Satellite data processing method and device, computer storage medium and terminal

Abstract

According to the satellite data processing method and device, the computer storage medium and the terminal, the ionosphere estimation value and the troposphere estimation value over the reference station are extracted, the ionosphere estimation value and the troposphere estimation value over the user side are combined, expansion of a coverage area of high-precision service is achieved, and the user experience is improved. The positioning performance of the ionosphere during the active period is improved, and technical support is provided for realizing cross-source resource sharing (CORS), improving service performance and reducing cost.

Description

Technical Field

[0001] The present invention relates to satellite navigation technology, and more particularly to a method, device, computer storage medium and terminal for processing satellite data. Background Art

[0002] The Global Navigation Satellite System (GNSS) can provide global users with three-dimensional, all-weather, high-quality positioning, navigation and timing (PNT) services, and is widely used in transportation, navigation, public safety and military fields. However, the GNSS navigation service can only obtain meter-level positioning accuracy. In order to improve positioning accuracy and availability, many countries have developed navigation augmentation systems, such as the Wide Area Augmentation System (WAAS), the European Ground-Based Augmentation System (EGNOS), the Beidou Satellite Navigation System Augmentation Service (BDSBAS) and the Galileo High Accuracy Service (Galileo HAS). High-precision applications such as geodetic surveying, deformation monitoring, and UAV mapping in related technologies usually use single-station real-time dynamic carrier phase difference technology (RTK), small area network RTK (NRTK), and precise point positioning (PPP) services; however, they cannot meet the needs of smart driving, precision agriculture, consumer drones, and shared bicycles for high-precision centimeter-level services anytime and anywhere. In recent years, domestic and foreign commercial companies have begun to provide services such as NRTK and PPP-RTK based on large-scale GNSS tracking station networks.

[0003] The cost of building a large-scale GNSS tracking station network is high. At present, some domestic commercial companies have built thousands of large-scale GNSS tracking station networks with high operation and maintenance costs, but they still cannot meet customers' demand for high-precision services anytime and anywhere: for smart driving services, it is necessary to cover the main road network; in low-latitude areas, in order to deal with the problem of active ionospheric, it is necessary to encrypt stations; for customers with integrated domestic and overseas services, it is also necessary to build stations in overseas areas. In order to meet the above needs, it is necessary to integrate the data resources of various users, such as: mobile user data such as smart driving vehicles, high-precision positioning mobile phones, and drones; fixed user data such as deformation monitoring equipment and 5G base station timing equipment. How to provide high-precision and highly reliable commercial services is a key technology that needs to be broken through.

[0004] A small number of studies in related technologies have focused on the above issues. Among them, the more representative literature is as follows: Shi Chuang proposed a fog positioning architecture that can process multi-source crowdsourced data in the cloud, but it is only a conceptual architecture without specific applications and verification of actual effects. Minson discussed earthquake early warning technology based on crowdsourced data; Zhilu Wu studied the inversion of water vapor over the ocean based on shipborne GNSS dynamic data, but these studies only post-processed user-side data offline and presented some academic results, which has little to do with high-precision centimeter-level services such as NRTK and PPP-RTK. Roberto Capua first proposed a PPP-RTK service mode in a collaborative positioning mode. This study made a preliminary attempt to perform PPP-RTK based on user-side data, without considering the fusion processing of reference station and user-side data. Hongjin Xu proposed an atmospheric modeling method based on vehicle-mounted crowdsourced GNSS data, but did not consider the fusion processing of reference station and user-side data, and did not consider the modeling credibility and precision factor issues. Xingxing Li studied the uncertainty information of atmospheric modeling, but this information partially relies on empirical information and cannot achieve real-time external compliance precision factor solution. The defects of crowdsourcing GNSS data processing technology in related technologies are as follows: first, there is no overall architecture that provides high-precision commercial operation services; second, for the various user-end data sources, it does not fully consider reliable quality control strategies to filter out poor quality data information; third, it does not provide accurate atmospheric accuracy information. Summary of the invention

[0005] The present application provides a method for processing satellite data, including: Based on the satellite-based augmentation service (SSR) correction number, the precise single point positioning ambiguity fixed PPP-AR positioning estimation is performed on the reference station to extract the ionosphere estimation value and troposphere estimation value above the reference station. Based on the extracted ionosphere estimation value and troposphere estimation value above the reference station, first regional atmosphere modeling and first quality factor solving are performed to determine the first regional atmosphere model and the first quality factor; The second regional atmospheric model and the second quality factor are iteratively solved based on the following process: Based on the SSR correction number, the determined first regional atmospheric model and the first quality factor, the PPP-RTK positioning of the user terminal is estimated to extract the ionosphere estimation value and the troposphere estimation value above the user terminal; Filter the extracted ionosphere estimation value and troposphere estimation value above the user end; The second regional atmospheric modeling and the second quality factor are determined based on the extracted ionosphere estimation value and troposphere estimation value above the reference station and the screened ionosphere estimation value and troposphere estimation value above the user terminal.

[0006] On the other hand, an embodiment of the present application further provides a computer storage medium, wherein the computer storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned satellite data processing method is implemented.

[0007] On the other hand, an embodiment of the present application further provides a terminal, comprising: a memory and a processor, wherein the memory stores a computer program; wherein: The processor is configured to execute the computer program in the memory; When the computer program is executed by the processor, the above-mentioned satellite data processing method is implemented.

