Real-time positioning enhancement method based on platform-user interaction feedback

Abstract

The present invention discloses a real-time positioning enhancement method based on platform-user interaction feedback, including: the end user performs RTK positioning, then uses the observations based on ambiguity fixing to calculate the ionospheric delay information, and transmits the ionospheric delay information back to the network RTK platform. The network RTK platform restores the ionospheric delay of the end user to the ionospheric delay between the end user and the reference station, then conducts a consistency check on the ionospheric delay information, and jointly models the ionospheric delay information that passes the check with the ionospheric delay information between reference stations using a crowdsourcing modeling method. The tropospheric delay is jointly modeled using an empirical model and the ionospheric delay information between reference stations. Finally, virtual observations are generated and broadcast to the end user for RTK positioning. The present invention improves the atmospheric modeling accuracy of long baselines, large obtuse angles, narrow and long networks, and atmospheric active network elements by combining user data feedback with fine switching of multiple atmospheric fitting models, thereby enhancing the positioning performance of users.

Description

Technical Field

[0001] The present invention belongs to the technical field of GNSS (Global Navigation Satellite System) positioning and navigation, and particularly relates to a real-time positioning enhancement method based on platform-user interaction feedback Background Art

[0002] The ground-based augmentation system is a high-precision real-time positioning technology based on network RTK positioning technology. This technology requires the establishment of a continuously operating reference station network within a certain area. Since these reference stations have accurate coordinate information and the ability to continuously solve for a long time, high-precision baseline atmospheric delay parameters can be obtained. After the user accesses, the network RTK platform provides positioning enhancement information with reduced systematic errors for the user through the method of model fitting and interpolation, so as to serve the user at the wide-area level to achieve real-time high-precision positioning

[0003] The advantage of the ground-based augmentation system is that it can provide high-precision real-time positioning services over a large area without the need for end-users to use their own set reference stations. It conducts data aggregation and data processing among multiple reference stations in the reference station network to achieve the generation of high-precision virtual observations. The ground-based augmentation system is widely used in fields such as land surveying and mapping, geological exploration, mechanical navigation, vehicle navigation, and UAV navigation, and plays an important role in application scenarios that require high-precision positioning

[0004] Currently, the relationship between the network RTK platform and the user is simple and one-way. The user cannot understand the accuracy of the enhancement data currently provided by the platform, and the platform cannot use the user's data to optimize its own positioning ability. Aiming at the problems of low accuracy of atmospheric modeling within network elements with long baselines, large obtuse angles, and active atmospheres, and insufficient utilization of user observation information, the present invention proposes a real-time positioning enhancement method based on platform-user interaction feedback to give full play to the information of both users and platforms to optimize the positioning service ability Summary of the Invention

[0005] To solve the above problems, the present invention discloses a real-time positioning enhancement method based on platform-user interaction feedback, which uses the combination of user data feedback and fine switching of multiple atmospheric fitting models to improve the regional atmospheric modeling accuracy. Through this technology, the atmospheric modeling accuracy of long baselines, large obtuse angles, narrow and long networks, and active atmosphere network elements can be improved, thereby enhancing the positioning performance of users

[0006] To achieve the above object, the technical solution of the present invention is as follows

[0007] A real-time positioning enhancement method based on platform-user interaction feedback includes the following steps

[0008] Step 1: The end-user performs RTK positioning

[0009] Step 2. The end user calculates the ionospheric delay based on the fixed ambiguity.

[0010] Step 3. The user transmits the relevant solution information back to the network RTK platform through an internal communication protocol or a standard communication protocol.

[0011] Step 4. The network RTK platform restores the ionospheric delay of the virtual baseline of the end user.

[0012] Step 5. Conduct consistency checks on the ionospheric delay modeling.

[0013] Step 6. The network RTK platform models the spatial atmospheric error.

[0014] Step 7. Generate and broadcast virtual observations.

[0015] The specific steps are as follows:

[0016] Step 1. The end user performs RTK positioning.

[0017] The terminal receives the virtual observations broadcast by the network RTK platform and performs short-baseline RTK positioning.

[0018] Step 2. The end user calculates the ionospheric delay based on the fixed ambiguity.

[0019] After the end user completes RTK positioning, the end user needs to calculate the ionospheric delay of each satellite after the ambiguity is fixed, as shown in the following formula.

