A method and system for RTK positioning enhancement

Abstract

The application provides an RTK positioning enhancement method and system, which is used in the field of satellite navigation, and the method comprises the following steps: according to the reference station pseudorange observation data, phase observation value, real-time precise ephemeris, real-time clock difference and OSB product, the atmospheric delay parameter is solved, and the real ionospheric delay and the tropospheric delay in the atmospheric delay parameter are extracted; the dynamic error accuracy of each error source is estimated through the time series analysis method and the space interpolation method; the comprehensive error is calculated, the mathematical relationship model of the comprehensive error and the equivalent SNR is constructed, the equivalent SNR value is updated according to the dynamic error accuracy of each error source, the equivalent SNR is mapped into the SNR field in the RTK data format; the RTK data is encoded, the original measurement SNR value in the RTK data is replaced by the equivalent SNR, and the RTK data is broadcast to the user end. The scheme can realize the precision information transmission without changing the traditional RTK data format, guarantee the positioning reliability under the complex atmospheric condition, and improve the RTK positioning precision.

Description

Technical Field

[0001] The present invention belongs to the field of satellite navigation technology, and in particular relates to an RTK positioning enhancement method and system. Background Art

[0002] In recent years, BDS / GNSS ground-based augmentation systems have flourished across my country's provinces and municipalities. Furthermore, with the development of national infrastructure such as the "Land State Network" and the "National GNSS Continuously Operating Reference Station Network," the BDS / GNSS reference station network now covers the majority of my country's land area. Using raw BDS / GNSS observation data and GNSS observation equations, station coordinates can be accurately calculated. Against this backdrop, RTK and PPP-RTK technologies have gained widespread application in high-precision positioning.

[0003] Traditional RTK (Real-Time Kinematic, or dynamic carrier phase differential) methods eliminate most errors by differentiating user station observations with those of a base station. This eliminates the majority of errors during the solution, assuming orbital, satellite clock, and atmospheric errors are all zero. However, with the increasing adoption of RTK technology, its application scenarios are becoming increasingly complex. Under conditions of drastic atmospheric fluctuations, such as during extreme atmospheric events like geomagnetic storms, solar storms, and typhoons, atmospheric errors can be significant. Ignoring these errors can lead to significant positioning errors. Furthermore, traditional RTK data formats do not support the transmission of this precision information. For example, traditional RTK data formats do not include space for information on orbital, satellite clock, and atmospheric errors. Even if external errors such as orbital, clock, and atmospheric delay are effectively monitored, this precision information cannot be directly transmitted to the user end. Summary of the Invention

[0004] In view of this, an embodiment of the present invention provides an RTK positioning enhancement method and system for solving the problem that the current RTK positioning has large errors and cannot broadcast accuracy information.

[0005] In a first aspect of an embodiment of the present invention, a method for enhancing RTK positioning is provided, comprising:

[0006] Obtain the original BDS / GNSS pseudorange observation data and phase observation values ​​of the base station, and obtain the BDS / GNSS satellite real-time precise ephemeris, real-time clock difference and OSB products;

[0007] Based on pseudorange observation data, phase observation values, real-time precise ephemeris, real-time clock difference and OSB products, the atmospheric delay parameters are solved using the PPP algorithm, and the true ionospheric delay and tropospheric delay in the atmospheric delay parameters are extracted;

[0008] The dynamic error accuracy of each error source is estimated by time series analysis and spatial interpolation, and the error sources include ionospheric delay, tropospheric delay, satellite orbit error, real-time clock error and observation error;

[0009] The satellite orbit error is obtained based on real-time precise ephemeris, and the observation value error is output by the reference station;

[0010] Calculate the comprehensive error of each error source, build a mathematical relationship model between the comprehensive error and the equivalent SNR, update the equivalent SNR value based on the dynamic error accuracy of each error source, and map the equivalent SNR to the SNR field in the RTK data format;

[0011] Encode the RTK data according to the RTCM standard protocol, use the equivalent SNR to replace the original measured SNR value in the RTK data, and broadcast the RTK data to the user end.