[0008] In another aspect, an embodiment of the present application further provides a device for processing satellite data, comprising: a reference station information extraction unit, a first processing unit, and a second processing unit; wherein: The reference value information extraction unit is set as follows: based on the satellite-based augmentation service SSR correction number, the precise single point positioning ambiguity fixed PPP-AR positioning estimation is performed on the reference station to extract the ionosphere estimation value and the troposphere estimation value above the reference station; The first processing unit is configured to: perform first regional atmospheric modeling and first quality factor solution based on the extracted ionosphere estimation value and troposphere estimation value above the reference station to determine the first regional atmospheric model and the first quality factor; The second processing unit is configured to perform iterative solution of the second regional atmospheric model and the second quality factor based on the following processing: Based on the SSR correction number, the determined first regional atmospheric model and the first quality factor, the PPP-RTK positioning of the user terminal is estimated to extract the ionosphere estimation value and the troposphere estimation value above the user terminal; Filter the extracted ionosphere estimation value and troposphere estimation value above the user end; The second regional atmospheric modeling and the second quality factor are determined based on the extracted ionosphere estimation value and troposphere estimation value above the reference station and the screened ionosphere estimation value and troposphere estimation value above the user terminal.

[0009] The disclosed embodiment extracts the ionosphere estimation value and the troposphere estimation value above the reference station, and by combining the ionosphere estimation value and the troposphere estimation value above the user terminal, achieves the expansion of the coverage area of ​​high-precision services, improves the positioning performance during the active ionosphere period, and provides technical support for realizing massive cross-source resource sharing (CORS), improving service performance and reducing costs.

[0010] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by implementing the present application. Other advantages of the present application can be realized and obtained by the schemes described in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings are used to provide an understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0012] Figure 1 A flowchart of a method for processing satellite data according to an embodiment of the present disclosure; Figure 2 This is a schematic diagram of PPP-RTK modeling in an embodiment of the present disclosure; Figure 3 The structure block diagram of the device for satellite data processing according to the embodiment of the present disclosure is shown in FIG. DETAILED DESCRIPTION

[0013] The present application describes multiple embodiments, but the description is exemplary rather than restrictive, and it is obvious to those skilled in the art that there may be more embodiments and implementations within the scope of the embodiments described in the present application. Although many possible feature combinations are shown in the drawings and discussed in the specific embodiments, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with any other feature or element in any other embodiment, or may replace any other feature or element in any other embodiment.

[0014] The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features and elements disclosed in the present application may also be combined with any conventional features or elements to form a unique invention scheme. Any features or elements of any embodiment may also be combined with features or elements from other invention schemes to form another unique invention scheme. Therefore, it should be understood that any feature shown and / or discussed in the present application may be realized individually or in any appropriate combination. Therefore, except for the limitations made according to the attached claims and their equivalents, the embodiments are not subject to other restrictions. In addition, various modifications and changes may be made within the scope of protection of the attached claims.

[0015] In addition, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not rely on the specific order of the steps described herein, the method or process should not be limited to the steps of the specific order described. As will be understood by those of ordinary skill in the art, other sequences of steps are also possible. Therefore, the specific sequence of the steps set forth in the specification should not be interpreted as a limitation to the claims. In addition, the claims for the method and / or process should not be limited to the steps of performing them in the order written, and those skilled in the art can easily understand that these sequences can be changed and still remain within the spirit and scope of the embodiments of the present application.

[0016] The disclosed embodiment proposes a cloud-side and core-side collaborative integrated PPP-RTK atmospheric enhancement modeling and high-reliability modeling method, which can make full use of various user data resources, integrate the ionosphere and troposphere estimates above the reference station and the user end, perform regional atmospheric modeling, and iteratively solve the quality factor to meet the user's high-precision and high-reliability positioning needs, which is of great significance for expanding the coverage area of ​​high-precision services, improving positioning performance during the active ionosphere, and reducing costs.

[0017] Figure 1 is a flow chart of a method for processing satellite data according to an embodiment of the present disclosure, as shown in FIG. Figure 1 As shown, including: Step 101: perform precise point positioning ambiguity fixation (PPP-AR) positioning estimation on the reference station based on satellite-based augmentation service (SSR) corrections, and extract ionosphere estimation values ​​and troposphere estimation values ​​above the reference station; Step 102: Based on the extracted ionosphere estimation value and troposphere estimation value above the reference station, perform first regional atmospheric modeling and solve the first quality factor to obtain the first regional atmospheric model and determine the first quality factor; The second regional atmospheric model is established and the second quality factor is solved iteratively based on the following processing: Step 103: Based on the SSR correction number, the obtained first regional atmospheric model and the determined first quality factor, PPP-RTK positioning estimation is performed on the user terminal to extract the ionosphere estimation value and the troposphere estimation value above the user terminal; Step 104: screening the extracted ionosphere estimation value and troposphere estimation value above the user terminal; Step 105: Perform second regional atmospheric modeling and solve a second quality factor based on the extracted ionosphere estimation value and troposphere estimation value above the reference station and the screened ionosphere estimation value and troposphere estimation value above the user terminal.

[0018] It should be noted that steps 103 to 105 of the embodiment of the present disclosure are an iterative solution process, and the second regional atmosphere modeling and the second quality factor are solved by repeating the above steps 103 to 105.

[0019] The disclosed embodiment extracts the ionosphere estimation value and the troposphere estimation value above the reference station, and by combining the ionosphere estimation value and the troposphere estimation value above the user terminal, achieves the expansion of the coverage area of ​​high-precision services, improves the positioning performance during the active ionosphere period, and provides technical support for realizing giant CORS, improving service performance and reducing costs.

[0020] In an exemplary embodiment, the embodiment of the present disclosure may perform the processing of steps 101 to 103 based on the PPP-AR algorithm.

