A PPP-RTK real-time positioning method and system for Android mobile devices

Abstract

The present invention discloses a PPP-RTK real-time positioning method and system for an Android mobile device, including obtaining GNSS observation values ​​by acquiring the underlying raw data of the device, externally connecting broadcast ephemeris and SSR products, matching the GNSS observation values ​​with real-time product parameters, and if there is a reference station, using the ionosphere value and troposphere value obtained after matching as observation values ​​to perform PPP-RTK solution using a non-combined model to eliminate the influence of the ionosphere and troposphere; if there is no reference station, using the ionosphere-free combined IF model to perform PPP-RTK solution on the dual-frequency observation values ​​obtained after matching, and using the non-combined model to perform PPP-RTK solution on the single-frequency observation values ​​obtained after matching to eliminate the influence of the ionosphere; and then performing Kalman filtering solution to obtain the high-precision position of the Android mobile device. Different PPP-RTK processing methods are used for ionospheric products with different precisions; and by making full use of the single- and dual-frequency observation values ​​of the Android mobile device, the user of the Android device can obtain fast convergence and high-precision positioning effects.

Description

Technical Field

[0001] The invention relates to GNSS positioning of a mobile device, and in particular to a PPP-RTK real-time positioning method for an Android mobile device. Background Art

[0002] Since Google opened the API of GNSS observation data of Android system, GNSS positioning of Android mobile devices has become a research hotspot. At present, the research on Android mobile devices mainly focuses on the quality analysis of GNSS observation data of Android phones, single point positioning (SPP), differential positioning (RTK), precise point positioning (PPP) and other aspects.

[0003] At present, the research on PPP of Android mobile phones is divided into two methods: post-event and real-time. The post-event positioning research mainly uses open source software (such as Geo++) to collect and record real-time data (pseudorange, carrier phase, Doppler, signal-to-noise ratio of each epoch), downloads post-event precise clock error, precise orbit, antenna correction file and other products from the website of relevant institutions (such as IGS), and performs positioning solution of mobile phone PPP; the real-time PPP research mainly makes a real-time positioning app, installs it on Android mobile devices, runs it in the HAL layer of the mobile phone, and obtains and converts the observation data of the Android mobile phone API interface in real time (pseudorange, carrier phase, Doppler, signal-to-noise ratio of the current epoch). In addition, the real-time data stream of precision products connected to the network (usually SSR corrections, including precise clock error, precise orbit, phase decimal deviation correction, etc.) is analyzed, and after time matching the observation data and precision products, precise single-point positioning of the current epoch is performed.

[0004] In the prior art, patent application CN104236579A discloses a method for realizing high-precision satellite navigation positioning based on the Android kernel layer. The method discloses the architecture and hierarchy of real-time positioning software running in the kernel of Android mobile devices, which is a common architecture for real-time GNSS positioning of Android devices. Guo Fei, Wu Weiwang, Zhang Xiaohong, et al. Implementation and accuracy analysis of real-time precision single-point positioning software for Android smartphones [J]. Journal of Wuhan University: Information Science Edition, 2021, 46(7):10. A method for PPP-RTK positioning of Android phones is disclosed, which uses ionospheric forecast products for atmospheric constraints and proposes the use of random models for Android phones. Gao Chengfa, Chen Bo, Liu Yongsheng. High-precision real-time dynamic positioning of GNSS for Android smartphones [J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(1):9. The method discloses the use of WHU ultra-fast ephemeris and the broadcast ionospheric model (Klobuchar model, with poor accuracy) to eliminate ionospheric errors and conduct PPP-RTK positioning research.

[0005] Compared with traditional measurement receivers, there are two difficulties in PPP-RTK high-precision positioning of Android mobile devices: 1. The GNSS antenna performance of Android mobile devices is weak, resulting in poor data quality output by the API interface. At the same time, the number of data observations output by Android devices is small, the data quality is relatively messy, and the number of satellites that meet dual-frequency observations is also small. 2. Android devices output fewer observations, and there may be rank deficiency or fewer observations during PPP positioning. Atmospheric products need to be added as observations to eliminate atmospheric parameters to be estimated for PPP-RTK positioning. However, the atmospheric products currently used in various schemes are ionosphere forecast products or broadcast ionosphere models, and the ionosphere parameters after convergence of PPP-RTK positioning of Android mobile devices may absorb other errors, which are not ionospheres in the physical sense. In addition, the ionosphere forecast model itself has a loss of forecast accuracy, and the correction efficiency of the broadcast model is even worse. This leads to the problem of slow convergence and poor accuracy of PPP-RTK of Android mobile devices with ionosphere constraints. Summary of the invention

[0006] Purpose of the invention: In view of the above shortcomings, the present invention provides a PPP-RTK real-time positioning method and system for an Android mobile device, which enables Android mobile device users to obtain fast, reliable, real-time and high-precision positioning.

