A method and device for realizing positioning based on RTK

Abstract

The present application discloses a method and device for realizing positioning based on RTK. The server directly broadcasts the model coefficients of atmospheric error to the user, and the user can calculate the atmospheric error compensation value in real time based on these model coefficients, which reduces the limitation of the distance between the user position and the reference station on the atmospheric error in the related technology and significantly improves the positioning performance. Especially under the condition of active ionosphere, the broadcast of model coefficients compensates for the error caused by the large distance between the user position and the reference station, thereby realizing large-scale high-precision positioning and improving the positioning performance of the user point.

Description

Technical Field

[0001] The present application relates to but is not limited to satellite navigation technology, and in particular to a method and device for achieving positioning based on RTK. Background Art

[0002] Global Navigation Satellite System (GNSS) technology can provide users with high-precision navigation, positioning and timing (PNT) services. Its full-time, low-cost and high-reliability characteristics make it one of the core foundations of modern location services. In recent years, with the increasing demand for high-precision location services in application scenarios such as precision agriculture, intelligent driving and drones, the high-precision development direction of GNSS technology has received widespread attention.

[0003] Carrier real-time kinematic differential (RTK) technology is currently the mainstream method for achieving centimeter-level or even millimeter-level high-precision positioning services. RTK uses ground-based augmentation technology and the correlation of errors between measuring stations to effectively reduce or eliminate satellite orbit errors, satellite clock errors, measuring station clock errors, tropospheric errors, and ionospheric errors through inter-station and inter-satellite double difference methods. This method can provide centimeter-level positioning accuracy and therefore occupies a dominant position in current high-precision applications. However, due to the strong correlation between ionospheric and tropospheric errors and satellite orbit errors and the distance between reference stations, when the distance between the base station and the mobile station exceeds a certain distance, such as 30 kilometers, the errors are difficult to completely eliminate, which limits the application scope of RTK technology and reduces the positioning performance of end users.

[0004] How to improve the positioning performance of end users is a technical problem that needs to be solved urgently. Summary of the invention

[0005] The present application provides a method and device for realizing positioning based on RTK, which can realize high-precision positioning over a large range and improve the positioning performance of user points.

[0006] The embodiment of the present invention provides a method for implementing positioning based on RTK, which is applied to a user point and includes:

[0007] Receive the model coefficients and virtual grid point data of the atmospheric error model broadcasted by the server;

[0008] Calculate the atmospheric error between the user point and the virtual grid point data based on the received model coefficients and virtual grid point data;

[0009] The observed values ​​are compensated according to the calculated atmospheric errors, or the calculated atmospheric errors are used to construct virtual observation equations to constrain the atmospheric parameters.

[0010] In an exemplary embodiment, the model coefficients of the atmospheric error model and the virtual grid point data are broadcast to the user point via a customized Radio Technical Commission standard RTCM format.

[0011] In an exemplary embodiment, the broadcasting to the user point in a customized RTCM format includes:

[0012] The model coefficients and the virtual grid point data are encapsulated into a custom RTCM field in an RTCM message, and broadcasted to the user.

[0013] In an exemplary embodiment, the step of calculating the atmospheric error between the user point and the virtual grid point includes:

[0014] The atmospheric error is calculated based on the received model coefficients and the virtual grid point data, and the coordinate difference between the actual position of the user point and the coordinates of the virtual grid point in the virtual grid point data.

[0015] In an exemplary embodiment, the atmospheric error is calculated using the following formula:

[0016]

[0017] Where ΔΔ represents the double difference operator; represents a double difference tropospheric delay between the user point and the virtual grid point; represents the double difference ionospheric delay between the user point and the virtual grid point; a trop 、b trop is the tropospheric delay model coefficient in the model parameters; a iono 、b iono is the ionospheric delay model coefficient in the model parameters; ΔN RV is the north coordinate difference between the user point and the virtual base station point; ΔE rv is the easting coordinate difference between the user point and the virtual base station point.