[0012] In a second aspect of an embodiment of the present invention, an RTK positioning enhancement system is provided, including:

[0013] The data acquisition module is used to obtain the original BDS / GNSS pseudo-range observation data and phase observation values ​​of the base station, and obtain the BDS / GNSS satellite real-time precise ephemeris, real-time clock difference and OSB products;

[0014] The error calculation module is used to calculate the atmospheric delay parameters using the PPP algorithm based on pseudorange observation data, phase observation values, real-time precise ephemeris, real-time clock difference and OSB products, and extract the true ionospheric delay and tropospheric delay from the atmospheric delay parameters;

[0015] An accuracy estimation module is used to estimate the dynamic error accuracy of each error source through time series analysis and spatial interpolation methods. The error sources include ionospheric delay, tropospheric delay, satellite orbit error, real-time clock error and observation error;

[0016] The satellite orbit error is obtained based on real-time precise ephemeris, and the observation value error is output by the reference station;

[0017] The model building module is used to calculate the comprehensive error of each error source, build a mathematical relationship model between the comprehensive error and the equivalent SNR, update the equivalent SNR value according to the dynamic error accuracy of each error source, and map the equivalent SNR to the SNR field in the RTK data format;

[0018] The encoding and sending module is used to encode RTK data according to the RTCM standard protocol, replace the original measured SNR value in the RTK data with the equivalent SNR, and broadcast the RTK data to the user end.

[0019] In a third aspect of an embodiment of the present invention, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the steps of the method described in the first aspect of the embodiment of the present invention when executing the computer program.

[0020] In a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method provided in the first aspect of the embodiment of the present invention are implemented.

[0021] In this embodiment, atmospheric error parameters are extracted by calculation, and a mathematical relationship model between the error of each error source and the SNR is constructed. This effectively replaces the SNR field in traditional RTK data and broadcasts it. This improves RTK positioning accuracy without changing the traditional RTK architecture. By integrating error precision information into traditional RTK data, precision information transmission is achieved without modifying the traditional RTK data format, ensuring positioning reliability in complex atmospheric conditions. Furthermore, the real-time cost is low, and no hardware replacement is required. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0023] Figure 1 A flowchart of an RTK positioning enhancement method provided by one embodiment of the present invention;

[0024] Figure 2 Another schematic diagram of a flow chart of an RTK positioning enhancement method provided by one embodiment of the present invention;

[0025] Figure 3 A schematic structural diagram of an RTK positioning enhancement system provided by one embodiment of the present invention;

[0026] Figure 4 The present invention provides a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the inventive purposes, features and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the following described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0028] It should be understood that the terms "comprise", "comprising", "include", "including" and other similar expressions in the description of the present application or claims and the above drawings mean covering non-exclusive inclusion, such as a process, method or system, device comprising a series of steps or units, which is not limited to the listed steps or units. In addition, "first" and "second" are used to distinguish different objects, and are not used to describe a specific order.

[0029] Please refer to Figure 1 The RTK positioning enhancement method provided by the embodiment of the present application includes the following steps:

[0030] S101, obtaining original BDS / GNSS pseudorange observation data and phase observation value of a reference station, and obtaining real-time precise ephemeris, real-time clock difference and OSB product of a BDS / GNSS satellite;

[0031] The reference station is used for long-term continuous observation of satellite navigation signals, and can transmit observation data to a ground fixed observation station of a data center in real time or timing by communication facilities. The observation data generally includes pseudorange, carrier phase and the like. The pseudorange is the distance calculated by the propagation time of the ranging code signal in the satellite navigation system, and the carrier phase is the measurement value of the phase of the satellite signal received by the reference station at the same receiving time relative to the phase of the carrier signal generated by the receiver.