[0007] Technical solution: To solve the above problems, the present invention adopts a PPP-RTK real-time positioning method for an Android mobile device, comprising the following steps:

[0008] (1) Obtain the underlying raw data of the Android mobile device and parse it to obtain GNSS observation values, including dual-frequency observation values ​​and single-frequency observation values;

[0009] (2) Externally connect broadcast ephemeris products, SSR products, and ionospheric forecast products, and obtain real-time product parameters of broadcast ephemeris products and SSR products, as well as product parameters of atmospheric products with lower reliability generated by ionospheric forecast products; at the same time, receive in real time product parameters of atmospheric products with higher reliability generated by base station data;

[0010] (3) Determine whether product parameters of atmospheric products with high reliability are received;

[0011] (4) Matching the obtained GNSS observation values ​​with the real-time product parameters to obtain matched data, including the precise clock error and precise orbit in the predicted SSR product; and performing spatial interpolation on the atmospheric products; if the product parameters of the atmospheric products with higher reliability are received, then the atmospheric products with higher reliability are spatially interpolated to obtain the ionosphere value and troposphere value at the location of the Android mobile device; if the product parameters of the atmospheric products with higher reliability are not received, then the atmospheric products with lower reliability are spatially interpolated to obtain the ionosphere value at the location of the Android mobile device;

[0012] (5) If the product parameters of the atmospheric product with higher reliability are received, the ionosphere value and the troposphere value obtained by matching the atmospheric product with higher reliability in step (4) are used as observation values ​​and the PPP-RTK solution is performed using the non-combined model to eliminate the influence of the ionosphere and the troposphere; if the product parameters of the atmospheric product with higher reliability are not received, the PPP-RTK solution is performed using the ionosphere-free combined IF model for the matched dual-frequency observation value and the non-combined model for the matched single-frequency observation value to eliminate the influence of the ionosphere;

[0013] (6) The solved observation values ​​are used to determine the width and narrowness of the lane through the ambiguity fixing module to obtain the high-precision position of the Android mobile device.

[0014] Furthermore, the specific steps in step (1) are:

[0015] (1.1) Use Java language to obtain the underlying raw data from the API interface of the Android mobile device, and set the thread startup frequency for obtaining raw data according to the raw data output interval;

[0016] (1.2) The raw data acquired each time is parsed in real time to convert it into GNSS observation values ​​directly used for GNSS positioning, including pseudorange observation values, carrier phase observation values, and Doppler observation values.

[0017] Furthermore, the external SSR products in step (2) also include precise orbit correction products, precise clock correction products, and phase decimal deviation UPD products.

[0018] Furthermore, in step (3), the precise clock error and precise orbit in the SSR product are predicted, and the observation time of the current epoch is t obs , the latest precise orbit correction number obtained is t orb 、The latest precise clock correction number is t clk , the precise track can be predicted for a duration of Thre orb , the time length of the precise clock error prediction is Thre clk , satisfying the relationship:

[0019] t obs -t orb <=Thre orb

[0020] t obs -t clk <=Thre clk

[0021] Furthermore, the non-combination model formula in step (5) is:

[0022]

[0023]

[0024]

[0025]

[0026] Among them, P1 and P2 represent the pseudorange observation values ​​of two frequencies, L1 and L2 represent the carrier phase observation values ​​of two frequencies, ρ represents the distance between the station and the satellite, c is the speed of light in vacuum, dt is the receiver clock error, dT is the satellite clock error, and T is the tropospheric delay error. is the ionospheric delay error on the L1 carrier observation value, γ is the ionospheric conversion factor of the two frequencies, λ1 and λ2 are the wavelengths of the two frequencies, B1 and B2 are the ambiguities in weeks on the two frequencies, is the multipath effect on two frequencies, is the pseudorange observation noise at two frequencies, is the multipath effect of the phase observations at two frequencies, is the phase observation noise at two frequencies.