[0021] Referring to the related art, the original GNSS observation equation of the embodiment of the present disclosure can be expressed as: (1) (2) in, It is a tracking station Tracking Satellites Frequency The carrier observation value, It is a tracking station Tracking Satellites Frequency The pseudo-range observation value of It is a tracking station To Satellite The geometric distance is the ionospheric delay at frequency 1 in the line of sight direction, and is the signal frequency and wavelength, is the tropospheric delay in the line of sight, is the receiver clock error, is the satellite clock error, The receiver is at the frequency Carrier hardware delay on, The satellite is at the frequency Carrier hardware delay on The receiver is at the frequency The pseudorange hardware delay on The satellite is at the frequency Pseudorange hardware delay on It is the frequency The fuzziness, is the carrier observation noise, is the pseudorange observation noise.

[0022] Referring to the relevant technology, the precise point positioning PPP estimation model based on the SSR correction number in the embodiment of the present disclosure is: (3) in, It is a tracking station Tracking Satellites The OMC (Observed-minus-computed, a method used to evaluate the positioning accuracy of satellite navigation systems) value of the pseudorange observation value of frequency point 1, It is a tracking station Tracking Satellites OMC value of pseudorange observation value at frequency point 2, It is a tracking station Tracking Satellites OMC value of pseudorange observation value at frequency point k, It is a tracking station Tracking Satellites OMC value of the carrier observation value at frequency point 1, It is a tracking station Tracking Satellites OMC value of the carrier observation value at frequency point 2, It is a tracking station Tracking Satellites OMC value of the carrier observation value at frequency point k, Indicates the signal frequency of frequency point 1, represents the signal frequency at frequency point k, It is a receiver To Satellite The unit vector of is the receiver position correction, is the receiver clock error that absorbs the receiver pseudorange hardware delay, c represents the beam, is the satellite clock error that absorbs the satellite pseudorange hardware delay, is the ionospheric delay at frequency 1 that absorbs the hardware delay between the receiver and the satellite pseudorange. It absorbs the ambiguity of pseudorange and carrier hardware delay. is the pseudorange frequency deviation, is the satellite clock frequency deviation , , , , , The present disclosure embodiment can correct the precise satellite orbit correction number broadcast by the SSR service. , , , , and ; Obtained from the precise satellite clock correction ; Provided by SSR service ; Embodiments of the present disclosure In the SSR service, it is a pseudo-range clock, which absorbs the satellite pseudo-range hardware delay. Absorbs pseudorange and carrier hardware delays.

[0023] In an exemplary embodiment, step 101 of the embodiment of the present disclosure performs PPP-AR positioning estimation on the reference station based on the SSR correction number, and extracts the ionosphere estimation value and the troposphere estimation value above the reference station, including: Step 1010: The PPP estimation model based on the SSR correction number estimates the floating point ambiguity of each frequency point. , and convert the floating point ambiguity of each frequency point into the ultra-wide lane floating point ambiguity and wide lane floating point ambiguity ; The disclosed embodiment converts the floating point ambiguity of each frequency point into an ultra-wide lane floating point ambiguity , Wide Lane Floating Point Ambiguity The expression is: (4) Using the Ultra-Wide Lane Uncalibrated Phase Bias (UPD), Wide Lane UPD, and Narrow Lane UPD products provided by SSR services: , , , and eliminate the hardware delay at the receiver end through inter-satellite single difference: , , , which can be achieved , , The ambiguity is fixed and the integer ambiguity is obtained: , , And the integer ambiguity: , .

[0024] Step 1011: Use the ultra-wide lane UPD provided by the SSR service ( )、Wide Lane UPD( )、Narrow Alley UPD( ) products and eliminate the ultra-wide lane hardware delay at the receiver end through inter-satellite single difference , Wide Lane Hardware Delay , Narrow Lane Hardware Delay , achieving ultra-wide lane floating point ambiguity , Wide Lane Floating Point Ambiguity , and the ambiguity of the hardware delay between the pseudorange and the first frequency of the carrier The ambiguity is fixed and the whole cycle ambiguity information is obtained; In an exemplary embodiment, the integer ambiguity information of the embodiment of the present disclosure includes: Ultra-wide lane blur , wide lane ambiguity , the integer ambiguity of the first frequency point and the integer ambiguity of the second frequency point , Frequency The integer ambiguity .

[0025] Step 1013: bring the obtained integer ambiguity information into the PPP estimation model based on the SSR correction number to obtain the ionosphere estimation value and the troposphere estimation value above the reference station; The ionospheric and tropospheric estimates above the reference station include: ionospheric delay after ambiguity fixation , the corresponding floating point ionospheric delay is , the estimated value of the zenith tropospheric wet term after the ambiguity is fixed The corresponding floating point zenith tropospheric wet term estimate is .because and The accuracy is similar, and the subsequent description of the embodiments of this disclosure does not distinguish between them. replace and .

[0026] In an exemplary embodiment, the embodiment of the present disclosure determines a first regional atmospheric model, including: Based on the ionosphere estimation value and the troposphere estimation value above the reference station, the corresponding ionosphere model and troposphere model are determined; the embodiment of the present disclosure can be based on or , Conduct first regional atmospheric modeling.

[0027] The ionosphere model of the embodiment of the present disclosure is generated by combining a polynomial model with a grid point model, and the troposphere model is generated by a grid point model; wherein the ionosphere model adopts a second-order polynomial model, and the expression is: (5) in, Indicates the reference station, , ,, , and represents the polynomial model coefficients, is the location of the center point of the modeling partition, is the reference station location.