[0018] In an exemplary embodiment, compensating the observation value according to the calculated atmospheric error includes:

[0019] The calculated atmospheric error is used as error correction to correct the errors of pseudorange observation values ​​and carrier observation values, or when estimating atmospheric error parameters during RTK positioning, the atmospheric error is used as a virtual observation equation for parameter estimation.

[0020] The embodiment of the present application also provides a method for implementing positioning based on RTK, which is applied to a server, including:

[0021] Establish an atmospheric error model for the target area and generate virtual grid point data;

[0022] The model coefficients of the atmospheric error model and the virtual grid point data are broadcast to the user to calculate the atmospheric error between the user point and the virtual grid point data based on the model coefficients and virtual grid point data broadcast by the server, so as to perform real-time atmospheric error compensation for the observation value or use the calculated atmospheric error to construct a virtual observation equation to constrain the atmospheric parameters.

[0023] In an exemplary embodiment, the model coefficients of the atmospheric error model and the virtual grid point data are broadcast to the user via a customized RTCM format.

[0024] An embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute any of the above-mentioned methods for realizing positioning based on RTK.

[0025] An embodiment of the present application further provides a computer device, including a memory and a processor, wherein the memory stores the following instructions that can be executed by the processor: used to execute any of the above-mentioned methods for realizing positioning based on RTK.

[0026] The embodiment of the present application also provides a device for realizing positioning based on RTK, which is arranged at a user point; and includes: a receiving module, a calculating module, and a second processing module; wherein,

[0027] A receiving module, used for receiving the model coefficients and virtual grid point data of the atmospheric error model broadcasted from the server;

[0028] A calculation module, used for calculating the atmospheric error between the user point and the virtual grid point according to the received model coefficient and the virtual grid point data;

[0029] The second processing module is used to compensate the observation value according to the calculated atmospheric error, or use the calculated atmospheric error to construct a virtual observation equation to constrain the atmospheric parameters.

[0030] The embodiment of the present application further provides a device for realizing positioning based on RTK, which is arranged at a server end; and comprises: a first processing module and a broadcasting module; wherein,

[0031] The first processing module is used to establish an atmospheric error model of the target area and generate virtual grid point data;

[0032] The broadcast module is used to broadcast the model coefficients and virtual grid point data of the atmospheric error model to the user so as to calculate the atmospheric error between the user point and the virtual grid point according to the model coefficients and virtual grid point data broadcast by the server, so as to perform real-time atmospheric error compensation for the observation value or use the calculated atmospheric error to construct a virtual observation equation to constrain the atmospheric parameters.

[0033] The method for realizing positioning based on RTK provided in the embodiment of the present application directly broadcasts the model coefficients of the atmospheric error of the server to the user, so that the user can calculate the error compensation value in real time based on these model coefficients, thereby reducing the limitation of the distance between the user position and the reference station (virtual reference station) on the atmospheric error in the related technology, and significantly improving the positioning performance. Under the condition of active ionosphere, the broadcast of model coefficients compensates for the error caused by the large distance between the user position and the reference station (virtual reference station), ensures the positioning performance, thereby realizing high-precision positioning over a large range and improving the positioning performance of the user point.

[0034] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings are used to provide further understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0036] Figure 1 This is a schematic diagram of a network type including three reference stations as an example in an embodiment of the present application;

[0037] Figure 2 The following is a flow chart of the method for realizing positioning based on RTK in the embodiment of the present application. Figure 1 ;

[0038] Figure 3 The following is a flow chart of the method for realizing positioning based on RTK in the embodiment of the present application. Figure 2 ;

[0039] Figure 4 The structure diagram of the device for realizing positioning based on RTK in the embodiment of the present application is shown in FIG. Figure 1 ;

[0040] Figure 5 The structure diagram of the device for realizing positioning based on RTK in the embodiment of the present application is shown in FIG. Figure 2 . DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present application more clear, the embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other arbitrarily without conflict.