[0027] Furthermore, the ionosphere-free combined IF model formula is:

[0028]

[0029] Among them, P IF is the pseudorange of the ionosphere-free combination, L IF is the phase observation value of the ionosphere-free combination, f1 and f2 represent two observation frequencies, B IF is the ionospheric-free combined ambiguity, dm represents the multipath effect of the pseudorange combined observation value, and δ m represents the multipath effect of the carrier combination observation value, ε P represents the combined noise of pseudorange observations, ε L represents the carrier observation combined noise.

[0030] Furthermore, when there is no reference station in step (5), the dual-frequency observation values ​​are combined without ionospheric separation to obtain a dual-frequency observation equation, and the single-frequency observation values ​​are non-differenced and non-combined to obtain a single-frequency observation equation; in the Kalman filter calculation of step (6), the variance of the dual-frequency observation equation is smaller than the variance of the single-frequency observation equation.

[0031] The present invention also adopts a PPP-RTK real-time positioning system for an Android mobile device, including a raw data acquisition and analysis module, a broadcast ephemeris product, an SSR product, an external product data acquisition and analysis module, a judgment module, a data matching module, and a PPP-RTK filtering and solving module; wherein:

[0032] The raw data acquisition and parsing module is used to obtain the underlying raw data of Android mobile devices and parse them to obtain GNSS observation values, including dual-frequency observation values ​​and single-frequency observation values;

[0033] The external product data acquisition and analysis module is used to obtain the real-time product parameters of broadcast ephemeris products and SSR products, as well as the product parameters of atmospheric products with low reliability generated by ionospheric forecast products; at the same time, it receives the product parameters of atmospheric products with high reliability generated by base station data in real time;

[0034] The judgment module is used to judge whether the product parameters of the atmospheric product with higher reliability are received;

[0035] The data matching module is used to match the obtained GNSS observation values ​​with the real-time product parameters to obtain the matched product parameters, including the precise clock error and precise orbit in the predicted SSR product; and to perform spatial interpolation on the atmospheric products; if the product parameters of the atmospheric products with higher reliability are received, the atmospheric products with higher reliability are spatially interpolated to obtain the ionosphere value and troposphere value at the location of the Android mobile device; if the product parameters of the atmospheric products with higher reliability are not received, the atmospheric products with lower reliability are spatially interpolated to obtain the ionosphere value at the location of the Android mobile device;

[0036] The PPP-RTK filtering solution module is used to use the non-combined model to perform PPP-RTK solution on the ionospheric values ​​and tropospheric values ​​obtained from the product parameters of the atmospheric products with higher reliability as observation values, thereby eliminating the influence of the ionosphere and troposphere; the ionospheric-free combined IF model is used to perform PPP-RTK solution on the dual-frequency observation values ​​obtained from the product parameters of the atmospheric products with higher reliability that are not received, and the non-combined model is used to perform PPP-RTK solution on the single-frequency observation values, thereby eliminating the influence of the ionosphere; and the ambiguity fixing module is used to perform wide and narrow lane judgment on the observation values ​​obtained by the PPP-RTK solution to obtain the high-precision position of the Android mobile device.

[0037] Beneficial effect: Compared with the prior art, the significant advantage of the present invention is that different PPP-RTK processing methods are adopted for ionospheric products with different precisions; and by making full use of the single and dual frequency observation values ​​of Android mobile devices, users of Android devices can obtain fast convergence and high-precision positioning effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flow chart of the PPP-RTK real-time positioning method of the present invention;

[0039] Figure 2 A flow chart for the generation and use of atmospheric products in the present invention;

[0040] Figure 3 It is a schematic diagram of the system architecture of the PPP-RTK real-time positioning system of the present invention. DETAILED DESCRIPTION

[0041] Example 1

[0042] Table 1 shows the parameters to be estimated for PPP positioning in the ionosphere-free combined (IF) and uncombined (UC) positioning modes. Assuming that there are N1 systems (GPS / BDS / GAL / GLO) participating in positioning, the total number of available satellites is N2, and the total number of parameters to be estimated is N. x ,but:

[0043] (1) For the ionosphere-free (IF) model:

[0044] N x =3+N1+1+N2

[0045] The IF model must use at least two-frequency observations, with a total of 2*N2 sets of observation equations, so N2 must satisfy:

[0046] N2>=3+N1+1

[0047] (2) For the unclash-free (UC) model:

[0048] N x =3+N1+1+2*N2+N2

[0049] The single-frequency observation value of the UC model has only 2*N2 sets of observation equations, 2*N2<N x , always in a state of loss.