[0020]

[0021] In the formula, and are the dual-frequency ambiguities after the terminal is fixed, and are the corresponding observations, f1 and f2 are the frequencies of the observations, and λ1 and λ2 are the wavelengths of the corresponding observations.

[0022] Step 3. The user transmits the relevant solution information back to the network RTK platform through an internal communication protocol or a standard communication protocol.

[0023] The user transmits the relevant solution information back to the network RTK platform through an internal communication protocol or a standard communication protocol. The communication protocols here include, but are not limited to, the existing TCP protocol and Ntrip protocol; the formats of the relevant solution information include, but are not limited to, the internally defined information coding format, the RTCM protocol and Rinex protocol that may be compatible in the future; the transmitted solution information includes, but is not limited to, the double-difference ionospheric delay, ionospheric delay accuracy, current reference satellite, current time index, current geometric position, data age, PDOP and other parameters.

[0024] Step 4. The network RTK platform restores the virtual baseline ionospheric delay of the end user.

[0025] Assume that there are several user terminals in the network elements as Figure 2 shown. If the master reference station of the current end user is the same as the master reference station of the current network element, and a virtual baseline is formed between the terminal and the master reference station, the double-difference ionospheric delay value of the virtual baseline can be obtained by combining the short baseline ionospheric delay from the VRS point to the terminal and the ionospheric modeling value from the VRS point to the master reference station, as shown in the following formula:

[0026]

[0027] If the master reference station of the current end user is different from the master reference station of the current network element, the ionospheric delay of the end user needs to be converted, as shown in the following formula:

[0028]

[0029] In the formula, are the extracted values of the double-difference ionospheric delay of the user virtual baseline with A and B as the master stations respectively;

[0030] is the original interpolation result of the master station at the VRS position; is the ionospheric delay of the short baseline formed by the user and the VRS point position, including the interpolation residual within the network element and the actual ionospheric delay of the short baseline.

[0031] Step 5. Consistency test of ionospheric delay modeling.

[0032] After obtaining the ionospheric delay of the virtual baseline, the clustering algorithm is used to classify the user aggregation range, and the outlier test is performed on the user virtual baseline values within a class. Considering that mainly based on the interpolation residual within the network element, which should theoretically follow a normal distribution, the Box-Cox transformation combined with Grubbs’ Test is used to detect outliers.

[0033]

[0034] In the formula, is the interpolation residual of the ionospheric delay of a single user after Box-Cox transformation within a class; N is the number of users within a class; represents the critical value of the t-distribution with (N - 2) degrees of freedom and the significance level of α / (2N).

[0035] Step 6. Crowdsourcing-based spatial atmospheric error modeling of the network RTK platform.

[0036] Assume that the double-difference ambiguities solved on two carrier frequencies between baselines are respectively and the double-difference tropospheric error for each satellite pair on the baseline can be solved by the following formula

[0037]

[0038] In the formula, and are the dual-frequency ambiguities after the terminal is fixed, and are the corresponding observations, f1 and f2 are the frequencies of the observations, and λ1 and λ2 are the wavelengths of the corresponding observations, is the geometric station-satellite distance.

[0039] For the spatial modeling of the error, the ionospheric error modeling can be carried out using different models according to the current user's location (for example: LIM, DIM, LCM, LSM, LSC, Kriging interpolation method, etc.). Here, the LIM model is taken as an example:

[0040]

[0041] In the formula, the subscripts 1, 2…n-1 represent the auxiliary reference stations or terminal users participating in the modeling; n represents the main reference station; Δx i,n and Δy i,n (i = 1, 2, 3,..., t-1) represent the differences in the planar positions between the n-1 auxiliary reference stations or users and the main reference station; the parameters a and b are the model coefficients to be determined. When the number of reference stations is greater than 3, by combining the ionospheric delay of the terminal user, the coefficients a and b can be obtained by solving formula (3).

[0042] Considering the spatio-temporal correlation characteristics of the troposphere, the tropospheric delay at the user's location in the reference station network can be obtained by interpolating the tropospheric delays of nearby reference stations. The interpolation method is as follows:

[0043]

[0044] In the formula, represents the tropospheric delay of the user's location along the slant path to satellite s, represents the tropospheric delay of reference station k along the slant path to satellite s calculated by formula (5), a k represents the interpolation coefficient, and satisfies the following relationship:

[0045]

[0046] In the formula, d k represents the distance between the user and reference station k.