[0028] In an exemplary embodiment, the ionospheric residual value at the grid point of the embodiment of the present disclosure is processed using an inverse distance weighting strategy (IDW, Inverse Distance Weighting), and the expression used is: (6) in, represents the reference station used for interpolation of grid point residual values, It represents the ionospheric residual value at the reference station after deducting the polynomial model value; For the Zenith Troposphere Delay (ZTD), the dry part is corrected by the empirical model, and the wet part is corrected by the grid point model. The expression is: (7) is the reference station used for interpolation of grid point residual values, is the estimate of the zenithal tropospheric wet term at the reference station.

[0029] In an exemplary embodiment, the embodiment of the present disclosure performs PPP-RTK positioning estimation on the user terminal based on the SSR correction number, the obtained first regional atmospheric model and the determined first quality factor, including: determining an atmospheric constraint based on a first regional atmospheric model and a first quality factor; Add atmospheric constraints to the PPP estimation model based on SSR corrections; Based on the PPP estimation model based on SSR correction numbers by adding atmospheric constraints, PPP-RTK positioning estimation is performed on the user side.

[0030] Figure 2 Schematic diagram of PPP-RTK modeling in the embodiment of the present disclosure, as shown in Figure 2 As shown, the outermost dotted line determines the coverage area of ​​the modeling partition, each circled point represents a grid point, the first grid point in the lower left corner refers to the first grid point in the relevant technology, the dotted area in the lower left corner is the effective area of ​​a single grid point, and the central black square represents the center point of the modeling partition.

[0031] After the ambiguity is fixed during the positioning process performed in step 103, the ionosphere estimation value above the user terminal can be extracted. , tropospheric estimates , and the corresponding integer ambiguity , , , user upload , , , , ,The user end can also directly upload the original observation data and perform positioning and solution in the cloud.

[0032] In an exemplary embodiment, the first regional atmospheric model of the embodiment of the present disclosure includes a first ionosphere model and a first troposphere model, the first quality factor includes a first ionosphere quality factor and a first troposphere quality factor, the atmospheric constraint includes a troposphere constraint and an ionosphere constraint, and the expression of the atmospheric constraint is: (8) In the formula, represents the ionospheric constraint, represents the ionosphere estimate, represents the value of the first ionospheric model above tracking station i, represents the ionospheric polynomial coefficient model value, represents the ionospheric grid point value, represents the first ionospheric quality factor, represents the tropospheric constraint, represents the tropospheric estimate, represents the value of the first tropospheric model above tracking station i, represents the zenith tropospheric dry part, represents the zenith tropospheric wet term, Represents the first tropospheric quality factor.

[0033] In an exemplary embodiment, the embodiment of the present disclosure filters the extracted ionosphere estimation value and troposphere estimation value above the user terminal, including: The ionosphere estimation values ​​and troposphere estimation values ​​above the user end are screened through the cross comparison method and closed loop detection method.

[0034] Since the user-side receiver, antenna, and surrounding environment are all uncontrollable, it is necessary to perform quality control on the ionosphere and troposphere estimates uploaded by the user to ensure that only high-quality user-side atmospheric information is involved in atmospheric modeling. The disclosed embodiment screens out correct and reliable ionosphere and troposphere estimates above the user side through quality control in step 104, thereby improving the credibility of modeling.

[0035] The embodiment of the present disclosure uses a cross comparison method and a closed loop detection method to screen the ionosphere estimation value and the troposphere estimation value above the user terminal; referring to formula (9) in the related art, the cross comparison method in the embodiment of the present disclosure, since the difference in ionosphere values ​​above a certain distance station is not large, the ionosphere gross error value detection is performed by comparing the ionosphere values ​​above each reference station; In range Outside the =3, can be set according to different ionospheric activity conditions), is the number of residual stations involved in modeling, is the number of users involved in modeling: (9) Referring to formula (10) in the related art, in the closed loop detection method in the embodiment of the present disclosure, the user and the two reference stations can form a triangulation network, and use the double difference ambiguity closure condition to detect the correctness of the user-side PPP-RTK positioning ambiguity fixation, thereby ensuring the correctness of the extracted atmospheric information: (10) In the formula, represents the double difference ambiguity between reference station i and user end u, represents the double difference ambiguity between user terminal u and reference station j, represents the double difference ambiguity between reference station j and reference station i, represents the single difference ambiguity between satellite s and satellite r at reference station i, represents the single difference ambiguity between satellite s and satellite r on the user end u, represents the undifferenced ambiguity of satellite s at reference station i, represents the undifferenced ambiguity of satellite r at reference station i.

[0036] Passed the test , , can participate in the atmospheric modeling of the second region, and update formulas (5), (6) and (7) as follows: (11) (12) (13) in, and are the number of reference stations and the number of user terminals used for ionospheric grid point interpolation, and are the number of reference stations and the number of user terminals used for tropospheric grid point interpolation, represents the ionospheric residual value at the reference station after deducting the polynomial model value, Represents the ionospheric residual value after deducting the polynomial model value on the user side, represents the estimated value of the zenith tropospheric wet term at the reference station, Represents the estimated value of the zenithal tropospheric wet term at the user end.

[0037] In the embodiment of the present disclosure, the ionosphere estimation value and the troposphere estimation value above the user terminal are screened in step 104, thereby improving the user positioning accuracy and convergence time. However, the accuracy of the atmospheric product accuracy information has a great influence on the convergence time, that is, , .

[0038] In the embodiment of the present disclosure, the cloud and the core are integrated with atmospheric modeling. In step 102, the atmospheric modeling of the first area uses only sparse reference stations. With the addition of user terminals, the atmospheric modeling accuracy of the second area is gradually improved. At the same time, the modeling accuracy changes frequently. In this process, it is necessary to provide a high-accuracy quality factor to further improve the user positioning performance.

