Star-based enhanced real-time troposphere estimation method based on robust quality control and adaptive forecasting, electronic equipment and medium

Abstract

The invention discloses a satellite-based enhanced real-time troposphere estimation method based on robust quality control and adaptive forecasting, which comprises the following steps: performing initial fitting based on a preset model to obtain a post-test residual error and an error in a unit weight, if the error in the unit weight is greater than a specified value, performing gross error detection and gross error elimination, and re-fitting to obtain a real-time troposphere estimation result; and finally, the final fitting coefficient is coded and broadcasted. By accurately identifying and removing the gross error, the troposphere product pollution caused by the gross error can be effectively avoided, and the precision and robustness of the troposphere product are improved. The adaptive model is adopted to forecast the troposphere, so that reduction of fitting observation stations of the troposphere product caused by short-time interruption of observation data flow of a monitoring station can be effectively avoided, and the data integrity rate of the fitting observation stations of the troposphere product is improved.

Description

Technical Field

[0001] The present invention relates to the field of aerospace technology, and particularly relates to a satellite-based augmentation real-time troposphere estimation method, an electronic device, and a medium based on robust quality control and adaptive prediction. Background Art

[0002] Real-time Precise Point Positioning (PPP) is a high-precision positioning technology based on the Global Navigation Satellite System (GNSS). It can achieve centimeter-level or even millimeter-level positioning accuracy globally using only a single receiver. Compared with Real-Time Kinematic (RTK) technology, PPP technology can determine the precise position of a user without relying on a ground reference station. Based on this, PPP technology is widely used in precision agriculture, geological disaster monitoring, aerospace, and other fields. However, the positioning convergence time of PPP technology is relatively long, about 15 minutes, that is, it needs to wait for a long time to determine the precise position, which limits its application in scenarios with high real-time requirements such as autonomous driving.

[0003] To shorten the positioning convergence duration, PPP technology can be combined with RTK technology to form PPP-RTK (Precise Point Positioning - Real Time Kinematic) technology. PPP-RTK technology extracts atmospheric delay information based on regional monitoring stations, which can significantly shorten the positioning convergence time, approximately reduced to 1 to 5 minutes.

[0004] The PPP-RTK server can be divided into two types: ground network broadcast, that is, ground-based augmentation, and satellite broadcast in the sky, that is, satellite-based augmentation. The former is limited by the network bandwidth, and the number of user accesses is also correspondingly limited. The latter broadcasts to ground users via satellite, and the number of user accesses is theoretically infinite. As an important part of atmospheric delay information, the accuracy of tropospheric delay information will affect the positioning accuracy of PPP-RTK. The bandwidth of satellite-based augmentation broadcast is limited. If all the tropospheric delay information of monitoring stations is directly broadcast, the data volume is large. Therefore, by fitting the tropospheric delay with a mathematical model and broadcasting the tropospheric fitting coefficients, the data broadcast volume can be significantly reduced. However, in existing fitting methods, once network delay or data interruption occurs, it is easy to cause the failure of real-time troposphere estimation, reduce the integrity rate of tropospheric delay fitting station data, and at the same time, the robustness of the polynomial fitting model is low. Using traditional gross error detection methods, such as the maximum a posteriori residual method, the misjudgment rate is high, and it is difficult to accurately identify the gross error value. Summary of the Invention

[0005] In view of some or all of the problems in the prior art, a first aspect of the present invention provides a satellite-based augmentation real-time troposphere estimation method based on robust quality control and adaptive prediction, including:

[0006] Perform an initial fitting based on a preset model to obtain the a posteriori residuals and the mean error of unit weight;

[0007] If the mean error of unit weight is greater than a specified value, conduct gross error detection and eliminate the gross error; and

[0008] Encode the fitting coefficients and broadcast them.

[0009] Further, the preset model is:

[0010] ZWD = C0 + C1·dB + C2·dL + C3·H,

[0011] where ZWD is the tropospheric wet delay of the monitoring station, C0, C1, C2, and C3 are tropospheric fitting coefficients, i.e., the tropospheric products broadcast by satellite-based augmentation, dB and dL respectively represent the differences in latitude and longitude between the monitoring station and the fitting center point of the surveyed area, and H is the elevation of the monitoring station, with the unit of kilometer.

[0012] Further, the tropospheric wet delay is obtained through undifferenced and non-combined estimation.