[0050] There are a total of 4*N2 sets of observation equations for dual-frequency observations, so N2 must satisfy:

[0051] N2>=3+N1+1

[0052] If there are reliable atmospheric products, the ionosphere and troposphere products can be used as observations to reduce the number of parameters to be estimated. For example, only the ionosphere parameters are required:

[0053] N x =3+N1+1+N2

[0054] When N2>=3+N1+1, the full rank condition is met, and single frequency can effectively increase the observation quantity. Therefore, the ionospheric parameters have a great influence on the PPP-RTK positioning of Android devices.

[0055]

[0056] Table 1 Parameters to be estimated for each solution model

[0057] like Figure 1 As shown, a PPP-RTK real-time positioning method for an Android mobile device in this embodiment includes the following steps:

[0058] (1) Obtain GNSS observation values ​​in real time through the API interface open to the outside world on Android phones.

[0059] (1.1) Use Java language (currently API interface data can only be obtained by Java programs) to obtain the underlying raw data from the Android phone API interface. When obtaining raw data from an Android phone, set the thread startup frequency for obtaining raw data according to the raw data output interval (usually 1s) to ensure real-time data acquisition.

[0060] (1.2) Each time raw data is obtained, it is parsed in real time. 3. The raw data is transferred to the C++ code layer for parsing, and the parsed data is converted into GNSS observation values ​​that can be directly used for GNSS positioning, such as pseudorange observation values, carrier phase observation values, Doppler observation values, etc.

[0061] (2) Use Ntrip or TCP communication methods to connect to real-time products. After receiving the product data stream in real time, it needs to be parsed to obtain the product value.

[0062] The real-time products that need to be connected externally are mainly SSR (State Space Representation) products, which include products that output precise orbit corrections, precise clock corrections, atmospheric corrections, UPD (Phase Decimal Deviation), etc. Among the products provided by SSR, precise clock corrections and precise orbit products have a strong correlation with time; atmospheric corrections have a strong correlation with both time and space; UPD also has a certain correlation with time and space.

[0063] Unlike ordinary GNSS receivers, the broadcast ephemeris output by the bottom layer of Android mobile devices is usually of poor quality. It is necessary to use an external broadcast ephemeris product to obtain the approximate position of the satellite in the current epoch.

[0064] (3) Determine whether there are several reference stations in the service area of ​​the Android mobile device: If there is a regional self-built station, high-consistency atmospheric products can be generated and the product parameters of highly consistent atmospheric products can be received; if there is no regional self-built station, external ionospheric forecast products are used.

[0065] When you have a regional self-built station, you can generate atmospheric products. The atmospheric products generated by the server process the atmospheric parameters obtained by satellite atmospheric information to keep them consistent with the atmospheric parameters required by Android mobile devices, such as Figure 2 As shown in Figure 2, the generation process of atmospheric products is:

[0066] (3.1) Build multiple GNSS observation stations evenly distributed in the service area and transmit observation data to the solution center in real time

[0067] (3.2) The solution center makes a quality judgment on the transmitted data and performs PPP positioning on the data of the available measurement stations. The PPP positioning program here must be consistent with the Android phone positioning program (the estimation model and related parameters are consistent), and the positioning mode is set to static positioning to ensure the stability of the estimated parameters. After convergence, the ionosphere and troposphere estimation values ​​of each satellite corresponding to each measurement station are extracted.

[0068] (3.3) Based on the locations of each GNSS observation station, regional atmospheric modeling of the ionosphere and troposphere is performed, and then the model parameters are encoded and broadcast over the network.

[0069] (4) According to the time and approximate position of the current epoch observation value, the current observation data is matched with the external product, including time matching and space matching.

[0070] The generation time of the external product received in real time must be at the observation time t of the current epoch. obs Previously, the precise orbit and clock corrections in the SSR were always delayed relative to the GNSS observation time of the Android mobile device, so the precise clock and orbit needed to be predicted to match the observation time of the current epoch. Given that the precise clock and orbit have different stabilities and the predictable time lengths are inconsistent, the latest precise orbit correction received is t orb 、The latest precise clock correction number is t clk , the precise track can be predicted for a duration of Thre orb , the time length of the precise clock error prediction is Thre clk , then the relationship is satisfied:

[0071] t obs -t orb <=Thre orb

[0072] t obs -t clk <=Thre clk

[0073] In addition, for atmospheric products, they have a high degree of spatial and temporal correlation. When in use, the space is interpolated according to the approximate coordinates obtained by SPP, and the time processing is to expand its variance with the time difference to obtain the ionosphere value and troposphere value of the current position of the Android mobile device.

