tropospheric zenith wet delay at discrete times is interpolated in both spatial and temporal dimensions using the Akima interpolation algorithm to obtain the second tropospheric zenith wet delay that matches the observation time of the global navigation satellite ground observation station.

[0079] In some embodiments of the present invention, the adjacent slopes between the first tropospheric zenith wet delays at any two integer times are calculated. Then, the difference between the calculated adjacent slopes is calculated to obtain the weight. Based on the weight, the first derivative value at each integer time is estimated. Using the first tropospheric zenith wet delay at the integer time and its corresponding derivative, a smooth transition curve is constructed between adjacent integer times. The first tropospheric zenith wet delay at any time can be obtained through this curve. Then, the second tropospheric zenith wet delay matching the observation time of the global navigation satellite ground observation station is obtained.

[0080] Figure 2 A flowchart illustrating a wide-area tropospheric modeling method integrating navigation satellites and meteorological models provided in this embodiment of the invention. Figure 2 The wide-area tropospheric modeling method that integrates navigation satellites and meteorological models provided in this embodiment of the invention can be executed by an electronic device, which can be a user terminal.

[0081] In an embodiment of the present invention, a wide-area tropospheric modeling method integrating navigation satellites and meteorological models, applied to a user terminal, includes:

[0082] S201, Receive the target coefficients broadcast from the satellite.

[0083] S202. Calculate the local zenith wet delay value using the target coefficients received, based on the latitude and longitude of the user terminal's location and the user's elevation.

[0084] In an embodiment of the present invention, user elevation refers to the altitude of the user terminal's location. The user terminal receives the target coefficients broadcast from the satellite. The latitude and longitude of the user terminal's (i.e., the receiver's) location, the user elevation, and the target coefficients are substituted into the improved tropospheric polynomial fitting model to obtain the value of the local zenith wet delay. Based on the calculated local zenith wet delay, the tropospheric propagation delay of the global navigation satellite ground observation station signal is corrected to obtain a more accurate positioning.

[0085] It is understood that, in the embodiments of the present invention, the user terminal receives the target coefficient sent by the server via the satellite and only needs to perform one calculation to obtain a high-precision local zenith wet delay that is adapted to the current location. In this way, while greatly reducing the computational load and communication cost of the user terminal, the positioning accuracy is efficiently improved.

[0086] Reference Figure 3 The diagram shows a structural schematic of an electronic device according to an embodiment of the present invention. The specific examples of the present invention do not limit the specific implementation of the electronic device.

[0087] like Figure 3 As shown, the electronic device may include: a processor 502, a communications interface 504, a memory 506, and a communications bus 508.

[0088] in:

[0089] The processor 502, communication interface 504, and memory 506 communicate with each other via communication bus 508.

[0090] Communication interface 504 is used to communicate with other electronic devices or servers.

[0091] The processor 502 is used to execute program 510, which can specifically execute the relevant steps in the above-described server-side or user-side method embodiments.

[0092] Specifically, program 510 may include program code that includes computer operation instructions.

[0093] Processor 502 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The smart device may include one or more processors of the same type, such as one or more CPUs; or it may include processors of different types, such as one or more CPUs and one or more ASICs.

[0094] Memory 506 is used to store program 510. Memory 506 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0095] Specifically, program 510 can be used to cause processor 502 to perform the operations corresponding to the methods described in the above method embodiments.

[0096] The specific implementation of each step in program 510 can be found in the corresponding descriptions of the steps and units in the above method embodiments, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the foregoing method embodiments, and will not be repeated here.

[0097] It should be noted that, depending on the implementation needs, the various components / steps described in the embodiments of the present invention can be broken down into more components / steps, or two or more components / steps or parts of the operation of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of the present invention.

[0098] The methods described above according to embodiments of the present invention can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD-ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code originally stored on a remote recording medium or a non-transitory machine-readable medium and subsequently stored on a local recording medium, downloaded via a network. Thus, the methods described herein can be processed by software stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., RAM, ROM, flash memory, etc.) capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods described herein. Furthermore, when a general-purpose computer accesses code used to implement the methods shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for executing the methods shown herein.

[0099] Those skilled in the art will recognize that the units and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the embodiments of the present invention.

[0100] The above embodiments are only used to illustrate the embodiments of the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the embodiments of the present invention. Therefore, all equivalent technical solutions also fall within the scope of the embodiments of the present invention, and the patent protection scope of the embodiments of the present invention should be defined by the claims.