[0039] In an exemplary embodiment, the first regional atmospheric model of the embodiment of the present disclosure includes a first ionosphere model and a first troposphere model, the first ionosphere model is generated by combining a polynomial model with a grid point model, and the first troposphere model is generated by a grid point model, and solving the first quality factor includes: Near the grid point, for example, within 120 kilometers, when the sum of the number of reference stations and user terminals used for the first regional atmospheric modeling is less than 3, the first quality factor is generated by the following processing for the partition of the entire first regional atmospheric modeling of each satellite: Calculate the difference between the atmospheric estimate and the value from the atmospheric model for the first region: (14) (15) The atmospheric estimates include the following atmospheric estimates of the reference station: Ionosphere estimates above the reference station , tropospheric estimates , is the number of ionospheric grid points used for interpolation above the reference station, is the number of tropospheric grid points used for interpolation above the reference station, represents the reference station used for interpolation of grid point residual values, represents the difference between the ionospheric estimate and the value of the first ionospheric model, represents the ionosphere estimate, represents the ionospheric polynomial coefficient model value, represents the interpolated distance to the grid points, Represents the ionospheric grid point value, represents the difference between the estimated value of the tropospheric wet term and the value of the first tropospheric model, represents the estimated value of the tropospheric wet part, represents the grid point value of the tropospheric wet term, Represents the reference station used for interpolation of grid point residual values; Obtained by the difference between the atmospheric estimate and the value of the first regional atmospheric model and The percentile of is used as the first quality factor for the entire first-region atmospheric modeling partition: (16) in, represents the first ionospheric quality factor, represents the first tropospheric quality factor; When the number of reference stations used for modeling near a grid point is greater than or equal to 3, the corresponding first quality factor is generated for each grid point of each satellite: (17) In the formula, is the number of reference stations involved in modeling near the grid point, is the number of reference stations near the grid point that participate in modeling.

[0040] In an exemplary embodiment, the second regional atmospheric model of the embodiment of the present disclosure includes a second ionosphere model and a second troposphere model, the second ionosphere model is composed of a polynomial model and a grid point model, the second troposphere model is composed of a grid point model, and solving the second quality factor includes: When the sum of the number of reference stations and user terminals used for the second regional atmospheric modeling near the grid point is less than 3, the second quality factor is generated by the following processing for the partition of the entire second regional atmospheric modeling of each satellite: The second quality factor is generated for each satellite partition of the entire second region atmosphere model by the following process: Compute the difference between the atmospheric estimate and the value from the atmospheric model for the second region: (18) (19) The atmospheric estimation values ​​include the following atmospheric estimation values ​​of the reference station or the user end: Ionosphere estimation value above the reference station , tropospheric estimates , the estimated ionosphere above the user , tropospheric estimates , is the number of ionospheric grid points used for interpolation above the reference station or user terminal, is the number of tropospheric grid points used for interpolation above the reference station or user terminal, Represents the reference station or user end used for interpolation of grid point residual values. represents the difference between the ionospheric estimate and the value of the second ionospheric model, represents the ionosphere estimate, represents the ionospheric polynomial coefficient model value, represents the interpolated distance to the grid points, Represents the ionospheric grid point value, represents the difference between the estimated value of the tropospheric wet term and the value of the second tropospheric model, represents the estimated value of the tropospheric wet part, represents the grid point value of the tropospheric wet term, Represents the reference station or user end used for interpolation of grid point residual values; Obtained by the difference between the atmospheric estimate and the value of the second regional atmospheric model and The percentile of is used as the second quality factor for the entire second regional atmospheric modeling partition: (20) in, represents the second ionospheric quality factor, represents the second ionospheric quality factor; When the number of reference stations and user terminals used for modeling near a grid point is greater than or equal to 3, a corresponding second quality factor is generated for each grid point of each satellite: (twenty one) In the formula, is the sum of the number of reference stations and user terminals involved in ionospheric modeling near the grid point, It is the sum of the number of reference stations and user terminals participating in tropospheric modeling near the grid point.

[0041] Based on the above first quality factor and second quality factor, the user end of the disclosed embodiment can timely and fully utilize the accuracy of the atmospheric modeling of the second region to gradually improve the service effect, and realize highly reliable atmospheric enhancement modeling with cloud-side integration and multi-user collaboration.

[0042] The embodiment of the present disclosure further provides a computer storage medium, in which a computer program is stored. When the computer program is executed by a processor, the above-mentioned satellite data processing method is implemented.

[0043] The embodiment of the present disclosure further provides a terminal, comprising: a memory and a processor, wherein a computer program is stored in the memory; The processor is configured to execute the computer program in the memory; When the computer program is executed by a processor, the above-mentioned satellite data processing method is implemented.

[0044] Figure 3 FIG. 1 is a structural block diagram of a device for processing satellite data according to an embodiment of the present disclosure. Figure 3As shown, it includes: a reference station information extraction unit, a first processing unit and a second processing unit; wherein, The reference value information extraction unit is set as follows: based on the satellite-based augmentation service SSR correction number, the precise single point positioning ambiguity fixed PPP-AR positioning estimation is performed on the reference station to extract the ionosphere estimation value and the troposphere estimation value above the reference station; The first processing unit is configured to: perform first regional atmospheric modeling and first quality factor solution based on the extracted ionosphere estimation value and troposphere estimation value above the reference station to determine the first regional atmospheric model and the first quality factor; The second processing unit is configured to perform iterative solution of the second regional atmospheric model and the second quality factor based on the following processing: Based on the SSR correction number, the determined first regional atmospheric model and the first quality factor, the PPP-RTK positioning of the user terminal is estimated to extract the ionosphere estimation value and the troposphere estimation value above the user terminal; Filter the extracted ionosphere estimation value and troposphere estimation value above the user end; The second regional atmospheric modeling and the second quality factor are determined based on the extracted ionosphere estimation value and troposphere estimation value above the reference station and the screened ionosphere estimation value and troposphere estimation value above the user terminal.