[0047] The above non-differential tropospheric modeling method directly models the tropospheric delay itself. Considering that the magnitude of the tropospheric delay itself is relatively large, while the residual tropospheric delay after correction by the prior model is generally small. Therefore, modeling and compensating for the error value of the prior model is expected to further improve the interpolation accuracy of the troposphere. The above equation is rewritten in the following form:

[0048]

[0049] where represents the tropospheric delay of the prior model on the inclined path between the reference station k and the satellite s.

[0050] In the ground-based augmentation system, the reference station network can usually track multiple common-view satellites. For the convenience of description, i and j are used to represent the reference satellite and the non-reference satellite respectively, and the above equation is written in the form of the inter-satellite single difference:

[0051]

[0052] By the approximate position uploaded by the user, the reference station closest to the user is selected as the main reference station. For the convenience of description, the first reference station is used as the main reference station, that is, r = 1. Then the double-difference tropospheric delay between the main reference station and the user can be expressed as:

[0053]

[0054] Integrating the above two formulas, we can get

[0055]

[0056] At the same time, considering the coefficient a k satisfies the relationship in the above formula which is equivalently expressed as:

[0057]

[0058] Furthermore, integrating and simplifying the above two formulas, the final interpolation model can be obtained:

[0059]

[0060] In the above formula, the first term on the right side of the equal sign is the double-difference tropospheric delay between the main reference station and the auxiliary station, which can be extracted by the ionosphere-free combination through baseline solution between reference stations; the second term is the model value of the double-difference tropospheric delay between the user and each reference station, which can be calculated by the prior model, such as UNB3m, GPT2w, etc.

[0061] For the user position g, the double-difference tropospheric delay value can be obtained according to the formula

[0062]

[0063] Step 7. Virtual observation value generation.

[0064] After completing the ionospheric delay and tropospheric delay modeling, the virtual observation value of the user position is generated according to the following formula

[0065]

[0066] In the formula, g and t represent the user station and the main reference station respectively; P and are the pseudorange and carrier observations respectively; Δρ is the difference in the satellite-to-station distance between the end user and the main reference station corresponding to the same satellite; λ represents the wavelength; the subscript j represents the frequency.

[0067] Send this virtual observation value to the end user through the standard protocol, and the end user executes Step 1 to resolve the ambiguity and perform RTK positioning.

[0068] The beneficial effects of the present invention are as follows:

[0069] The real-time positioning enhancement method based on platform-user interaction feedback proposed by the present invention realizes two-way information interaction between the network RTK platform and the end user. Compared with the unidirectional service of the conventional network RTK, the atmospheric modeling effect is greatly improved under long baselines, large obtuse angles, narrow triangular networks, and active atmospheric conditions, effectively improving the RTK positioning performance of the end user and realizing the positioning enhancement of the end user. Description of the Drawings

[0070] Figure 1 is the implementation flowchart of the present invention;

[0071] Figure 2 is the schematic diagram of the virtual baseline of the network RTK for restoring the end user;

[0072] Figure 3 is the effect diagram of ionospheric delay modeling using the method of the present invention. Detailed Embodiments

[0073] The following further clarifies the present invention in conjunction with the drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and not to limit the scope of the present invention.

[0074] As shown in the figure, this embodiment discloses a real-time positioning enhancement method based on platform-user interaction feedback, and the specific steps are as follows:

[0075] Step 1, the end user performs RTK positioning.

[0076] The terminal receives the virtual observation value broadcast by the network RTK platform and performs short-baseline RTK positioning.

[0077] Step 2. The end user calculates the ionospheric delay based on the fixed ambiguity.

[0078] After the end user completes RTK positioning, the end user needs to calculate the ionospheric delay of each satellite in reverse after the ambiguity is fixed, as shown in the following formula.

[0079]

[0080] In the formula, and are the dual-frequency ambiguities after being fixed by the end user, and are the corresponding observations, f1 and f2 are the frequencies of the observations, and λ1 and λ2 are the wavelengths of the corresponding observations.

[0081] Step 3. The user uploads the relevant solution information back to the network RTK platform through an internal communication protocol or a standard communication protocol.

[0082] The user uploads the relevant solution information back to the network RTK platform through an internal communication protocol or a standard communication protocol. Here, the communication protocols include but are not limited to the existing TCP protocol, Ntrip protocol; the formats of the relevant solution information include but are not limited to the internally defined information coding format, the RTCM protocol and Rinex protocol that may be compatible in the future, etc.; the uploaded solution information includes but is not limited to the double-difference ionospheric delay, ionospheric delay accuracy, current reference satellite, current time index, current geometric position, data age, PDOP and other parameters.