[0042] In order to facilitate understanding of the present application, the present application will be described more fully below with reference to the relevant drawings. Embodiments of the present application are provided in the drawings. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0044] It is understood that the terms "first" and "second" used in this application are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0045] It can be understood that the “connection” in the following embodiments should be understood as “electrical connection”, “communication connection”, etc. if the connected circuits, modules, units, etc. have electrical signals or data transmission between each other.

[0046] When used herein, the singular forms "a", "an", and "said / the" may also include plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "include / comprise" or "have" and the like specify the presence of stated features, wholes, steps, operations, components, parts, or combinations thereof, but do not exclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof. At the same time, the term "and / or" used in this specification includes any and all combinations of the relevant listed items.

[0047] In order to cope with the limitations of traditional RTK in large-scale high-precision positioning, network RTK technology came into being. Network RTK establishes a regional base station network through the collaboration of multiple base stations to provide users with high-precision positioning services with a wider coverage. Among them, the virtual reference station (VRS) mode, as the core implementation method of network RTK, uses the server to dynamically allocate virtual reference stations to each mobile station, thereby effectively reducing the impact of errors on positioning accuracy. This mode has become the standard for real-time high-precision positioning services with its stability and reliability. However, with the increase in concurrent demands from massive users, the VRS mode has gradually faced a bottleneck in service capabilities, and the method of individually allocating virtual reference stations to each user has put forward higher requirements for service capabilities.

[0048] To address this problem, gridded VRS technology was born. Gridded VRS divides the service area into uniform grid points, providing users with virtual differential data on the grid points closest to them, thereby significantly improving service capabilities. However, the setting of grid point spacing is the key to this technology. Too large a spacing will lead to a decrease in user positioning performance, while too small a spacing will increase the computational burden. To this end, in addition to broadcasting VRS data of grid points, the server also provides model coefficients to end users through a private protocol. End users use these coefficients to calculate the atmospheric error between their own position and the grid points, and compensate for the observation error, thereby improving the positioning performance of the RTK terminal while ensuring the service capabilities of the server.

[0049] Gridded VRS technology is in a certain network type (including at least three reference stations). First, a reference station is selected from the network type as the master station. Based on the observation data between the master station and other reference stations, the baseline atmospheric information of the two connected in the network type is calculated, and the atmospheric error of the VRS point relative to the master station is calculated through a certain model. Then, the geometric distance deviation of the VRS point relative to the master station is calculated using the broadcast ephemeris information, and the geometric distance deviation and atmospheric error are corrected to the master station to generate virtual grid point data.

[0050] The method for generating virtual grid point data is shown in formula (1) to formula (12).

[0051] The single-difference pseudorange between the master station A and the virtual grid point V and single-difference carrier observations The calculation is shown in formula (1) and formula (2):

[0052]

[0053] In formula (1) and formula (2), A represents the master station, V represents the virtual grid point, superscript s represents the common-view satellite between the master station A and the virtual grid point V, superscript r represents the reference satellite of the common-view satellite, which is used for inter-satellite single difference; λ represents the wavelength. The master station A and the virtual grid point V are inter-station single-differenced, denote the single-difference pseudorange and single-difference carrier observation values ​​of satellite s, respectively. represents the geometric distance single difference from the single difference satellite to the receiver, represents the single difference of tropospheric error, represents the single difference of ionospheric error, cΔdt AV represents the receiver clock error single difference, represents the integer ambiguity single difference, and The separate tables show the single differences of pseudorange and carrier observation noise.

[0054] Move the observation values ​​of the master station A in formula (1) and formula (2) to the right to obtain the pseudorange observation value and carrier phase observation value of the virtual grid point V:

[0055]

[0056] In formula (3) and formula (4), ΔΔ represents the double difference operator, that is, the inter-station single difference error of satellite S minus the inter-station single difference error of reference satellite r. and is a constant and can be eliminated in RTK positioning and is considered equal to 0. The coordinates of the virtual grid point V and the master station A are precisely known, and their ambiguity, multipath error, station clock error, and observation noise are considered to be consistent with those of the master station. and According to the results of regional atmospheric modeling:

[0057]

[0058] In formula (5), L represents the atmospheric delay difference between the reference stations in the triangulated network unit composed of three reference stations (such as A, B, and C). In the triangulated network unit composed of A, B, and C, ΔN and ΔE represent the plane coordinate difference with the main station A as the origin, that is, the difference between AB and AC in the north and east directions, forming the coefficient matrix B; a and b represent the model coefficients of the atmospheric error model, forming the model coefficient matrix X of the atmospheric error model, and L represents the atmospheric delay corresponding to AB and AC.