[0032] The data center can process the observation data of a plurality of reference stations to obtain the precise ephemeris and real-time clock difference of the satellite. The precise ephemeris contains satellite orbit information. The OSB (observable-specific signal bias, i.e. absolute signal bias) product is an error compensation technology used in GNSS satellite navigation positioning, which quantitatively characterizes the hardware delay error of a single signal in an absolute form. BDS / GNSS refers to Beidou (Beidou Navigation Satellite System) / Global Navigation Satellite System (Global Navigation Satellite System).

[0033] By obtaining the pseudorange observation data, phase observation value, real-time precise ephemeris, real-time clock difference and OSB product of the reference station, the coordinates of the reference station and the atmospheric delay parameters can be solved.

[0034] It can be understood that the OSB product is a prerequisite for PPP-AR (ambiguity fixation). Based on this product, the PPP fixed solution can be solved to shorten the convergence time.

[0035] S102, calculating the atmospheric delay parameters using the PPP algorithm based on the pseudorange observation data, phase observation values, real-time precise ephemeris, real-time clock difference, and OSB products, and extracting the true ionospheric delay and tropospheric delay from the atmospheric delay parameters;

[0036] The PPP (Precise Point Positioning) algorithm is a core technology for achieving high-precision global positioning using a single GNSS receiver. Based on pseudorange and carrier phase observations, it establishes a mathematical model that incorporates parameters such as satellite orbit error, clock error, ionospheric delay, and tropospheric delay. High-precision positioning and error parameter resolution are achieved through techniques such as ambiguity fixation and error processing. Atmospheric delay parameters refer to various parameters that affect the propagation of electromagnetic waves in the atmosphere, primarily including tropospheric and ionospheric delays. The atmospheric delay parameters calculated by the PPP algorithm differ from the actual atmospheric delay, necessitating the construction of corresponding models to extract the ionospheric and tropospheric delays separately.

[0037] The PPP algorithm adopts a non-differential and non-combination strategy to perform PPP solution.

[0038] The undifferenced and non-combined model can directly use the original observation values, retain all frequency band information, and estimate the parameters of the atmospheric delay. At the same time, it can avoid the accuracy loss caused by the combination of observation values, and can independently solve the error and equivalent SNR of each frequency point, improving flexibility and ensuring integrity.

[0039] Optionally, an ionospheric delay model is constructed, and the ionospheric delay is extracted based on the ionospheric delay model, where the ionospheric delay model is a spherical harmonic function;

[0040] A tropospheric delay model is constructed, and the tropospheric delay is extracted based on the tropospheric delay model, wherein the tropospheric delay model is composed of a Saastamoinen model and an exponential function model.

[0041] Exemplarily, constructing an ionospheric delay model , assuming that the ionospheric delay model is a spherical harmonic function, the vertical total electron content VTEC is the dependent variable, and the longitude and latitude of the puncture point are the independent variables, that is:

[0042] ;

[0043] Where VTEC is the vertical total electron content, n and m are the order and degree of the spherical harmonic function, respectively. max is the maximum order of spherical harmonics, is the normalized Legendre function, φ is the geomagnetic latitude of the puncture point, s is the solar longitude of the puncture point, 、 are the coefficients of spherical harmonics respectively.

[0044] Construct a tropospheric delay model and extract the tropospheric delay. The main part of the tropospheric delay model is the Saastamoinen model:

[0045] ;

[0046] Where, , is the latitude of the station, is the elevation of the measuring station, B and is a list function, E is the satellite elevation angle, P s and T s are atmospheric pressure and temperature respectively, both of which can be obtained using the standard meteorological element method.

[0047] Since the Saastamoinen model is not effective when the height difference is large, an exponential function model can be introduced to compensate for it. The exponential function model is:

[0048] ;

[0049] Where, is the reference elevation (such as mean sea level), and is the coefficient to be fitted, which can be obtained by fitting the tropospheric delay of the reference station network. The natural constant e is the base exponential function.