[0074] (5) Perform PPP-RTK processing on the Android phone:

[0075] (5.1) If there is a reference station, atmospheric products with high consistency can be obtained and solved using a non-combined model. Although the non-combined model has more parameters to be estimated, the real-time atmospheric products with high consistency can be used as observations, thereby reducing the number of parameters to be estimated and achieving rapid convergence. At the same time, single-frequency and dual-frequency observations can be mixed. The use of single-frequency observations can increase the amount of observations used to make positioning more robust. The formula of the non-combined model is as follows:

[0076]

[0077]

[0078]

[0079]

[0080] Among them, P1 and P2 represent the pseudorange observation values ​​of two frequencies, L1 and L2 represent the carrier phase observation values ​​of two frequencies, ρ represents the distance between the station and the satellite, c is the speed of light in vacuum, dt is the receiver clock error, dT is the satellite clock error, and T is the tropospheric delay error. is the ionospheric delay error on the L1 carrier observation, is the multipath effect on two frequencies, is the pseudorange observation noise at two frequencies, is the multipath effect of the phase observations at two frequencies, The noise of the phase observation values ​​on the two frequencies is eliminated. After eliminating the ionosphere and troposphere, the only parameters that need to be estimated are the three-dimensional coordinates, the receiver clock error, and the current frequency ambiguity.

[0081] After receiving the real-time atmospheric products, the ionosphere and troposphere values ​​of the mobile phone area are obtained by interpolation, and the relevant parameters are constrained. The variance of the ionosphere observation value is set according to the accuracy of the atmospheric product itself, the distance between the user and the atmospheric reference station, and the time delay. Variance of tropospheric observations

[0082] (5.2) If there is no reference station, there is a lack of real-time atmospheric products with high consistency, and ionospheric forecast products or broadcast ionospheric products are needed. In this case, the IF model is used for dual-frequency observations and the non-difference combination is used for single-frequency observations. The formula combination is as follows:

[0083]

[0084]

[0085]

[0086]

[0087] Among them, f1 and f2 represent two observation frequencies, dm represents the multipath effect of the pseudorange combination observation value, and δ m represents the multipath effect of the carrier combination observation value, ε P represents the combined noise of pseudorange observations, ε L represents the carrier observation combined noise.

[0088] When the consistency of atmospheric products is poor, using the atmosphere as an observation value will not only fail to speed up convergence, but will even lead to a further reduction in positioning accuracy. At the same time, in order to solve the problem that the unusable single-frequency observation value will lead to fewer available observations and waste of existing observation values, the solution model of the dual-frequency observation value uses the IF model. The IF model directly uses the dual-frequency combination to eliminate the first-order ionosphere, which can ensure the accuracy of positioning. At the same time, the solution of the single-frequency observation value uses a non-combination model, and the atmospheric product is used as the observation value, which can effectively prevent the equation rank deficiency problem and provide redundant observations to make the positioning more robust.

[0089] The dual-frequency combination of the IF model will lead to increased noise. When constructing the equation, a larger variance is usually given than that of the non-combined model. Since the accuracy of the ionospheric product in this embodiment is not enough (not consistent enough), the single-frequency equation with the ionosphere as the observation value will have poor accuracy. Therefore, the variance of the observation equation here should be represents the variance of the dual-frequency observation equation, Indicates the variance of the single-frequency observation equation to ensure that the observation equation of the IF combination has a higher weight, thereby improving the positioning accuracy of the Android device. The larger the variance, the lower the weight of the observation equation. In the subsequent Kalman filter calculation, Determines the influence of single-frequency and dual-frequency observation equations on positioning results.