[0059] by Figure 1 Taking the network diagram shown as an example, the triangulated network unit includes the main reference station (A) represented by a solid triangle, the auxiliary reference stations (B, C) represented by hollow triangles, and the VRS stations represented by circles.

[0060] The regional modeling of atmospheric error includes: establishing the coefficient matrix and parameter matrix through the plane coordinate difference (north ΔN and east ΔE) in the triangulated network unit. As shown in formula (6), the least squares method is used to calculate the model coefficient X, which is used to fit the atmospheric delay model:

[0061] X=(B T PB) -1 (B T PL) -1 (6)

[0062] In formula (6), B represents the known base station coordinate difference matrix, L represents the observation delay difference, and P is a matrix that weights the observation values, which is used to adjust the contribution of each observation value to the model solution. Formula (6) represents the mathematical model used to describe the atmospheric error distribution by calculating the model coefficient X by inputting the known base station coordinate difference matrix B and the atmospheric delay difference L (including tropospheric delay and ionospheric delay). The final solution X is a set of model parameters that reflects the variation law of atmospheric delay in the region.

[0063] According to the position of the VRS point, as shown in formula (7), the atmospheric error of the VRS point relative to the master station A can be calculated:

[0064]

[0065] Based on the above information, the pseudo-range of the grid point virtual base station, i.e., the virtual grid point V, is calculated. and carrier observations As shown in formula (8) and formula (9) respectively:

[0066]

[0067] Considering the computing resources, the spacing of VRS points is usually set at about 5 kilometers, so that the maximum distance between the user and the nearest VRS point can reach about 3.5 kilometers. In general, the atmospheric error of the spacing of 3.5 kilometers can be ignored, that is, when the ionospheric activity is weak, the atmospheric error can be ignored at this spacing. However, when the ionosphere is active, the atmospheric error cannot be ignored.

[0068] In order to solve this problem, an embodiment of the present application provides a method for achieving positioning based on RTK. When the ionosphere is active, by customizing the Radio Technical Commission for Maritime Services (RTCM) standard format field, the atmospheric error of the user's position relative to the virtual grid point calculated according to the model coefficient is corrected to improve the positioning performance of the terminal and achieve large-scale high-precision positioning.

[0069] Figure 2 The method for realizing positioning based on RTK in the embodiment of the present application is applied to the server, such as Figure 2 As shown, it may include:

[0070] Step 200: Establish an atmospheric error model for the target area and generate virtual grid point data.

[0071] In one exemplary embodiment, step 200 may include:

[0072] First, obtain the reference station data distributed nationwide. In one embodiment, the reference station data distributed nationwide can be transmitted to the data center of the server in real time. The reference station data is the GNSS data received by the reference station (reference station) in the CORS system. The CORS system consists of multiple ground reference stations (reference stations), which are the core hardware facilities of the CORS system.

[0073] Next, the data center at the server end preprocesses the real-time CORS reference station data. In one embodiment, the preprocessing may include: data quality check; double difference baseline solution (eliminating the systematic error of the common view satellite); extracting atmospheric error information such as the troposphere and ionosphere.

[0074] Afterwards, based on the extracted atmospheric error information, the least square method is used to solve the model coefficients of the atmospheric error model and establish the atmospheric error model of the target area. Taking the simplest three reference stations forming a target area as an example, the specific implementation is shown in formula (5) and formula (6).

[0075] Finally, virtual grid point data at the virtual grid point is generated, as shown in formula (8) and formula (9). In one embodiment, the virtual grid point data may include pseudo-range and carrier observation values ​​of the virtual grid point, coordinate information of the virtual grid point, etc.