[0013] Further, if the data stream of the monitoring station is interrupted and the interruption duration is within a specified range, the most recent valid tropospheric delay is used as the prediction value for the current epoch. If the interruption duration is greater than the preset duration, the corresponding monitoring station is removed from the fitting monitoring station list.

[0014] Further, the specified range is less than 5 minutes, and / or the preset duration is 5 minutes.

[0015] Further, the satellite-based augmentation real-time troposphere estimation method further includes:

[0016] Before performing the initial fitting, align the data of each monitoring station through timestamps and eliminate the values beyond the preset range.

[0017] Further, the initial fitting is performed based on the preset model using the least squares method.

[0018] Further, the elimination of gross errors includes:

[0019] Eliminate the data of the i-th station in sequence, re-fit and calculate the new mean error of unit weight;

[0020] Determine the station that minimizes the mean error of unit weight as the gross error; and

[0021] Repeat the elimination of gross errors and re-fitting until the mean error of unit weight is not greater than the specified value.

[0022] Further, encoding the fitting coefficients includes:

[0023] Converting the fitting coefficients into a binary format, encoding them according to the satellite-based augmentation protocol, and attaching a timestamp, a region ID, and a data validity flag.

[0024] A second aspect of the present invention provides a satellite-based augmentation real-time troposphere estimation system, including:

[0025] A data acquisition module, which is used to acquire data of a monitoring station, where the data includes a real-time observation data stream of the monitoring station, real-time precise orbit and clock offset products, real-time broadcast ephemeris products, and the latitude, longitude, and elevation of the monitoring station;

[0026] An adaptive prediction module, which is used to predict the tropospheric delay when the data of the monitoring station is interrupted;

[0027] A quality control module, which is communicatively connected to the data acquisition module and the adaptive prediction module, and is used to eliminate gross errors and obtain the final fitting coefficients; and

[0028] A data encoding module, which is communicatively connected to the quality control module and is used to encode the final fitting coefficients.

[0029] Based on the satellite-based augmentation real-time troposphere estimation method as described above, a third aspect of the present invention provides an electronic device for estimating the real-time troposphere, which includes a memory and a processor, where the memory is configured to store a computer program, and the computer program executes the satellite-based augmentation real-time troposphere estimation method as described above when running on the processor.

[0030] A fourth aspect of the present invention further provides a computer-readable storage medium for estimating the real-time troposphere, which stores a computer program, and the computer program executes the satellite-based augmentation real-time troposphere estimation method as described above when running on a processor.

[0031] A satellite-based augmentation real-time troposphere estimation method based on robust quality control and adaptive prediction provided by the present invention estimates the zenith wet delay (ZWD) of a GNSS monitoring station through a non-differenced and non-combined real-time precise positioning algorithm, fits the quality-controlled ZWD of the monitoring stations in the monitoring area through a low-order polynomial model containing an elevation factor, and at the same time, for the monitoring stations with short-term interruption of the data stream, an adaptive model is used to predict the ZWD, effectively improving the integrity rate of the tropospheric delay fitting station data. In addition, for the gross errors of the ZWD of the GNSS monitoring stations, a robust DIA (detection, identification, adjustment) quality control method is used to accurately identify and eliminate the gross errors, effectively avoiding the pollution of the tropospheric products caused by the gross errors, and improving the accuracy and robustness of the tropospheric products. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] To further clarify the above and other advantages and features of the embodiments of the present invention, a more specific description of the embodiments of the present invention will be presented with reference to the accompanying drawings. It can be understood that these drawings only depict typical embodiments of the present invention and thus will not be considered as limiting its scope. In the drawings, for clarity, the same or corresponding components will be denoted by the same or similar reference numerals.

[0033] Figure 1 Schematic structural diagram of a satellite-based augmentation real-time troposphere estimation system based on robust quality control and adaptive prediction, showing an embodiment of the present invention;

[0034] Figure 2 Schematic flow diagram of a satellite-based augmentation real-time troposphere estimation method based on robust quality control and adaptive prediction, showing an embodiment of the present invention;

[0035] Figure 3 Schematic distribution diagram of monitoring stations, showing an embodiment of the present invention;

[0036] Figure 4a and 4b Schematic diagram showing the number of fitting monitoring stations when not predicting and predicting the troposphere strategy, respectively;

[0037] Figure 5 Schematic comparison diagram of the fitting accuracy of the troposphere under different strategies; and

[0038] Figure 6 Schematic comparison diagram of the troposphere error under different strategies. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] In the following description, the present invention is described with reference to the embodiments. However, those skilled in the art will recognize that the embodiments can be implemented without one or more specific details or in conjunction with other alternative and / or additional methods or components. In other cases, well-known structures or operations are not shown or described in detail so as not to obscure the inventive points of the present invention. Similarly, for purposes of explanation, specific numbers and configurations are set forth in order to provide a thorough understanding of the embodiments of the present invention. However, the present invention is not limited to these specific details.