[0083] Step 4. The network RTK platform restores the ionospheric delay of the virtual baseline of the end user.

[0084] Assume that there are several user terminals in the network element as shown in Figure 2 , and the virtual baselines they construct are as shown in Figure 2 . If the main reference station of the current end user is the same as the main reference station of the current network element, by combining the short-baseline ionospheric delay from the VRS point to the terminal and the ionospheric modeling value from the VRS point to the main reference station, the double-difference ionospheric delay value of the virtual baseline can be obtained, as shown in the following formula:

[0085]

[0086] If the main reference station of the current end user is different from the main reference station of the current network element, then the ionospheric delay of the end user needs to be converted, as shown in the following formula:

[0087]

[0088] In the formula, They are the extracted values of the virtual baseline dual-difference ionospheric delay for users with A and B as the master stations respectively; is the original interpolation result of the master station at the VRS position; is the ionospheric delay of the short baseline formed by the user and the VRS point position, including the interpolation residual of the network element and the actual ionospheric delay of the short baseline.

[0089] Step 5. Consistency test of ionospheric delay modeling.

[0090] After obtaining the ionospheric delay of the virtual baseline, use the clustering algorithm to classify the user aggregation range, and perform an outlier test on the virtual baseline values of users within a class. Considering that mainly based on the interpolation residual of the network element, it should theoretically follow a normal distribution, and the Box-Cox transformation combined with Grubbs’ Test is used to detect outliers.

[0091]

[0092] In the formula, is the interpolation residual of the ionospheric delay of a single user after Box-Cox transformation within the class; N is the number of users within the class; represents the critical value of the t-distribution with (N - 2) degrees of freedom and the significance level of α / (2N).

[0093] Step 6. Crowdsourcing-based spatial atmospheric error modeling for the network RTK platform.

[0094] Assume that the double-difference ambiguities solved on two carrier frequencies between baselines are respectively and Then the double-difference tropospheric error of each satellite pair on the baseline can be solved according to the following formula

[0095]

[0096] In the formula, and are the dual-frequency ambiguities after the terminal is fixed, and are the corresponding observations, f1 and f2 are the frequencies of the observations, λ1 and λ2 are the wavelengths of the corresponding observations, is the geometric station-satellite distance.

[0097] For the spatial modeling of errors, ionospheric error modeling can be carried out using different models according to the position of the current user (for example: LIM, DIM, LCM, LSM, LSC, Kriging interpolation method, etc.). Here, the LIM model is taken as an example:

[0098]

[0099] In the formula, the subscripts 1, 2…n - 1 represent the auxiliary reference stations or end - users participating in the modeling; n represents the master reference station; Δx i,n and Δy i,n (i = 1, 2, 3,..., t - 1) represent the differences in the planar positions between the n - 1 auxiliary reference stations or users and the master reference station; the parameters a and b are the model coefficients to be determined. When the number of reference stations is greater than 3, combining with the ionospheric delay of the end - user, the coefficients a and b can be obtained by solving Equation (3).

[0100] Considering the spatio - temporal correlation characteristics of the troposphere, the tropospheric delay at the user location in the reference station network can be interpolated from the tropospheric delays of nearby reference stations. The interpolation method is as follows:

[0101]

[0102] In the formula, represents the tropospheric delay of the slant path between the user location and satellite s, a k represents the interpolation coefficient and satisfies the following relationship:

[0103]

[0104] In the formula, d k represents the distance between the user and reference station k.

[0105] The above non - differential tropospheric modeling method directly models the tropospheric delay itself. Considering that the magnitude of the tropospheric delay itself is relatively large, while the residual tropospheric delay after prior model correction is generally small. Therefore, modeling and compensating for the error value of the prior model is expected to further improve the interpolation accuracy of the troposphere. The above equation is rewritten in the following form:

[0106]

[0107] In the ground - based augmentation system, the reference station network can usually track multiple co - visible satellites. For the convenience of description, i and j are used to represent the reference satellite and non - reference satellite respectively, and the above formula is written in the form of inter - satellite single - difference:

[0108]

[0109] Through the approximate position uploaded by the user, the reference station closest to the user is selected as the master reference station. For the convenience of description, taking the first reference station as the master reference station, that is, r = 1, then the double - difference tropospheric delay between the master reference station and the user can be expressed as:

[0110]

[0111] Integrating the above two formulas, we can get

[0112]

[0113] Meanwhile, considering the coefficient a k Satisfy the relationship in the above formula Equivalently expressed as:

[0114]

[0115] Furthermore, integrating and simplifying the above two formulas, the final interpolation model can be obtained:

[0116]

[0117] In the above formula, the first term on the right side of the equal sign is the double-difference tropospheric delay between the main reference station and the auxiliary station, which can be extracted by the ionosphere-free combination through the baseline solution between the reference stations; the second term is the model value of the double-difference tropospheric delay between the user and each reference station, which can be calculated by the prior model, such as UNB3m, GPT2w, etc.