[0076] Step 201: The model coefficients and virtual grid point data of the atmospheric error model are broadcast to the user to calculate the atmospheric error between the user point and the virtual grid point according to the model coefficients and virtual grid point data broadcast by the server, so as to perform real-time atmospheric error compensation for the observation value or use the calculated atmospheric error to construct a virtual observation equation to constrain the atmospheric parameters.

[0077] In an exemplary embodiment, the model coefficients and virtual grid point data of the atmospheric error model can be broadcast to the user through a custom RTCM format. For example, the model coefficients and virtual grid point data can be encapsulated into an RTCM message and broadcast to the user point using a custom RTCM field. The custom RTCM field is an extended field in an RTCM message that complies with the standard RTCM protocol. It is an additional custom field used to carry model coefficients and virtual grid point data that is added within the framework of the standard RTCM protocol using the extension space allowed by the protocol. In other words, the custom RTCM field in the embodiment of the present application is an extended field added in the framework of the standard RTCM message.

[0078] Figure 2 The method for realizing positioning based on RTK shown in the figure broadcasts the model coefficients of atmospheric errors directly to users, so that users can calculate the error compensation values ​​in real time based on these model coefficients, reducing the limitation of the distance between the user point and the reference station on the atmospheric error in the related technology, and significantly improving the positioning performance. Under the condition of active ionosphere, the broadcast of model coefficients compensates for the error caused by the large distance between the user point and the reference station, ensures the positioning performance, thereby realizing high-precision positioning over a large range and improving the positioning performance of the user point.

[0079] Figure 3 The method for realizing positioning based on RTK in the embodiment of the present application is applied to user points, such as Figure 3 As shown, it may include:

[0080] Step 300: receiving model coefficients and virtual grid point data of an atmospheric error model broadcasted from a server.

[0081] In an exemplary embodiment, the model coefficients and virtual grid point data of the atmospheric error model can be broadcast to the user point through a custom RTCM format. For example, the model coefficients and virtual grid point data are encapsulated into a custom RTCM field in the RTCM message and broadcast to the user point. The custom RTCM field is an extended field in the RTCM message that complies with the standard RTCM protocol. It is an additional custom field used to carry model coefficients and virtual grid point data added within the framework of the standard RTCM protocol using the extension space allowed by the protocol. In other words, the custom RTCM field in the embodiment of the present application is an extended field added in the framework of the standard RTCM message.

[0082] Step 301: Calculate the atmospheric error between the user point and the virtual grid point data based on the received model coefficients and virtual grid point data.

[0083] In an exemplary embodiment, step 301 may include:

[0084] The user point calculates the atmospheric error based on the received model coefficients and virtual grid point data, as well as the coordinate difference between the actual position of the user point and the coordinates of the virtual grid point in the virtual grid point data. In one embodiment, the atmospheric error can be calculated according to formula (10):

[0085]

[0086] In formula (14), ΔΔ represents the double difference operator, that is, the inter-station single difference error of satellite S minus the inter-station single difference error of reference satellite r. represents the double difference tropospheric delay between the user point R and the virtual grid point V; represents the double difference ionospheric delay between the user point R and the virtual grid point V; a trop , b trop is the tropospheric delay model coefficient, which describes the influence of the relative position between the user point and the virtual base station (the coordinate difference ΔN in the north and the coordinate difference ΔE in the east) on the delay; a iono , b iono is the ionospheric delay model coefficient, describing the influence of relative position on ionospheric delay; ΔN RV ΔE is the north coordinate difference between the user point and the virtual base station, describing the position difference of the two points in the north direction; rv It is the east coordinate difference between the user point and the virtual base station point, describing the position difference of the two points in the east direction.

[0087] It should be noted that formula (10) can also be calculated using a single difference operator.

[0088] Step 302: Compensate the observed value according to the calculated atmospheric error, or use the calculated atmospheric error to construct a virtual observation equation to constrain the atmospheric parameters.