[0040] In this specification, the reference to "an embodiment" or "the embodiment" means that the specific features, structures, or characteristics described in connection with the embodiment are included in at least one embodiment of the present invention. The phrase "in an embodiment" appearing throughout this specification does not necessarily refer to the same embodiment.

[0041] It should be noted that the embodiments of the present invention describe the method steps in a specific order. However, this is only for the purpose of elaborating the specific embodiment and does not limit the sequence of the steps. On the contrary, in different embodiments of the present invention, the sequence of the steps can be adjusted according to the actual requirements.

[0042] In the present invention, each module of the system according to the present invention can be implemented using software, hardware, firmware, or a combination thereof. When a module is implemented using software, the function of the module can be realized through a computer program flow. For example, the module can be realized through a code segment (such as a code segment in languages such as C, C++) stored in a storage device (such as a hard disk, memory, etc.), where when the code segment is executed by a processor, the corresponding function of the module can be realized. When a module is implemented using hardware, the function of the module can be realized by setting the corresponding hardware structure. For example, the function of the module can be realized by hardware programming of a programmable device such as a field programmable gate array (FPGA), or by designing an application specific integrated circuit (ASIC) including multiple electronic devices such as transistors, resistors, and capacitors. When a module is implemented using firmware, the function of the module can be written in a read-only memory such as an EPROM or EEPROM of the device in the form of program code, and when the program code is executed by a processor, the corresponding function of the module can be realized. In addition, certain functions of the module may need to be realized by separate hardware or in cooperation with the hardware. For example, the detection function is realized through a corresponding sensor (such as a proximity sensor, an acceleration sensor, a gyroscope, etc.), the signal emission function is realized through a corresponding communication device (such as a Bluetooth device, an infrared communication device, a baseband communication device, a Wi-Fi communication device, etc.), the output function is realized through a corresponding output device (such as a display, a speaker, etc.), and so on.

[0043] To solve the problem of insufficient product integrity and accuracy caused by data interruption and gross error contamination in satellite-based augmentation real-time tropospheric services, the present invention provides a satellite-based augmentation real-time tropospheric estimation method and system based on robust quality control and adaptive prediction. On the one hand, when the data stream of the monitoring station is interrupted, a linear prediction model is adopted to maintain the continuity of the tropospheric delay and ensure the integrity rate of the monitoring station data participating in the fitting. On the other hand, a robust DIA quality control method is adopted to eliminate candidate gross errors one by one and refit, and the elimination result with the smallest mean square error of unit weight is selected as the final gross error determination basis, thereby effectively avoiding the contamination of the tropospheric product caused by gross errors and improving the accuracy and robustness of the tropospheric product.

[0044] The technical solution of the present invention will be further described below with reference to the accompanying drawings of the embodiments.

[0045] Figure 1Schematic diagram of the structure of a satellite-based augmentation real-time troposphere estimation system based on robust quality control and adaptive prediction, showing an embodiment of the present invention. As Figure 1 As shown, a satellite-based augmentation real-time troposphere estimation system based on robust quality control and adaptive prediction includes a data acquisition module 101, an adaptive prediction module 102, a quality control module 103, and a data encoding module 104. The data acquisition module 101 is used to obtain data of the monitoring station, where the data includes the real-time observation data stream of the monitoring station, real-time precise orbit clock error products, real-time broadcast ephemeris products, and the latitude, longitude, and elevation of the monitoring station. The data can be used to construct the spatial parameters in the fitting model. The adaptive prediction module 102 is used to predict the tropospheric delay when the data of the monitoring station is interrupted. The quality control module 103 is communicatively connected to the data acquisition module 101 and the adaptive prediction module 102, and is used to eliminate gross errors through the robust DIA quality control method and obtain the final fitting coefficients. The data encoding module 104 is communicatively connected to the quality control module 103 and is used to encode and broadcast the final fitting coefficients.