[0090] Example 2

[0091] like Figure 3 As shown, in this embodiment, a PPP-RTK real-time positioning system for an Android mobile device includes a raw data acquisition and analysis module, a broadcast ephemeris product, an SSR product, an external product data acquisition and analysis module, a judgment module, a data matching module, and a PPP-RTK filtering and solving module; the raw data acquisition and analysis module obtains the underlying raw data of the device through an API interface open to the outside of the Android mobile phone, and parses to obtain GNSS observation values, including dual-frequency observation values ​​and single-frequency observation values;

[0092] The external product data acquisition and analysis module includes an Ntrip / TCP receiving module, which uses the Ntrip / TCP receiving module to receive the data stream of broadcast ephemeris products, SSR products, and ionospheric forecast products in real time. The external product data acquisition and analysis module performs analysis to obtain real-time product parameters; at the same time, it receives the product parameters of atmospheric products with high reliability generated by the base station data in real time;

[0093] The judgment module is used to determine whether the product parameters of the atmospheric product with high reliability are received; to determine the type of external product connected to the Android mobile device: if it is an atmospheric product calculated in real time by the base station in the service area, the accuracy and consistency of the atmospheric product are good; if it is an external ionospheric forecast product, the accuracy of the ionosphere is poor;

[0094] The data matching module is used to match the obtained GNSS observation values ​​with the real-time product parameters to obtain the matched product parameters, and predict the precise clock error and precise orbit in the SSR product through the SPP solution module and the atmospheric constraint module; and perform spatial interpolation on the atmospheric products or ionosphere, and perform spatial interpolation on the atmospheric products to obtain the ionosphere value and troposphere value of the Android mobile device;

[0095] The PPP-RTK filtering solution module is used to use the non-combined model to perform PPP-RTK solution on the ionosphere values ​​and troposphere values ​​obtained from the base station as observation values, eliminating the influence of the ionosphere and troposphere; the ionosphere-free combined IF model is used to perform PPP-RTK solution on the dual-frequency observation values ​​without the base station, and the non-combined model is used to perform PPP-RTK solution on the single-frequency observation values, eliminating the influence of the ionosphere; and the observation values ​​obtained by PPP-RTK solution are judged by the ambiguity fixing module. If the wide and narrow fixed lane is successfully determined, it is a fixed solution, and if the ambiguity fixation fails, it is a floating point solution, so as to obtain the high-precision position of the Android mobile device.

Claims

1. A PPP-RTK real-time positioning method for an Android mobile device, characterized in that: The following steps are involved: (1) Obtain the underlying raw data of the Android mobile device and parse it to obtain GNSS observation values, including dual-frequency observation values ​​and single-frequency observation values; (2) Externally connect broadcast ephemeris products, SSR products, and ionospheric forecast products, and obtain real-time product parameters of broadcast ephemeris products and SSR products, as well as product parameters of atmospheric products with lower reliability generated by ionospheric forecast products; at the same time, receive in real time product parameters of atmospheric products with higher reliability generated by base station data; (3) Determine whether product parameters of atmospheric products with high reliability are received; (4) Matching the obtained GNSS observation values ​​with the real-time product parameters to obtain matched data, including the precise clock error and precise orbit in the predicted SSR product; and performing spatial interpolation on the atmospheric products; if the product parameters of the atmospheric products with higher reliability are received, then the atmospheric products with higher reliability are spatially interpolated to obtain the ionosphere value and troposphere value at the location of the Android mobile device; if the product parameters of the atmospheric products with higher reliability are not received, then the atmospheric products with lower reliability are spatially interpolated to obtain the ionosphere value at the location of the Android mobile device; (5) If the product parameters of the atmospheric product with higher reliability are received, the ionosphere value and the troposphere value obtained after matching the atmospheric product with higher reliability in step (4) are used as observation values ​​and the PPP-RTK solution is performed using the non-combined model to eliminate the influence of the ionosphere and the troposphere; if the product parameters of the atmospheric product with higher reliability are not received, the dual-frequency observation value obtained after matching is used for PPP-RTK solution using the ionosphere-free combined IF model, and the single-frequency observation value obtained after matching is used for PPP-RTK solution using the non-combined model to eliminate the influence of the ionosphere; (6) The observation values ​​solved by PPP-RTK are used to determine the width and narrowness of the lane through the ambiguity fixing module to obtain the high-precision position of the Android mobile device.

2. The real-time positioning method according to claim 1, characterized in that: The specific steps in step (1) are: (1.1) Use Java language to obtain the underlying raw data from the API interface of the Android mobile device, and set the thread startup frequency for obtaining raw data according to the raw data output interval; (1.2) The raw data acquired each time is parsed in real time to convert it into GNSS observation values ​​directly used for GNSS positioning, including pseudorange observation values, carrier phase observation values, and Doppler observation values.