[0089] In one exemplary embodiment, step 302 may include:

[0090] The calculated atmospheric error is used as error correction to correct the errors of pseudorange observation values ​​and carrier observation values; or when estimating atmospheric error parameters during RTK positioning, the atmospheric error is used as a virtual observation equation for parameter estimation.

[0091] In an exemplary embodiment, the pseudorange observation value and carrier phase observation value of the satellite s between the user point R and the virtual grid point V are calculated based on the calculated atmospheric errors, namely, the tropospheric delay and the ionospheric delay.

[0092] In one embodiment, as shown in formula (11), the single-difference pseudorange observation value of satellite s between the user point R and the virtual grid point V is expressed as:

[0093]

[0094] In formula (11), It represents the single difference observation value of pseudorange between user point R and virtual grid point V corresponding to satellite s, and represents the difference between pseudorange observation values ​​of user point R and virtual grid point V to satellite s; Represents the geometric distance single difference, describing the actual geometric distance difference between the user point and the virtual base station point; represents the single difference of tropospheric delay, which is caused by the distribution of water vapor in the atmosphere; represents the single difference of ionospheric delay, which is caused by the change of electron density in the upper ionosphere; cΔdt RV is the receiver clock error single difference, which represents the clock error between the user point R and the virtual grid point V receiver, and is converted into the equivalent value of pseudorange after multiplying by the speed of light c; is the satellite clock error single difference, which represents the clock error of the user point R and the virtual grid point V relative to the satellite s, and is the pseudorange equivalent value after multiplying it by the speed of light c; Represents the single difference of noise, including uncertainties such as multipath effects and hardware delays.

[0095] In one embodiment, as shown in formula (12), the single-difference carrier phase observation value of satellite s between the user point R and the virtual grid point V is expressed as:

[0096]

[0097] In formula (12), represents the single-difference observation value of the carrier phase between the user point and the virtual grid point V corresponding to the satellite s; λ represents the wavelength of the carrier signal; They represent geometric range single difference, tropospheric delay single difference, and ionospheric delay single difference respectively; represents the integer ambiguity single difference, which is the integer uncertainty of the carrier phase observation; cΔdt RV represents the receiver clock error single difference; represents the single difference of satellite clock error; represents the noise single difference.

[0098] Figure 3 In the method of realizing positioning based on RTK shown in the figure, the user point calculates the error compensation value in real time based on these model coefficients, and the calculated atmospheric error is directly used to compensate the atmospheric error in the observation value, or to construct a virtual observation equation for parameter estimation, thereby correcting the error of the observation value, reducing the limitation of the distance between the user point and the reference station on the atmospheric error in the related technology, and significantly improving the positioning performance. Under the condition of active ionosphere, the broadcasting of model coefficients compensates for the error caused by the large distance between the user point and the reference station, ensures the positioning performance, thereby realizing high-precision positioning over a large range and improving the positioning performance of the user point.

[0099] Through the method for realizing positioning based on RTK provided by the embodiment of the present application, in the process of RTK terminal positioning, by calculating the atmospheric error between the user point position and the virtual grid point, and using these errors in real time to compensate or constrain the RTK positioning algorithm, the limitation of the distance between the user point and the reference station on the atmospheric error in the related technology is reduced, and the positioning performance is improved. In particular, under the condition of active ionosphere, the broadcasting of model coefficients compensates for the error caused by the large distance between the user point and the reference station, ensures the positioning performance, thereby realizing large-scale high-precision positioning and improving the positioning performance of the user point.

[0100] The RTK-based positioning method provided in the embodiment of the present application not only solves the problem of reduced accuracy caused by atmospheric errors, but also ensures positioning performance without increasing the computing burden on the server, thereby providing stable, efficient, real-time, high-precision positioning services for massive users.

[0101] It should be noted that the present embodiment uses a simple plane model as an example to describe the correction method, but it is not used to limit the protection scope of the present application. The method for realizing positioning based on RTK provided in the present embodiment is also applicable to other atmospheric modeling methods.

[0102] The present application also provides a computer-readable storage medium storing computer-executable instructions for executing the above-mentioned Figure 2 or Figure 3 Any one of the methods for achieving positioning based on RTK.