[0050] The final tropospheric delay model is:

[0051] ;

[0052] Where, represents the tropospheric projection function at the station coordinates, represents the Saastamoinen model tropospheric delay of the station when the elevation is 0, Indicates the station elevation h Exponential function model tropospheric delay on .

[0053] This embodiment can improve the universality of atmospheric delay parameter error estimation by extracting ionospheric delay and tropospheric delay separately through the model.

[0054] S103, estimating the dynamic error accuracy of each error source by time series analysis and spatial interpolation, wherein the error sources include ionospheric delay, tropospheric delay, satellite orbit error, real-time clock error, and observation error;

[0055] The satellite orbit error is obtained based on real-time precise ephemeris, and the observation error is output by the reference station. The observation error includes pseudorange observation error and phase observation error, which can be calculated and output by the reference station.

[0056] Time series analysis constructs statistical sequences by arranging historical observations of variables in chronological order, and uses mathematical models such as ARMA and ARIMA models to predict future trends. Spatial interpolation is a statistical method that converts measured data from discrete points into continuous data surfaces. It uses data from known points to infer values ​​in unknown areas, using methods such as linear combination models, linear interpolation models, inverse distance weighted models, and low-order surface models.

[0057] Preferably, the dynamic error accuracy of satellite orbit error, real-time clock difference and observation error is estimated in the time dimension by time series analysis method; the dynamic error accuracy of ionospheric delay and tropospheric delay is estimated in the spatial dimension by spatial interpolation method.

[0058] Exemplarily, the ionospheric delay and tropospheric delay are estimated using an improved linear interpolation method, and the specific interpolation formula is:

[0059] ;

[0060] ;

[0061] Can be shortened to: ;

[0062] Where n is the number of reference stations, is the interpolation coefficient, subscripts u and i represent the user station and reference station respectively, and represents the coordinates in the local plane coordinate system, and represents the plane coordinate difference between the user station and the reference station. The solution is:

[0063] ;

[0064] After obtaining the interpolation coefficients, the dynamic accuracy information of each error source can be estimated by interpolation in real time:

[0065] ;

[0066] Where, represents the interpolation accuracy of the undifferenced ionospheric delay or tropospheric delay at the reference station, Indicates the interpolated ionospheric or tropospheric delay interpolation accuracy at the user station.

[0067] S104: Calculate the comprehensive error of each error source, construct a mathematical relationship model between the comprehensive error and the equivalent SNR, update the equivalent SNR value according to the dynamic error accuracy of each error source, and map the equivalent SNR to the SNR field in the RTK data format;

[0068] The comprehensive error is composed of the errors of various error sources and can be obtained by adding the orbit error standard deviation, satellite clock error standard deviation, observation value error standard deviation, ionospheric delay error standard deviation and tropospheric delay error standard deviation.

[0069] Optionally, the error residual of each error source is calculated based on the observation value of the reference station, and the standard deviation of the error residual of each error source is calculated by the sliding window method; the sum of the standard deviations of the error residuals of each error source is used as the comprehensive error.

[0070] In this embodiment, the reference station network formed by each reference station can solve the error and corresponding residual of each error source, and the comprehensive error is calculated based on the error residual of each error source. The error residual can be obtained by the observation value of the reference station (i.e., the observation error) and the actual solved error.

[0071] The equivalent SNR (Signal-to-Noise Ratio) combines the SNR signals of the errors from each error source. After adding the combined error to the observed SNR, it is mapped to the SNR field in the RTK data format for broadcast, which can improve positioning accuracy.

[0072] Among them, a mathematical relationship model between the comprehensive error standard deviation and the equivalent SNR is constructed, and the relationship model is expressed as:

[0073] ;

[0074] Where, is the equivalent SNR, S To measure SNR, represents the comprehensive error standard deviation, a, b, c These are parameters obtained through training and fitting of machine learning algorithms. e is a natural constant.