[0046] To further describe the specific principles and steps of the methods adopted by each module, Figure 2 Schematic diagram of the flow of a satellite-based augmentation real-time troposphere estimation method based on robust quality control and adaptive prediction, showing an embodiment of the present invention. As Figure 2 As shown, a satellite-based augmentation real-time troposphere estimation method based on robust quality control and adaptive prediction includes:

[0047] First, in step 201, data preprocessing. Perform real-time precise positioning and tropospheric data preprocessing. In an embodiment of the present invention, real-time precise positioning is performed based on the data source, where the data source is collected by the data acquisition module 101 and includes the real-time observation data stream of the monitoring station, real-time precise orbit clock error products, real-time broadcast ephemeris products, and the latitude, longitude, and elevation of the monitoring station, etc. In an embodiment of the present invention, the zenith tropospheric wet delay ZWD of the monitoring station is extracted based on the data source through non-differential and non-combination precise positioning r :

[0048]

[0049] Among them,

[0050] are the pseudorange observation values of the monitoring station r and the satellite s at the first and second frequencies respectively;

[0051] are the phase observation values of the monitoring station r and the satellite s at the first and second frequencies respectively;

[0052] is the geometric distance between monitoring station r and satellite s;

[0053] c is the speed of light;

[0054] dt r and dt s are the receiver and satellite clock biases respectively;

[0055] is the tropospheric wet delay projection function between monitoring station r and satellite s;

[0056] ZWD r is the tropospheric wet delay of monitoring station r;

[0057] is the ionospheric delay between monitoring station r at the first frequency and satellite s;

[0058] γ is the conversion coefficient of ionospheric delays at different frequencies,

[0059] and are the phase ambiguities between monitoring station r at the first and second frequencies and satellite s respectively; and

[0060] is the unmodeled residual.

[0061] In an embodiment of the present invention, in order to improve the estimation accuracy and calculation efficiency, it is also necessary to preprocess the data source, including data synchronization, abnormal initial screening, and data storage. Among them, data synchronization means aligning the data of each monitoring station through timestamps to ensure epoch consistency. Abnormal initial screening means removing the values that significantly exceed the physical range, such as the tropospheric wet delay ZWD of the monitoring station greater than 0.5 meters or less than 0.01 meters, etc. Data storage is to store the latest estimated ZWD for prediction. In an embodiment of the invention, if a certain monitoring station lacks the data of the current epoch but there is historical data in the buffer, it is marked as "forecastable state", and if there is no historical data and the continuous lack exceeds the specified duration, such as 5 minutes, etc., it is marked as "invalid station" and does not participate in the subsequent process;

[0062] Next, in step 202, it is judged whether the data stream is interrupted. As mentioned above, in the embodiment of the present invention, when the data stream of the monitoring station is interrupted, the adaptive prediction module 102 can adopt a linear prediction model to maintain the continuity of the tropospheric delay. Based on this, in an embodiment of the present invention, it is also necessary to judge in real time whether the data stream is interrupted. If it is interrupted, it enters step 203 for tropospheric prediction, otherwise, it directly enters step 204 for tropospheric fitting;

[0063] In step 203, tropospheric prediction. When the data stream of the monitoring station is interrupted and the interruption duration is not greater than the preset value, tropospheric prediction is performed. In an embodiment of the present invention, if the data status of the monitoring station is "forecastable status", the most recent valid value of ZWD stored is read T0 , then the interruption duration Δt is determined. If the interruption duration is not greater than the preset value, the best historical data is automatically selected and a linear prediction model is used, that is, directly using ZWD T0 as the predicted value for the current epoch:

[0064] ZWD T0+Δt = ZWD T0 ,

[0065] where T0 is the timestamp of the most recent valid tropospheric delay, and Δt is the interruption duration. If the interruption duration is greater than the preset value, the corresponding monitoring station is marked as "invalid" and removed from the list of fitting monitoring stations. In an embodiment of the present invention, the preset value is 5 minutes. It should be understood that in other embodiments of the present invention, different preset values can be selected according to actual needs;

[0066] In step 204, tropospheric fitting. In an embodiment of the present invention, a low-order polynomial model containing an elevation factor is used for tropospheric fitting:

[0067] ZWD = C0 + C1·dB + C2·dL + C3·H,

[0068] where ZWD is the tropospheric wet delay of the monitoring station, C0, C1, C2, and C3 are tropospheric fitting coefficients, that is, the tropospheric products broadcast by satellite-based augmentation. Among them, C3 reflects the rate of change of tropospheric delay with elevation, dB and dL respectively represent the differences in latitude and longitude between the monitoring station and the fitting center point of the measurement area, and H is the elevation of the monitoring station, and its unit is kilometers. In an embodiment of the present invention, the least squares method is used to perform an initial fit based on the preset model, and the error of tropospheric fitting is calculated according to the following formula: V = AX - L, where V is the a posteriori residual, A is the coefficient matrix, A = [A1; A2; …; A n , where A i = [1 dB i dL i H] represents the coefficient of the i-th monitoring station, n is the number of fitting monitoring stations, X is the tropospheric fitting coefficient, X = [C0; C1; C2; C3], and L is the tropospheric delay of the monitoring station. Then, the adjustment result is calculated by least squares adjustment: Finally, the a posteriori residual and the mean square error of unit weight

[0069] Next, in step 205, it is determined whether the mean error of unit weight is greater than a specified value. As described above, in an embodiment of the present invention, the robust DIA quality control method is implemented through the quality control module 103 to obtain the final fitting coefficients. When the mean error of unit weight is greater than the specified value, step 206 is entered for gross error detection and rejection. Otherwise, step 207 is directly entered for fitting coefficient encoding and broadcasting. In an embodiment of the present invention, the specified value is where k is a constant, and its value can be, for example, from 1 to 1.5, preferably 1, is the empirical value of the mean error of unit weight, and its value can be, for example, 1 centimeter;

[0070] In step 206, gross error detection and rejection. When the mean error of unit weight is greater than the specified value, gross error detection is performed. In an embodiment of the present invention, the data of the i-th monitoring station is sequentially rejected, and re-fitting is performed to calculate the new mean error of unit weight The monitoring station that makes the mean error of unit weight the smallest is regarded as a gross error, that is, Cyclic inspection is performed, and gross errors are repeatedly rejected and re-fitted until the mean error of unit weight is not greater than the specified value. Thus, the final fitting coefficients C0, C1, C2, and C3 can be obtained; and

[0071] Finally, in step 207, fitting coefficient encoding and broadcasting. The final fitting coefficients are encoded by the data encoding module 104 and broadcast. In an embodiment of the present invention, the final fitting coefficients are converted into a binary format, encoded according to the satellite-based augmentation protocol, and a timestamp, a region ID, and a data validity flag are appended. In an embodiment of the present invention, it is broadcast in real time through the L-band satellite, and the broadcast frequency is 0.1 Hz. After the user terminal receives it, combined with its own position (dB 用户 、dL 用户 、H 用户 ) to calculate the local ZWD 用户 :

[0072] ZWD 用户 = C0 + C1·dB 用户 + C2·dL 用户 + C3·H 用户 .

[0073] To verify the performance of the satellite-based augmentation real-time troposphere estimation method according to the embodiments of the present invention, regional monitoring stations are selected for verification, and the distribution of the monitoring stations is as Figure 3As shown, nine monitoring stations are used as tropospheric service monitoring stations, and tropospheric fitting coefficient products are obtained through polynomial fitting. STA0 in the figure is a verification station and does not participate in tropospheric fitting. The observation data is from October 16, 2024, with a sampling rate of 10 s. The specific tropospheric estimation strategy is shown in Table 1. The tropospheric forecast duration threshold is set to 5 min when data is interrupted. An error of 5 cm is added to the accurately estimated tropospheric delay at the STA8 monitoring station to simulate the gross difference of the tropospheric delay estimated at this monitoring station. Then, the following three strategies are respectively used for tropospheric estimation: Strategy 1, without quality control; Strategy 2, traditional maximum a posteriori residual DIA quality control; and Strategy 3, the satellite-based augmentation real-time tropospheric estimation method as described above.