3. The real-time positioning method according to claim 1, characterized in that: The external SSR products in step (2) also include precise orbit correction products, precise clock correction products, and phase decimal deviation UPD products.

4. The real-time positioning method according to claim 1, characterized in that: In step (4), the precise clock error and precise orbit in the SSR product are predicted, and the observation time of the current epoch is t obs , the latest precise orbit correction number obtained is t orb 、The latest precise clock correction number is t clk , the precise track can be predicted for Thre orb , the time length of the precise clock error prediction is Thre clk , satisfying the relationship: t obs -t orb <=Thre orb t obs -t clk <=Thre clk。 5. The real-time positioning method according to claim 1, characterized in that: The non-combination model formula in step (5) is: Among them, P1 and P2 represent the pseudorange observation values ​​of two frequencies, L1 and L2 represent the carrier phase observation values ​​of two frequencies, ρ represents the distance between the station and the satellite, c is the speed of light in vacuum, dt is the receiver clock error, dT is the satellite clock error, and T is the tropospheric delay error. is the ionospheric delay error on the L1 carrier observation value, γ is the ionospheric conversion factor of the two frequencies, λ1 and λ2 are the wavelengths of the two frequencies, B1 and B2 are the ambiguities in weeks on the two frequencies, is the multipath effect on the two frequencies, is the pseudorange observation noise at two frequencies, is the multipath effect of the phase observations at two frequencies, is the phase observation noise at two frequencies.

6. The real-time positioning method according to claim 5, characterized in that: The ionosphere-free combined IF model formula is: Among them, P IF is the pseudorange of the ionosphere-free combination, L IF is the phase observation value of the ionosphere-free combination, f1 and f2 represent two observation frequencies, B IF is the ionospheric-free combined ambiguity, dm represents the multipath effect of the pseudorange combined observation value, and δ m represents the multipath effect of the carrier combination observation value, ε P represents the combined noise of pseudorange observations, ε L represents the carrier observation combined noise.

7. The real-time positioning method according to claim 1, characterized in that: When there is no reference station in step (5), the dual-frequency observation values ​​are combined without ionospheric separation to obtain the dual-frequency observation equation, and the single-frequency observation values ​​are non-differenced and non-combined to obtain the single-frequency observation equation; in the PPP-RTK solution of step (5), the variance of the dual-frequency observation equation is smaller than the variance of the single-frequency observation equation.

8. A PPP-RTK real-time positioning system for an Android mobile device, characterized in that: It includes raw data acquisition and analysis module, broadcast ephemeris product, SSR product, external product data acquisition and analysis module, judgment module, data matching module, and PPP-RTK filtering and solving module; among which: The raw data acquisition and parsing module is used to obtain the underlying raw data of Android mobile devices and parse them to obtain GNSS observation values, including dual-frequency observation values ​​and single-frequency observation values; The external product data acquisition and analysis module is used to obtain the real-time product parameters of broadcast ephemeris products and SSR products, as well as the product parameters of atmospheric products with low reliability generated by ionospheric forecast products; at the same time, it receives the product parameters of atmospheric products with high reliability generated by base station data in real time; The judgment module is used to judge whether the product parameters of the atmospheric product with higher reliability are received; The data matching module is used to match the obtained GNSS observation values ​​with the real-time product parameters to obtain matched data, including the precise clock error and precise orbit in the predicted SSR product; and to perform spatial interpolation on the atmospheric products; if the product parameters of the atmospheric products with higher reliability are received, the atmospheric products with higher reliability are spatially interpolated to obtain the ionosphere value and troposphere value at the location of the Android mobile device; if the product parameters of the atmospheric products with higher reliability are not received, the atmospheric products with lower reliability are spatially interpolated to obtain the ionosphere value at the location of the Android mobile device; The PPP-RTK filtering solution module is used to use the non-combined model to perform PPP-RTK solution on the ionospheric and tropospheric values ​​obtained from the product parameters of the atmospheric products with higher reliability as observation values, thereby eliminating the influence of the ionosphere and troposphere. The ionosphere-free combined IF model is used to perform PPP-RTK solution on the dual-frequency observation values ​​obtained from the product parameters of the atmospheric products with higher reliability that are not received. The single-frequency observation values ​​obtained after matching are solved by PPP-RTK using the non-combined model to eliminate the influence of the ionosphere. The observation values ​​obtained by PPP-RTK solution are judged by the ambiguity fixing module to obtain the high-precision position of the Android mobile device.

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