[0045] In an exemplary embodiment, the reference value information extraction unit of the embodiment of the present disclosure is configured as follows: The PPP estimation model based on the SSR correction number estimates the floating point ambiguity of each frequency point , and convert the floating point ambiguity of each frequency point into the ultra-wide lane floating point ambiguity and wide lane floating point ambiguity ; Ultra-wide lane uncalibrated phase deviation using SSR service , Wide Lane Uncalibrated Phase Deviation , Narrow Lane Uncalibrated Phase Deviation Products that eliminate ultra-wide lane hardware delays at the receiver end through inter-satellite single difference , Wide Lane Hardware Delay , Narrow Lane Hardware Delay , achieving the converted ultra-wide lane floating point blur , Wide Lane Floating Point Ambiguity , and the ambiguity of the hardware delay between the pseudorange and the first frequency of the carrier The ambiguity of is fixed and the whole cycle ambiguity information is obtained; The obtained integer ambiguity information is introduced into the PPP estimation model based on the SSR correction number to obtain the ionosphere estimation value and troposphere estimation value above the reference station; The ionospheric and tropospheric estimates above the reference station include: ionospheric delay after ambiguity fixation and the corresponding floating ionospheric delay , the estimated value of the zenith tropospheric wet term after the ambiguity is fixed and the corresponding floating point estimate of the zenith tropospheric wet term .

[0046] In an exemplary embodiment, the first processing unit of the embodiment of the present disclosure is configured to determine a first regional atmospheric model, including: Based on the estimated ionosphere and troposphere values ​​above the reference station, the corresponding ionosphere model and troposphere model are determined.

[0047] In an exemplary embodiment, the second processing unit of the embodiment of the present disclosure is configured to perform PPP-RTK positioning estimation on the user terminal based on the SSR correction number, the obtained first regional atmospheric model and the determined first quality factor, including: determining an atmospheric constraint based on a first regional atmospheric model and a first quality factor; Add atmospheric constraints to the PPP estimation model based on SSR corrections; Based on the PPP estimation model based on SSR correction numbers by adding atmospheric constraints, PPP-RTK positioning estimation is performed on the user side.

[0048] In an exemplary embodiment, the first regional atmospheric model in the embodiment of the present disclosure includes a first ionosphere model and a first troposphere model, the first quality factor includes a first ionosphere quality factor and a first troposphere quality factor, the atmospheric constraint includes a troposphere constraint and an ionosphere constraint, and the expression of the atmospheric constraint is: ; In the formula, represents the ionospheric constraint, represents the ionosphere estimate, represents the value of the first ionospheric model above tracking station i, represents the ionospheric polynomial coefficient model value, represents the ionospheric grid point value, represents the first ionospheric quality factor, represents the tropospheric constraint, represents the tropospheric estimate, represents the value of the first tropospheric model above tracking station i, represents the zenith tropospheric dry part, represents the zenith tropospheric wet term, Represents the first tropospheric quality factor.

[0049] In an exemplary embodiment, the second processing unit of the embodiment of the present disclosure is configured to filter the extracted ionosphere estimation value and troposphere estimation value above the user terminal, including: The ionosphere estimation values ​​and troposphere estimation values ​​above the user end are screened through the cross comparison method and closed loop detection method.

[0050] In an exemplary example, the first regional atmospheric model in the embodiment of the present disclosure includes a first ionosphere model and a first troposphere model, the first ionosphere model is generated by combining a polynomial model with a grid point model, the first troposphere model is generated by using a grid point model, and solving the first quality factor includes: When the sum of the number of reference stations and user terminals used for the first regional atmospheric modeling near the grid point is less than 3, the first quality factor is generated by the following processing for the partition of the entire first regional atmospheric modeling of each satellite: Calculate the difference between the atmospheric estimate and the value from the atmospheric model for the first region: ; ; The atmospheric estimates include the following atmospheric estimates of the reference station: Ionosphere estimates above the reference station , tropospheric estimates , is the number of ionospheric grid points used for interpolation above the reference station, is the number of tropospheric grid points used for interpolation above the reference station, represents the reference station used for interpolation of grid point residual values, represents the difference between the ionospheric estimate and the value of the first ionospheric model, represents the ionosphere estimate, represents the ionospheric polynomial coefficient model value, represents the interpolated distance to the grid points, Represents the ionospheric grid point value, represents the difference between the estimated value of the tropospheric wet term and the value of the first tropospheric model, represents the estimated value of the tropospheric wet part, represents the grid point value of the tropospheric wet term, Represents the reference station used for interpolation of grid point residual values; Obtained by the difference between the atmospheric estimate and the value of the first regional atmospheric model and The percentile of is used as the first quality factor for the entire first-region atmospheric modeling partition: ; in, represents the first ionospheric quality factor, represents the first tropospheric quality factor; When the number of reference stations used for modeling near a grid point is greater than or equal to 3, the corresponding first quality factor is generated for each grid point of each satellite: ; In the formula, is the number of reference stations involved in modeling near the grid point, is the number of reference stations near the grid point that participate in modeling.