[0075] The Kalman filter is used to update the relationship model parameters online according to the dynamic error accuracy of each error source, and the equivalent SNR value is proportionally mapped to the SNR field of the traditional RTK data format for replacement and broadcasting.

[0076] S105, encode the RTK data according to the RTCM standard protocol, replace the original measurement SNR value in the RTK data with the equivalent SNR, and broadcast the RTK data to the user end.

[0077] The RTCM (Radio Technical Commission for Maritime Services) standard protocol is a communication protocol for improving the transmission efficiency and positioning accuracy of GNSS data. The RTK data is high-precision positioning data obtained through RTK technology (Real-Time Kinematic). The RTK technology can provide high-precision reference signals by using a reference station, and can calculate and correct the position error of the receiver in real time.

[0078] According to the RTCM standard protocol, the observation value and the equivalent SNR value are encoded, the equivalent SNR is embedded in the original SNR field, for example, the "signal strength" bit of the RTCM MSM7 message, and then the NTRIP, TCP, etc. Network protocol is used to broadcast data through wireless network.

[0079] Among them, the data frame format is compatible with the traditional RTCM 3.x protocol, ensuring seamless parsing by the user end.

[0080] In this embodiment, by constructing the relationship model between the error source error and the equivalent SNR, the real-time error is mapped to the equivalent SNR, the precision information transmission is realized without modifying the traditional RTK data format, the existing user equipment is compatible, and the RTK positioning accuracy is improved. By dynamically updating the equivalent SNR value, the positioning solution weight is optimized, and the positioning divergence probability under complex atmospheric conditions is significantly reduced. Support for joint modeling of multiple error sources (orbit, clock error, ionosphere, and troposphere) improves the spatiotemporal consistency of the correction amount. Only the server needs to be updated to support enhanced services, and the user end does not need to upgrade the software algorithm and does not need to replace the hardware, with low implementation cost.

[0081] In one embodiment, as shown in Figure 2 the step S105 further includes:

[0082] S201, after the user end receives the RTK data, the observation value and the equivalent SNR information are obtained by parsing the RTK data;

[0083] The observation value refers to the observation value of the reference station, i.e. the pseudorange observation data and the carrier phase observation value. The RTK data includes observation value and equivalent SNR value. Based on the obtained observation value and equivalent SNR, the user end coordinates can be calculated by parsing the RTK data.

[0084] S202, based on the equivalent SNR information, an observation value random model is constructed, and the Kalman filter filtering weight is optimized;

[0085] Wherein, the observation value weight in the observation value random model is positively correlated with the equivalent SNR;

[0086] The observation weight is positively correlated with the equivalent SNR, which can suppress the impact of low-confidence correction on the solution.

[0087] Exemplarily, the observation value random model may be a signal-to-noise ratio random model, which is expressed as:

[0088] ;

[0089] Where C i is a constant, usually 1.61×10 4 (mm 2 ), is the equivalent SNR, represents the standard deviation of the observations.

[0090] S203: Calculate the precise coordinates of the user terminal through a Kalman filter based on the observation values ​​and the observation value random model.

[0091] Solve the Kalman filter to obtain floating-point ambiguity resolution and variance matrix. Based on the floating-point ambiguity resolution and variance matrix, use the LAMBDA algorithm to resolve integer ambiguities; update the variance-covariance matrix and coordinate parameters, and output centimeter-level positioning results.

[0092] It should be understood that the sequence numbers of the steps in the above embodiments do not imply a specific order of execution; the order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0093] Figure 3 A schematic diagram of the structure of an RTK positioning enhancement system provided in an embodiment of the present invention, the system comprising:

[0094] The data acquisition module 310 is used to obtain the original BDS / GNSS pseudo-range observation data and phase observation values ​​of the reference station, and obtain the BDS / GNSS satellite real-time precise ephemeris, real-time clock difference and OSB products;

[0095] The error calculation module 320 is used to calculate the atmospheric delay parameters using the PPP algorithm based on the pseudorange observation data, phase observation values, real-time precise ephemeris, real-time clock difference and OSB products, and extract the true ionospheric delay and tropospheric delay from the atmospheric delay parameters;

[0096] The PPP algorithm adopts a non-differential and non-combination strategy to perform PPP solution.