[0074]

[0075] Table 1

[0076] Figure 4a and 4b respectively show schematic diagrams of the number of fitting monitoring stations when the tropospheric strategy is not forecasted and forecasted. As Figure 4a shown, when the real-time data stream of the station is interrupted or unstable for a short time, it will lead to a reduction in the number of stations participating in tropospheric fitting, and the fluctuation of the number of fitting stations is also relatively large. The data integrity rate of the tropospheric delay fitting stations is 99.08%. As Figure 4b shown, after adopting the tropospheric forecast strategy, the number of stations participating in tropospheric fitting is relatively stable. Only a few epochs result in a reduction in the number of fitting stations due to long-term interruption of the data stream. The data integrity rate of the tropospheric delay fitting stations is 99.87%, which is about 0.79% higher than that without forecasting. Therefore, adopting the tropospheric adaptive forecast method as described above can reduce the fluctuation of the fitting monitoring station data caused by the interruption of the real-time data stream and improve the data integrity rate of the tropospheric delay fitting stations.

[0077] Figure 5 shows a schematic diagram of the comparison of the fitting accuracy of the troposphere under different strategies, where the fitting accuracy is represented by the mean square error of unit weight, which can evaluate the quality of the fitting data to a certain extent. As Figure 5As shown, since Strategy 1 does not perform fitting quality control and is adversely affected by gross errors, the fitting accuracy is poor, and the overall RMS is 0.014 m. Strategy 2 identifies gross errors through the maximum a posteriori residual. Because it misjudges the gross error value, it eliminates the fitting value with the largest residual, and the fitting accuracy is slightly improved. The overall RMS is 0.008 m. Strategy 3 accurately identifies and eliminates gross errors through the minimum mean square error of unit weight. The fitting accuracy is significantly improved. The overall RMS is 0.005 m, which is 64.3% higher than Strategy 1 and 37.5% higher than Strategy 2. Although the fitting accuracy of Strategy 2 is somewhat improved compared to Strategy 1, because it misjudges the gross error value, the true accuracy of the product has not been improved, which will be analyzed in detail below.

[0078] Figure 6 Figure showing the comparison schematic diagram of tropospheric errors under different strategies. The tropospheric error is the comparison between the tropospheric model value calculated by combining the longitude and latitude coordinates of the verification station with the tropospheric fitting coefficient and the accurate tropospheric value estimated by precise positioning. As Figure 6 shown, affected by the gross error observations, the accuracy of the tropospheric product of Strategy 1 is poor. Therefore, the tropospheric error of the verification station is large, and the overall RMS is 0.015 m. Because Strategy 2 misjudges and eliminates clean fitting values, the true gross error will still affect the accuracy of the tropospheric product. Therefore, the tropospheric error of the verification station is larger than that of Strategy 1, and the overall RMS is 0.019 m. Strategy 2 only accurately identifies and eliminates gross errors in a few epochs, and the misjudgment rate is as high as 85%. Strategy 3 accurately identifies and eliminates the gross error troposphere, ensuring the accuracy of the tropospheric product. The tropospheric error of the verification station is the smallest, and the overall RMS is 0.005 m, which is 66.7% higher than Strategy 1 and 73.7% higher than Strategy 2. Strategy 3 only misjudges the gross error troposphere in a few epochs, and the misjudgment rate is only 1%, which is significantly lower than that of Strategy 2.

[0079] In summary, the space-based augmentation real-time tropospheric estimation method provided by the present invention can avoid the situation of fluctuations in the number of tropospheric fitting stations caused by short-term interruptions in real-time data streams, and improve the integrity rate of tropospheric delay fitting station data. It adopts a more rigorous tropospheric fitting quality control method, which can accurately identify and eliminate tropospheric gross error values, ensuring the accuracy and robustness of the tropospheric product.

[0080] Based on the space-based augmentation real-time tropospheric estimation method described above, the present invention also provides an electronic device for estimating real-time troposphere, which includes a memory and a processor, wherein the memory is configured to store a computer program, and the computer program executes the space-based augmentation real-time tropospheric estimation method described above when running on the processor.

[0081] The present invention further provides a computer-readable storage medium for estimating the real-time troposphere, characterized in that a computer program is stored, and when the computer program runs on a processor, it executes the satellite-based enhanced real-time troposphere estimation method as described above.

[0082] Although the embodiments of the present invention have been described above, it should be understood that they are presented only as examples and not as limitations. It will be apparent to those skilled in the relevant art that various combinations, variations, and changes can be made thereto without departing from the spirit and scope of the present invention. Therefore, the breadth and scope of the present invention disclosed herein should not be limited by the exemplary embodiments disclosed above, but should be defined only by the appended claims and their equivalents.