[0103] The present application further provides a computer device, including a memory and a processor, wherein the memory stores the following instructions executable by the processor: Figure 2 or Figure 3 Any of the steps of the method for implementing positioning based on RTK.

[0104] Figure 4 The structure diagram of the device for realizing positioning based on RTK in the embodiment of the present application is shown in FIG. Figure 1 , Figure 4 The device shown is set at the server end, such as Figure 4 As shown, it at least includes: a first processing module and a broadcasting module; wherein,

[0105] The first processing module is used to establish an atmospheric error model of the target area and generate virtual grid point data;

[0106] The broadcast module is used to broadcast the model coefficients and virtual grid point data of the atmospheric error model to the user so as to calculate the atmospheric error between the user point and the virtual grid point according to the model coefficients and virtual grid point data broadcast by the server, so as to perform real-time atmospheric error compensation for the observation value or use the calculated atmospheric error to construct a virtual observation equation to constrain the atmospheric parameters.

[0107] In an exemplary embodiment, the first processing module may be used to:

[0108] Acquire the reference station data distributed across the country; preprocess the real-time CORS reference station data; use the least squares method to solve the model coefficients of the atmospheric error model based on the extracted atmospheric error information, and establish the atmospheric error model of the target area; generate virtual grid point data at the virtual grid points.

[0109] In one embodiment, the virtual grid point data may include, for example, pseudoranges and carrier observation values ​​of the virtual grid points, coordinate information of the virtual grid points, and the like.

[0110] In an exemplary embodiment, the advertisement module may be used to:

[0111] The model coefficients of the atmospheric error model and virtual grid point data are broadcast to user points through a customized RTCM format.

[0112] Figure 5 The structure diagram of the device for realizing positioning based on RTK in the embodiment of the present application is shown in FIG. Figure 2 , Figure 5 The device shown is arranged at the user end, such as Figure 5 As shown, it at least includes: a receiving module, a computing module, and a second processing module; wherein,

[0113] A receiving module, used for receiving the model coefficients and virtual grid point data of the atmospheric error model broadcasted from the server;

[0114] A calculation module, used for calculating the atmospheric error between the user point and the virtual grid point according to the received model coefficient and the virtual grid point data;

[0115] The second processing module is used to compensate the observation value according to the calculated atmospheric error, or use the calculated atmospheric error to construct a virtual observation equation to constrain the atmospheric parameters.

[0116] In an exemplary embodiment, the receiving module may be used to receive model coefficients and virtual grid point data of an atmospheric error model broadcasted in a customized RTCM format.

[0117] In one exemplary embodiment, the computing module may be used to:

[0118] The atmospheric error is calculated based on the model coefficients and virtual grid point data broadcast by the receiving server and the coordinate difference between the actual position of the user point and the virtual grid point.

[0119] In an exemplary embodiment, the second processing module may be used to:

[0120] Based on the calculated atmospheric errors, namely the tropospheric delay and the ionospheric delay, the pseudorange observation value and the carrier phase observation value of the satellite s between the user point R and the virtual grid point V are calculated.

[0121] In short, from RTK to network RTK, and then to grid VRS, high-precision location service technology has gradually broken through the limitations of traditional methods in the process of continuous optimization, meeting the needs of high-precision positioning in a large range, at low cost and with high efficiency. The evolution of these technologies has not only improved the service capabilities of the GNSS PNT system, but also laid a solid foundation for its widespread application in cutting-edge fields such as intelligent driving, precision agriculture and drones.

[0122] Although the embodiments disclosed in this application are as above, the contents described are only embodiments adopted to facilitate understanding of this application and are not intended to limit this application. Any technician in the field to which this application belongs can make any modifications and changes in the form and details of implementation without departing from the spirit and scope disclosed in this application, but the scope of patent protection of this application shall still be based on the scope defined in the attached claims.