[0097] Optionally, extracting the true ionospheric delay and tropospheric delay from the atmospheric delay parameters includes:

[0098] The ionospheric delay model is constructed, and ionospheric delay is extracted based on the ionospheric delay model, and the ionospheric delay model is a spherical harmonic function.

[0099] The tropospheric delay model is constructed, and tropospheric delay is extracted based on the tropospheric delay model, and the tropospheric delay model is composed of a Saastamoinen model and an exponential function model.

[0100] The precision estimation module 330 is configured to estimate the dynamic error precision of each error source, including ionospheric delay, tropospheric delay, satellite orbit error, real-time clock difference and observation value error, by time series analysis and spatial interpolation method.

[0101] The satellite orbit error is obtained according to real-time precise ephemeris, and the observation value error is output by the reference station.

[0102] The precision estimation module 330 includes:

[0103] The time error estimation unit estimates the dynamic error precision of the satellite orbit error, the real-time clock difference and the observation value error in the time dimension by the time series analysis method.

[0104] The spatial error estimation unit estimates the dynamic error precision of the ionospheric delay and the tropospheric delay in the spatial dimension by the spatial interpolation method.

[0105] The model construction module 340 is configured to calculate the comprehensive error of each error source, construct a mathematical relationship model between the comprehensive error and the equivalent SNR, update the equivalent SNR value according to the dynamic error precision of each error source, and map the equivalent SNR to the SNR field in the RTK data format.

[0106] Optionally, the error residuals of each error source are calculated based on the observation values of the reference station, the standard deviations of the error residuals of each error source are calculated by the sliding window method, and the sum of the standard deviations of the error residuals of each error source is taken as the comprehensive error.

[0107] Specifically, the mathematical relationship model between the comprehensive error standard deviation and the equivalent SNR includes:

[0108] The mathematical relationship model between the comprehensive error standard deviation and the equivalent SNR is represented as:

[0109] ;

[0110] In the formula, The equivalent SNR is S The measurement SNR is The comprehensive error standard deviation is a, b, c The parameters are obtained by training and fitting the machine learning algorithm, e The natural constant is.

[0111] The encoding and sending module 350 is used to encode the RTK data according to the RTCM standard protocol, replace the original measured SNR value in the RTK data with the equivalent SNR, and broadcast the RTK data to the user end.

[0112] Optionally, the encoding and sending module 350 further includes:

[0113] The positioning solution module is used to parse the RTK data received by the user end to obtain observation values ​​and equivalent SNR information; construct an observation value random model based on the equivalent SNR information, and optimize the Kalman filter filtering weight; wherein the observation value weight in the observation value random model is positively correlated with the equivalent SNR; based on the observation value and the observation value random model, the Kalman filter is used to solve the user end's precise coordinates.

[0114] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0115] Figure 4 1 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device is used for RTK positioning services. Figure 4 As shown, the electronic device 4 of this embodiment includes: a memory 410, a processor 420 and a system bus 430, and the memory 410 includes an executable program 4101 stored thereon. It can be understood by those skilled in the art that Figure 4 The electronic device structure shown in the figure does not constitute a limitation to the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0116] The following combination Figure 4 A detailed introduction to the various components of electronic equipment:

[0117] Memory 410 can be used to store software programs and modules. Processor 420 executes the software programs and modules stored in memory 410 to perform various functional applications and data processing of the electronic device. Memory 410 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as sound playback or image playback). The data storage area may store data generated based on the use of the electronic device (such as cached data). Memory 410 may also include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state memory device.