Claims

1. A satellite-based augmentation real-time troposphere estimation method based on robust quality control and adaptive prediction, characterized in that Including: Performing initial fitting based on a preset model to obtain the a posteriori residuals and the mean error of unit weight; If the mean error of unit weight is greater than a specified value, performing gross error detection, removing the gross errors, and refitting; And Encoding the final fitting coefficients and broadcasting them.

2. The satellite-based augmentation real-time troposphere estimation method according to claim 1, wherein The preset model is a low-order polynomial model containing an elevation factor: ZWD = C0 + C1·dB + C2·dL + C3·H, where ZWD is the tropospheric wet delay of the monitoring station, C0, C1, C2, and C3 are tropospheric fitting coefficients, dB and dL respectively represent the differences in latitude and longitude between the monitoring station and the fitting center point of the survey area, and H is the elevation of the monitoring station, with the unit of kilometer.

3. The satellite-based augmentation real-time troposphere estimation method according to claim 2, wherein The tropospheric wet delay is obtained through undifferenced and non-combined estimation: where They are the pseudorange observations of the first and second frequency monitoring stations r and the satellite s respectively; They are the phase observation values of the first and second frequency monitoring stations r and the satellite s respectively; is the geometric distance between the monitoring station r and the satellite s; c is the speed of light; dt r and dt s are the receiver and satellite clock errors respectively; is the tropospheric wet delay projection function of monitoring station r and satellite s; ZWD r is the tropospheric wet delay of monitoring station r; is the ionospheric delay of the first frequency monitoring station r and the satellite s; γ is the conversion coefficient of ionospheric delays at different frequencies, and are the phase ambiguities of the first and second frequency measurement stations r and the satellite s, respectively; and is the unmodeled residual.

4. The satellite-based augmentation real-time troposphere estimation method according to claim 2, wherein If the data stream of the monitoring station is interrupted and the interruption duration is not greater than a preset duration, the most recent valid tropospheric delay is used as the predicted value for the current epoch: ZWD T0+Δt = ZWD T0 , where T0 is the timestamp of the most recent valid tropospheric delay, and Δt is the interruption duration; and If the interruption duration is greater than the preset duration, the corresponding monitoring station is removed from the fitting monitoring station list, where the preset duration is 5 minutes.

5. The satellite-based augmentation real-time troposphere estimation method according to claim 1, wherein The satellite-based augmentation real-time tropospheric estimation method further includes: Before performing initial fitting, aligning the data of each monitoring station by timestamp and removing the values outside the preset range.

6. The satellite-based augmentation real-time troposphere estimation method according to claim 1, characterized in that, Performing initial fitting based on the preset model using the least squares method, including: Calculating the error of tropospheric fitting according to the following formula: V = AX - L, Among them, V is the posterior residual, A is the coefficient matrix, A = [A1; A2; …; A n , where A i = [1 dB i dL i H] represents the coefficient of the i-th monitoring station, and n is the number of fitting monitoring stations. where X is the tropospheric fitting coefficient, X = [C0; C1; C2; C3], and L is the tropospheric delay of the monitoring station; Calculating the adjustment result through least squares adjustment: and Calculate the a posteriori residuals and the mean error of unit weight 7. The satellite-based augmentation real-time troposphere estimation method according to claim 1, characterized in that Removing gross errors includes: Eliminate the data of the i-th measuring station in sequence, re-fit and calculate the new mean square error of unit weight Regarding the monitoring station that makes the mean error of unit weight the smallest as a gross error; and Repeating the removal of gross errors and refitting until the mean error of unit weight is not greater than the specified value.

8. The satellite-based augmentation real-time troposphere estimation method according to claim 1, wherein Encoding the fitting coefficients includes: Converting the fitting coefficients into binary format, encoding them according to the satellite-based augmentation protocol, and attaching a timestamp, a region ID, and a data validity flag.

9. An electronic device for estimating the real-time troposphere, characterized in that, Including a memory and a processor, where the memory is configured to store a computer program, and the computer program executes the satellite-based augmentation real-time tropospheric estimation method as described in any one of claims 1 to 8 when running on the processor.

10. A computer-readable storage medium for estimating the real-time troposphere, characterized in that, A computer program is stored, and the computer program executes the satellite-based augmentation real-time tropospheric estimation method as described in any one of claims 1 to 8 when running on a processor.

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