Claims

1. A method for achieving positioning based on RTK, characterized in that: Applied to user points, including: Receive the model coefficients and virtual grid point data of the atmospheric error model broadcasted by the server; Calculate the atmospheric error between the user point and the virtual grid point data based on the received model coefficients and virtual grid point data; The observed values ​​are compensated according to the calculated atmospheric errors, or the calculated atmospheric errors are used to construct virtual observation equations to constrain the atmospheric parameters.

2. The method according to claim 1, wherein: The model coefficients of the atmospheric error model and the virtual grid point data are broadcast to the user point via a customized Radio Technical Committee standard RTCM format.

3. The method according to claim 2, wherein: The broadcasting to the user point in a customized RTCM format includes: The model coefficients and the virtual grid point data are encapsulated into a custom RTCM field in an RTCM message, and broadcasted to the user.

4. The method according to claim 1, wherein: The calculating of the atmospheric error between the user point and the virtual grid point comprises: The atmospheric error is calculated based on the received model coefficients and the virtual grid point data, and the coordinate difference between the actual position of the user point and the coordinates of the virtual grid point in the virtual grid point data.

5. The method according to claim 4, wherein: The atmospheric error is calculated using the following formula: Where ΔΔ represents the double difference operator; represents a double difference tropospheric delay between the user point and the virtual grid point; represents the double difference ionospheric delay between the user point and the virtual grid point; a trop , b trop is the tropospheric delay model coefficient in the model parameters; a iono , b iono is the ionospheric delay model coefficient in the model parameters; ΔN RV is the north coordinate difference between the user point and the virtual base station point; ΔE rv is the easting coordinate difference between the user point and the virtual base station point.

6. The method according to claim 1, wherein: The compensating the observed value according to the calculated atmospheric error comprises: The calculated atmospheric error is used as error correction to correct the errors of pseudorange observation values ​​and carrier observation values, or when estimating atmospheric error parameters during RTK positioning, the atmospheric error is used as a virtual observation equation for parameter estimation.

7. A method for achieving positioning based on RTK, characterized in that: Applied to the server, including: Establish an atmospheric error model for the target area and generate virtual grid point data; The model coefficients of the atmospheric error model and the virtual grid point data are broadcast to the user to calculate the atmospheric error between the user point and the virtual grid point data based on the model coefficients and virtual grid point data broadcast by the server, so as to perform real-time atmospheric error compensation for the observation value or use the calculated atmospheric error to construct a virtual observation equation to constrain the atmospheric parameters.

8. The method according to claim 7, wherein: The model coefficients of the atmospheric error model and the virtual grid point data are broadcast to the user via a customized RTCM format.

9. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the method for realizing positioning based on RTK as described in any one of claims 1 to 6, or the method for realizing positioning based on RTK as described in claim 7 or 8.

10. A computer device comprising a memory and a processor, wherein: The memory stores the following instructions that can be executed by the processor: used to execute the method for realizing positioning based on RTK as described in any one of claims 1-6, or the steps of the method for realizing positioning based on RTK as described in claim 7 or 8.

11. A device for realizing positioning based on RTK, characterized in that: Set at the user point; including: a receiving module, a computing module, and a second processing module; wherein, A receiving module, used for receiving the model coefficients and virtual grid point data of the atmospheric error model broadcasted from the server; A calculation module, used for calculating the atmospheric error between the user point and the virtual grid point according to the received model coefficient and the virtual grid point data; The second processing module is used to compensate the observation value according to the calculated atmospheric error, or use the calculated atmospheric error to construct a virtual observation equation to constrain the atmospheric parameters.

12. A device for realizing positioning based on RTK, characterized in that: Set on the server side; including: The first processing module and the broadcasting module; wherein, The first processing module is used to establish an atmospheric error model of the target area and generate virtual grid point data; The broadcast module is used to broadcast the model coefficients and virtual grid point data of the atmospheric error model to the user so as to calculate the atmospheric error between the user point and the virtual grid point according to the model coefficients and virtual grid point data broadcast by the server, so as to perform real-time atmospheric error compensation for the observation value or use the calculated atmospheric error to construct a virtual observation equation to constrain the atmospheric parameters.

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