[0118] The memory 410 includes an executable program 4101 for the interface generation method. The executable program 4101 can be divided into one or more modules / units. The one or more modules / units are stored in the memory 410 and executed by the processor 420 to implement RTK positioning, etc. The one or more modules / units can be a series of computer program instruction segments that can perform specific functions. The instruction segments are used to describe the execution process of the executable program 4101 in the electronic device 4. For example, the executable program 4101 can be divided into functional modules such as a data acquisition module, an error resolution module, an accuracy estimation module, a model construction module, and an encoding and transmission module.

[0119] Processor 420 is the control center of the electronic device, connecting the various components of the entire electronic device using various interfaces and lines. By running or executing software programs and / or modules stored in memory 410 and accessing data stored in memory 410, it performs various functions of the electronic device and processes data, thereby monitoring the overall status of the electronic device. Optionally, processor 420 may include one or more processing units; preferably, processor 420 may integrate an application processor and a modem processor, wherein the application processor primarily processes the operating system, application programs, etc., and the modem processor primarily handles wireless communications. It is understood that the modem processor described above may not be integrated into processor 420.

[0120] The system bus 430 connects the various functional components within the computer and can transmit data, address information, and control information. It can be a PCI bus, an ISA bus, a CAN bus, or other types. Instructions from the processor 420 are transmitted to the memory 410 via the bus, and the memory 410 feeds data back to the processor 420. The system bus 430 is responsible for the exchange of data and instructions between the processor 420 and the memory 410. Of course, the system bus 430 can also connect to other devices, such as network interfaces and display devices.

[0121] In an embodiment of the present invention, the executable program executed by the processing 420 included in the electronic device includes:

[0122] Obtain the original BDS / GNSS pseudorange observation data and phase observation values ​​of the base station, and obtain the BDS / GNSS satellite real-time precise ephemeris, real-time clock difference and OSB products;

[0123] Based on pseudorange observation data, phase observation values, real-time precise ephemeris, real-time clock difference and OSB products, the atmospheric delay parameters are solved using the PPP algorithm, and the true ionospheric delay and tropospheric delay in the atmospheric delay parameters are extracted;

[0124] The dynamic error accuracy of each error source is estimated by time series analysis and spatial interpolation, and the error sources include ionospheric delay, tropospheric delay, satellite orbit error, real-time clock error and observation error;

[0125] The satellite orbit error is obtained based on the real-time precise ephemeris, and the observation value error is output by the reference station;

[0126] Calculate the comprehensive error of each error source, build a mathematical relationship model between the comprehensive error and the equivalent SNR, update the equivalent SNR value based on the dynamic error accuracy of each error source, and map the equivalent SNR to the SNR field in the RTK data format;

[0127] Encode the RTK data according to the RTCM standard protocol, use the equivalent SNR to replace the original measured SNR value in the RTK data, and broadcast the RTK data to the user end.

[0128] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0129] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0130] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A RTK positioning enhancement method, characterized in that: include: Obtain the original BDS / GNSS pseudorange observation data and phase observation values ​​of the base station, and obtain the BDS / GNSS satellite real-time precise ephemeris, real-time clock difference and OSB products; Based on pseudorange observation data, phase observation values, real-time precise ephemeris, real-time clock difference and OSB products, the atmospheric delay parameters are solved using the PPP algorithm, and the true ionospheric delay and tropospheric delay in the atmospheric delay parameters are extracted; The dynamic error accuracy of each error source is estimated by time series analysis and spatial interpolation, and the error sources include ionospheric delay, tropospheric delay, satellite orbit error, real-time clock error and observation error; The satellite orbit error is obtained based on the real-time precise ephemeris, and the observation value error is output by the reference station; Calculate the comprehensive error of each error source, build a mathematical relationship model between the comprehensive error and the equivalent SNR, update the equivalent SNR value based on the dynamic error accuracy of each error source, and map the equivalent SNR to the SNR field in the RTK data format; Encode the RTK data according to the RTCM standard protocol, use the equivalent SNR to replace the original measured SNR value in the RTK data, and broadcast the RTK data to the user end.