Claims

1. A wide-area tropospheric modeling method integrating navigation satellites and meteorological models, applied to a server, characterized in that, include: In areas where the distribution of global navigation satellite ground observation stations is missing, the first tropospheric zenith wet delay is obtained through meteorological data provided by the global numerical weather prediction model. The first tropospheric zenith wet delay is then interpolated using the Akima interpolation algorithm to generate a second tropospheric zenith wet delay that is synchronized with the global navigation satellite ground observation stations. A virtual reference station that meets the requirements is constructed according to the preset principles, and the third tropospheric zenith wet delay corresponding to the virtual reference station is obtained through the second tropospheric zenith wet delay. The fourth tropospheric zenith wet delay and the third tropospheric zenith wet delay estimated by the global navigation satellite ground observation station are fused to obtain the fused tropospheric zenith wet delay. The model was built using the fused tropospheric zenith wet delay and the improved tropospheric polynomial fitting model, and the target coefficients of the improved tropospheric polynomial fitting model suitable for satellite broadcasting were determined. The target coefficients are broadcast via satellite for use by users. The modeling process utilizes the fused tropospheric zenith wet delay and an improved tropospheric polynomial fitting model to determine the target coefficients of the improved tropospheric polynomial fitting model suitable for satellite-to-satellite broadcasting, including: Based on the fused tropospheric zenith wet delay, the least squares method is used for parameter estimation. During the estimation process, outliers are removed by median detection to obtain the initial tropospheric polynomial fitting model. When the initial tropospheric polynomial fitting model meets the preset conditions, the coefficients of the initial tropospheric polynomial fitting model are used as the target coefficients for the current period; otherwise, the coefficients of the initial tropospheric polynomial fitting model for the previous period are used as the target coefficients. The step of interpolating the first tropospheric zenith wet delay using the Akima interpolation algorithm to generate the second tropospheric zenith wet delay synchronized with the global navigation satellite ground observation station includes: The first tropospheric zenith wet delay at discrete times is interpolated in both spatial and temporal dimensions using the Akima interpolation algorithm to obtain the second tropospheric zenith wet delay that matches the observation time of the global navigation satellite ground observation station. The preset principles include: setting virtual reference stations based on the distribution and terrain features of global navigation satellite ground observation stations; the distance between the virtual reference station and the global navigation satellite ground observation station does not exceed a fixed threshold; and the height difference between different global navigation satellite ground observation stations is kept within a preset height.

2. The method according to claim 1, characterized in that, The improved tropospheric polynomial fitting model is expressed by the following formula: ； In the above formula, For the fused tropospheric zenith wet delay, The water vapor scale factor. , , , , and All are variable parameters. For elevation, This represents the latitude deviation corresponding to the ground observation stations of global navigation satellites. This represents the longitude deviation corresponding to the ground observation station of the global navigation satellite system.

3. The method according to claim 1, characterized in that, Before fusing the fourth and third tropospheric zenith wet delays estimated by global navigation satellite ground observation stations to obtain the fused tropospheric zenith wet delay, the method further includes: By using the non-differential non-combination precise single-point positioning ambiguity fixing technique, the fourth tropospheric zenith wet delay of the global navigation satellite ground observation station is estimated in real time.

4. A wide-area tropospheric modeling method integrating navigation satellites and meteorological models, applied to the user end, characterized in that, The method includes: Receive target coefficients broadcast from the satellite; The local zenith wet delay value is calculated using the target coefficients received, based on the latitude and longitude of the user's location and the user's elevation. The target coefficient is obtained through the following steps: in areas where the distribution of global navigation satellite ground observation stations is missing, the first tropospheric zenith wet delay is obtained through meteorological data provided by the global numerical weather prediction model; the first tropospheric zenith wet delay at discrete times is interpolated in spatial and temporal dimensions using the Akima interpolation algorithm to obtain the second tropospheric zenith wet delay that matches the observation time of the global navigation satellite ground observation station. A virtual reference station that meets the requirements is constructed according to the preset principles, and the third tropospheric zenith wet delay corresponding to the virtual reference station is obtained through the second tropospheric zenith wet delay. The fourth tropospheric zenith wet delay and the third tropospheric zenith wet delay estimated by the global navigation satellite ground observation station are fused to obtain the fused tropospheric zenith wet delay. Based on the fused tropospheric zenith wet delay, the least squares method is used to estimate the parameters. During the estimation process, outliers are removed by the median detection method to obtain the initial tropospheric polynomial fitting model. When the initial tropospheric polynomial fitting model meets the preset conditions, the coefficients of the initial tropospheric polynomial fitting model are used as the target coefficients for the current period; otherwise, the coefficients of the initial tropospheric polynomial fitting model for the previous period are used as the target coefficients. The preset principles include: setting virtual reference stations based on the distribution and terrain features of global navigation satellite ground observation stations; the distance between the virtual reference station and the global navigation satellite ground observation station does not exceed a fixed threshold; and the height difference between different global navigation satellite ground observation stations is kept within a preset height.

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