[0051] In an exemplary embodiment, the second regional atmospheric model in the embodiment of the present disclosure includes a second ionosphere model and a second troposphere model, the second ionosphere model is composed of a polynomial model and a grid point model, the second troposphere model is composed of a grid point model, and solving the second quality factor includes: When the sum of the number of reference stations and user terminals used for the second regional atmospheric modeling near the grid point is less than 3, the second quality factor is generated by the following processing for the partition of the entire second regional atmospheric modeling of each satellite: The second quality factor is generated for each satellite partition of the entire second region atmosphere model by the following process: Compute the difference between the atmospheric estimate and the value from the atmospheric model for the second region:

[0052]

[0053] The atmospheric estimation values ​​include the following atmospheric estimation values ​​of the reference station or the user end: Ionosphere estimation value above the reference station , tropospheric estimates , the estimated ionosphere above the user , tropospheric estimates , is the number of ionospheric grid points used for interpolation above the reference station or user terminal, is the number of tropospheric grid points used for interpolation above the reference station or user terminal, Represents the reference station or user end used for interpolation of grid point residual values. represents the difference between the ionospheric estimate and the value of the second ionospheric model, represents the ionosphere estimate, represents the ionospheric polynomial coefficient model value, represents the interpolated distance to the grid points, Represents the ionospheric grid point value, represents the difference between the estimated value of the tropospheric wet term and the value of the second tropospheric model, represents the estimated value of the tropospheric wet part, represents the grid point value of the tropospheric wet term, Represents the reference station or user end used for interpolation of grid point residual values; Obtained by the difference between the atmospheric estimate and the value of the second regional atmospheric model and The percentile of is used as the second quality factor for the entire second regional atmospheric modeling partition:

[0054] in, represents the second ionospheric quality factor, represents the second ionospheric quality factor; When the number of reference stations and user terminals used for modeling near a grid point is greater than or equal to 3, a corresponding second quality factor is generated for each grid point of each satellite:

[0055] In the formula, is the sum of the number of reference stations and user terminals involved in ionospheric modeling near the grid point, It is the sum of the number of reference stations and user terminals participating in tropospheric modeling near the grid point.

[0056] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or transient medium). As is known to those skilled in the art, the term "computer storage medium" includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

Claims

1. A method for satellite data processing, characterized in that: include: Based on the satellite-based augmentation service (SSR) correction number, the precise single point positioning ambiguity fixed PPP-AR positioning estimation is performed on the reference station to extract the ionosphere estimation value and troposphere estimation value above the reference station. Based on the extracted ionosphere estimation value and troposphere estimation value above the reference station, first regional atmosphere modeling and first quality factor solving are performed to determine the first regional atmosphere model and the first quality factor; The second regional atmospheric model and the second quality factor are iteratively solved based on the following process: Based on the SSR correction number, the determined first regional atmospheric model and the first quality factor, the PPP-RTK positioning of the user terminal is estimated to extract the ionosphere estimation value and the troposphere estimation value above the user terminal; Filter the extracted ionosphere estimation value and troposphere estimation value above the user end; The second regional atmospheric modeling and the second quality factor are determined based on the extracted ionosphere estimation value and troposphere estimation value above the reference station and the screened ionosphere estimation value and troposphere estimation value above the user terminal.

2. The method according to claim 1, characterized in that: The PPP-AR positioning estimation of the reference station based on the SSR correction number includes: The floating point ambiguity of each frequency point is estimated based on the PPP estimation model of the SSR correction number , and convert the floating point ambiguity of each frequency point into the ultra-wide lane floating point ambiguity and wide lane floating point ambiguity ; Ultra-wide lane uncalibrated phase deviation using SSR service , Wide Lane Uncalibrated Phase Deviation , Narrow Lane Uncalibrated Phase Deviation Products that eliminate ultra-wide lane hardware delays at the receiver end through inter-satellite single difference , Wide Lane Hardware Delay , Narrow Lane Hardware Delay , achieving the converted ultra-wide lane floating point ambiguity , Wide Lane Floating Point Ambiguity , and the ambiguity of the hardware delay between the pseudorange and the first frequency of the carrier The ambiguity of is fixed and the whole cycle ambiguity information is obtained; Bringing the obtained integer ambiguity information into the PPP estimation model based on the SSR correction number to obtain the ionosphere estimation value and the troposphere estimation value above the reference station; The ionospheric estimation value and tropospheric estimation value above the reference station include: the ionospheric delay after the ambiguity is fixed and the corresponding floating ionospheric delay , the estimated value of the zenith tropospheric wet term after the ambiguity is fixed and the corresponding floating-point zenith tropospheric wet term estimate .

3. The method according to claim 1, characterized in that Determining the first regional atmospheric model comprises: Based on the estimated ionosphere value and the estimated troposphere value above the reference station, a corresponding ionosphere model and troposphere model are determined.

4. The method according to claim 2, characterized in that: The method of performing PPP-RTK positioning estimation on the user terminal based on the SSR correction number, the obtained first regional atmospheric model and the determined first quality factor includes: determining an atmospheric constraint based on a first regional atmospheric model and a first quality factor; Adding atmospheric constraints to the PPP estimation model based on SSR corrections; Based on the PPP estimation model based on the SSR correction number by adding the atmospheric constraint, PPP-RTK positioning estimation is performed on the user end.

5. The method according to claim 4, characterized in that The first regional atmospheric model includes a first ionosphere model and a first troposphere model, the first quality factor includes a first ionosphere quality factor and a first troposphere quality factor, the atmospheric constraint includes a troposphere constraint and an ionosphere constraint, and the expression of the atmospheric constraint is: ； In the formula, represents the ionospheric constraint, represents the ionosphere estimate, represents the value of the first ionospheric model above tracking station i, represents the ionospheric polynomial coefficient model value, represents the ionospheric grid point value, represents the first ionospheric quality factor, represents the tropospheric constraint, represents the tropospheric estimate, represents the value of the first tropospheric model above tracking station i, represents the zenith tropospheric dry part, represents the zenith tropospheric wet term, Represents the first tropospheric quality factor.