2. The method according to claim 1, characterized in that The PPP algorithm adopts a non-differential and non-combination strategy to perform PPP solution.

3. The method according to claim 1, characterized in that The extraction of the true ionospheric delay and tropospheric delay in the atmospheric delay parameters comprises: constructing an ionospheric delay model, and extracting ionospheric delay based on the ionospheric delay model, wherein the ionospheric delay model is a spherical harmonic function; A tropospheric delay model is constructed, and the tropospheric delay is extracted based on the tropospheric delay model, wherein the tropospheric delay model is composed of a Saastamoinen model and an exponential function model.

4. The method according to claim 1, wherein The method of estimating the dynamic error accuracy of each error source by using the time series analysis method and the spatial interpolation method includes: The dynamic error accuracy of satellite orbit error, real-time clock error and observation error is estimated in the time dimension through time series analysis method; The dynamic error accuracy of estimating ionospheric delay and tropospheric delay in the spatial dimension is estimated by spatial interpolation method.

5. The method according to claim 1, wherein The calculation of the comprehensive error of each error source includes: The error residuals of each error source are calculated based on the observation values ​​of the reference station, and the standard deviation of the error residuals of each error source is calculated using the sliding window method; The sum of the standard deviations of the error residuals of each error source is taken as the comprehensive error.

6. The method according to claim 1, characterized in that The mathematical relationship model between the comprehensive error and the equivalent SNR is constructed as follows: Construct a mathematical relationship model between the comprehensive error standard deviation and the equivalent SNR. The mathematical relationship model is expressed as: ； Where, is the equivalent SNR, S To measure SNR, represents the comprehensive error standard deviation, a, b, c These are parameters obtained through training and fitting of machine learning algorithms. e is a natural constant.

7. The method according to claim 1, characterized in that The encoding of the RTK data according to the RTCM standard protocol, replacing the original measured SNR value in the RTK data with the equivalent SNR, and broadcasting the RTK data to the user terminal also includes: After receiving the RTK data, the user end parses the RTK data to obtain the observation value and equivalent SNR information, builds the observation value random model based on the equivalent SNR information, and optimizes the Kalman filter weight; Wherein, the observation value weight in the observation value random model is positively correlated with the equivalent SNR; Based on the observation values ​​and the observation value random model, the Kalman filter is used to solve the precise coordinates of the user end.

8. An RTK positioning enhancement system, characterized in that: include: The data acquisition module is used to obtain the original BDS / GNSS pseudo-range observation data and phase observation values ​​of the base station, and obtain the BDS / GNSS satellite real-time precise ephemeris, real-time clock difference and OSB products; The error calculation module is used to calculate the atmospheric delay parameters using the PPP algorithm based on pseudorange observation data, phase observation values, real-time precise ephemeris, real-time clock difference and OSB products, and extract the true ionospheric delay and tropospheric delay from the atmospheric delay parameters; An accuracy estimation module is used to estimate the dynamic error accuracy of each error source through time series analysis and spatial interpolation methods. The error sources include ionospheric delay, tropospheric delay, satellite orbit error, real-time clock error and observation error; The satellite orbit error is obtained based on the real-time precise ephemeris, and the observation value error is output by the reference station; The model building module is used to calculate the comprehensive error of each error source, build a mathematical relationship model between the comprehensive error and the equivalent SNR, update the equivalent SNR value according to the dynamic error accuracy of each error source, and map the equivalent SNR to the SNR field in the RTK data format; The encoding and sending module is used to encode RTK data according to the RTCM standard protocol, replace the original measured SNR value in the RTK data with the equivalent SNR, and broadcast the RTK data to the user end.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the RTK positioning enhancement method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed, the steps of the RTK positioning enhancement method according to any one of claims 1 to 7 are implemented.

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