[0118] For the user position g, the double-difference tropospheric delay value can be obtained according to the formula

[0119]

[0120] Step 7. Generation of virtual observations.

[0121] After completing the ionospheric delay and tropospheric delay modeling, generate the virtual observations of the user position according to the following formula

[0122]

[0123] In the formula, g and t represent the user station and the main reference station respectively; P and are the pseudorange and carrier observations respectively; Δρ is the difference in the station-satellite distance between the end user and the main reference station corresponding to the same satellite; λ represents the wavelength; the subscript j represents the frequency.

[0124] Send this virtual observation to the end user through the standard protocol, and the end user executes step 1 to resolve the ambiguity and perform RTK positioning.

[0125] Figure 2 It is a schematic diagram for the network RTK platform to restore the virtual baseline of the end user. By restoring the ionospheric delay between the end user and the virtual reference station to the ionospheric delay between the reference station and the end user, the restored ionospheric delay can then be used as the observation information to participate in the modeling.

[0126] Figure 3Comparison of the modeling effects of the crowdsourcing-based atmospheric error modeling method adopted in this paper with those of the conventional triangular network modeling under different numbers of users participating in the modeling.

[0127] It should be noted that the above content only illustrates the technical idea of the present invention and cannot be used to limit the protection scope of the present invention. For those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements all fall within the protection scope of the claims of the present invention.

Claims

1. A real-time positioning enhancement method based on platform-user interaction feedback, characterized in that, Including: Step 1: The end user performs RTK positioning; Step 2: The end user calculates the ionospheric delay according to the fixed ambiguity; After the end user completes RTK positioning, it is necessary for the end user to calculate the ionospheric delay of each satellite inversely after the ambiguity is fixed, as shown in the following formula In the formula, and are the dual-frequency ambiguities after the terminal is fixed, and are the corresponding observations, f1 and f2 are the frequencies of the observations, and λ1 and λ2 are the wavelengths of the corresponding observations; Step 3: The user uploads the relevant solution information back to the network RTK platform through the internal communication protocol or the standard communication protocol; The user uploads the relevant solution information back to the network RTK platform through the internal communication protocol or the standard communication protocol. The communication protocols here include but are not limited to the existing TCP protocol and Ntrip protocol; the formats of the relevant solution information include but are not limited to the internally customized information coding format, the RTCM protocol and Rinex protocol that may be compatible in the future; the uploaded solution information includes but is not limited to the double-difference ionospheric delay, the ionospheric delay accuracy, the current reference satellite, the current time index, the current geometric position, the data age, and the PDOP parameter; Step 4: The network RTK platform restores the ionospheric delay of the virtual baseline of the end user; Assume that there are several user terminals in the network element. The terminal and the main reference station form a virtual baseline. If the main reference station of the current end user is the same as the main reference station of the current network element, combining the short-baseline ionospheric delay from the VRS point to the terminal and the ionospheric modeling value from the VRS point to the main reference station, the double-difference ionospheric delay value of the virtual baseline can be obtained, as shown in the following formula: If the main reference station of the current end user is different from the main reference station of the current network element, then it is necessary to convert the ionospheric delay of the end user, as shown in the following formula: In the formula, are respectively the extracted values of the user virtual baseline double-difference ionospheric delay with A and B as the master stations; is the original interpolation result at the VRS position of the master station; is the ionospheric delay of the short baseline formed by the user and the VRS point position, including the interpolation residual within the network element and the actual ionospheric delay of the short baseline; Step 5: Consistency check of ionospheric delay modeling; After obtaining the ionospheric delay of the virtual baseline, the clustering algorithm is used to classify the user aggregation range, and the outlier test is performed on the virtual baseline values of the users within a class; Considering that Based on the interpolation residuals within the network element, it should theoretically follow a normal distribution, and the Box-Cox transformation combined with Grubbs’ Test is used to detect outliers; In the formula, is the interpolation residual of the ionospheric delay of a single user after Box-Cox transformation within the class; N is the number of users within the class; represents the critical value of the t-distribution with (N - 2) degrees of freedom and the significance level of α / (2N); Step 6: Space atmosphere error modeling of the network RTK platform; Step 7: Generation and broadcast of virtual observations.

2. The real-time positioning enhancement method based on platform-user interaction feedback according to claim 1, wherein The end user performs RTK positioning as described in Step 1 The terminal receives the virtual observations broadcast by the network RTK platform and performs short-baseline RTK positioning.

3. The real-time positioning enhancement method based on platform-user interaction feedback according to claim 1, characterized in that The space atmosphere error modeling of the network RTK platform as described in Step 6; Suppose the double-difference ambiguities solved at two carrier frequencies between baselines are respectively and Then the double-difference tropospheric error of each satellite pair on the baseline is solved according to the following formula In the formula, and are the double-frequency ambiguities after the terminals are fixed. and are the corresponding observed values, f1 and f2 are the frequencies of the observed values, and λ1 and λ2 are the wavelengths of the corresponding observed values. is the geometric station-satellite distance; For the spatial modeling of errors, the ionospheric error modeling uses different models for modeling according to the location of the current user. Here, the LIM model is taken as an example: In the formula, the subscripts 1, 2, …, n - 1 represent the auxiliary reference stations or end users participating in the modeling; n represents the main reference station; Δx i,n and Δy i,n represent the differences in the planar positions between the n - 1 auxiliary reference stations or users and the main reference station, where i = 1, 2, 3, ..., t - 1; the parameters a and b are the model coefficients to be determined; when the number of reference stations is greater than 3, by combining the ionospheric delays of the end users, the coefficients a and b are obtained by solving Equation (3). Considering the spatio-temporal correlation characteristics of the troposphere, the tropospheric delay at the user's location in the reference station network is obtained by interpolating the tropospheric delays of nearby reference stations. The interpolation method is as follows: In the formula, represents the tropospheric delay of the slant path between the user location and satellite s, represents the tropospheric delay of the slant path between reference station k and satellite s calculated by equation (5), a k represents the interpolation coefficient, and satisfies the following relationship: where d k represents the distance between the user and the reference station k; The above tropospheric modeling method directly models the tropospheric delay itself. Considering that the magnitude of the tropospheric delay itself is relatively large, while the residual tropospheric delay after prior model correction is generally small. Therefore, modeling and compensating the error value of the prior model is expected to further improve the interpolation accuracy of the troposphere. The above equation is rewritten in the following form: Among them represents the tropospheric delay of the prior model on the slant path between the reference station k and the satellite s; In the ground-based augmentation system, the reference station network can usually track multiple co-visible satellites. For the convenience of description, i and j are used to represent the reference satellite and the non-reference satellite respectively, and the above formula is written in the form of an inter-satellite single difference: Through the approximate position uploaded by the user, the reference station closest to the user is selected as the main reference station. For the convenience of description, the first reference station is used as the main reference station, that is, r = 1. Then the double-difference tropospheric delay between the main reference station and the user is expressed as: Integrating the above two formulas gives Also taking into account the coefficient a k Satisfy the relationship in the above formula, Equivalently expressed as: Furthermore, the above two formulas are integrated and simplified to obtain the final interpolation model: In the above formula, the first term on the right side of the equal sign is the double-difference tropospheric delay between the main reference station and the secondary station, which is extracted by the ionosphere-free combination through the baseline solution between the reference stations; the second term is the model value of the double-difference tropospheric delay between the user and each reference station, which is obtained through the prior model. For the user position g, the double-difference tropospheric delay value is obtained according to Equation (15).

4. The real-time positioning enhancement method based on platform-user interaction feedback according to claim 1, wherein The virtual observations described in step 7 are generated and broadcast. After completing the modeling of the ionospheric delay and the tropospheric delay, the virtual observations of the user position are generated according to the following formula. In the formula, g and t represent the user station and the main reference station respectively; P and are the pseudorange and the carrier observation respectively; Δρ is the difference in the station-satellite distance between the terminal user and the main reference station corresponding to the same satellite; λ represents the wavelength; The subscript j represents the frequency. This virtual observation is sent to the end user through the standard protocol, and the end user executes step 1 to resolve the ambiguity and perform RTK positioning.

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