6. The method according to any one of claims 1 to 5, characterized in that: The step of screening the extracted ionosphere estimation value and troposphere estimation value above the user terminal includes: The ionosphere estimation value and the troposphere estimation value above the user terminal are screened by a cross comparison method and a closed loop detection method.

7. The method according to any one of claims 1 to 5, characterized in that: The first regional atmospheric model includes a first ionosphere model and a first troposphere model, the first ionosphere model is generated by combining a polynomial model with a grid point model, and the first troposphere model is generated by using a grid point model, and solving the first quality factor includes: When the number of reference stations for the first regional atmospheric modeling near the grid point is less than 3, a first quality factor is generated for each satellite partition of the entire first regional atmospheric modeling by the following processing: Calculate the difference between the atmospheric estimate and the value from the atmospheric model for the first region: ； ； The atmospheric estimates include the following atmospheric estimates of the reference station: Ionosphere estimates above the reference station , tropospheric estimates , is the number of ionospheric grid points used for interpolation above the reference station, is the number of tropospheric grid points used for interpolation above the reference station, represents the reference station used for interpolation of grid point residual values, represents the difference between the ionospheric estimate and the value of the first ionospheric model, represents the ionosphere estimate, represents the ionospheric polynomial coefficient model value, represents the interpolated distance to the grid points, Represents the ionospheric grid point value, represents the difference between the estimated value of the tropospheric wet term and the value of the first tropospheric model, represents the estimated value of the tropospheric wet part, represents the grid point value of the tropospheric wet term, Represents the reference station used for interpolation of grid point residual values; The difference between the atmospheric estimation value and the value of the first regional atmospheric model is obtained and The percentile of is used as the first quality factor for the entire first-region atmospheric modeling partition: ； in, represents the first ionospheric quality factor, represents the first tropospheric quality factor; When the number of reference stations used for modeling near the grid point is greater than or equal to 3, a corresponding first quality factor is generated for each grid point of each satellite: ； In the formula, is the number of reference stations involved in modeling near the grid point, is the number of reference stations near the grid point that participate in modeling.

8. The method according to any one of claims 1 to 5, characterized in that: The second regional atmospheric model includes a second ionosphere model and a second troposphere model, the second ionosphere model is composed of a polynomial model and a grid point model, the second troposphere model is composed of a grid point model, and solving the second quality factor includes: When the sum of the number of reference stations and user terminals used for the second regional atmospheric modeling near the grid point is less than 3, the second quality factor is generated by the following processing for the partition of the entire second regional atmospheric modeling of each satellite: The second quality factor is generated for each satellite partition of the entire second region atmosphere model by the following process: Compute the difference between the atmospheric estimate and the value from the atmospheric model for the second region: The atmospheric estimation values ​​include the following atmospheric estimation values ​​of the reference station or the user end: Ionosphere estimation value above the reference station , tropospheric estimates , the estimated ionosphere above the user , tropospheric estimates , is the number of ionospheric grid points used for interpolation above the reference station or user terminal, is the number of tropospheric grid points used for interpolation above the reference station or user terminal, Represents the reference station or user end used for interpolation of grid point residual values. represents the difference between the ionospheric estimate and the value of the second ionospheric model, represents the ionosphere estimate, represents the ionospheric polynomial coefficient model value, represents the interpolated distance to the grid points, Represents the ionospheric grid point value, represents the difference between the estimated value of the tropospheric wet term and the value of the second tropospheric model, represents the estimated value of the tropospheric wet part, represents the grid point value of the tropospheric wet term, Represents the reference station or user end used for interpolation of grid point residual values; Obtained by the difference between the atmospheric estimate and the value of the second regional atmospheric model and The percentile of is used as the second quality factor for the entire second regional atmospheric modeling partition: in, represents the second ionospheric quality factor, represents the second ionospheric quality factor; When the number of reference stations and user terminals used for modeling near a grid point is greater than or equal to 3, a corresponding second quality factor is generated for each grid point of each satellite: In the formula, is the sum of the number of reference stations and user terminals involved in ionospheric modeling near the grid point, It is the sum of the number of reference stations and user terminals participating in tropospheric modeling near the grid point.

9. A computer storage medium, wherein a computer program is stored in the computer storage medium, and when the computer program is executed by a processor, the method for processing satellite data according to any one of claims 1 to 8 is implemented.

10. A terminal, comprising: A memory and a processor, wherein the memory stores a computer program; wherein, The processor is configured to execute the computer program in the memory; When the computer program is executed by the processor, the method for satellite data processing according to any one of claims 1 to 8 is implemented.

11. A satellite data processing device, comprising: A reference station information extraction unit, a first processing unit and a second processing unit; wherein, The reference value information extraction unit is set as follows: based on the satellite-based augmentation service SSR correction number, the precise single point positioning ambiguity fixed PPP-AR positioning estimation is performed on the reference station to extract the ionosphere estimation value and the troposphere estimation value above the reference station; The first processing unit is configured to: perform first regional atmospheric modeling and first quality factor solution based on the extracted ionosphere estimation value and troposphere estimation value above the reference station to determine the first regional atmospheric model and the first quality factor; The second processing unit is configured to perform iterative solution of the second regional atmospheric model and the second quality factor based on the following processing: Based on the SSR correction number, the determined first regional atmospheric model and the first quality factor, the PPP-RTK positioning of the user terminal is estimated to extract the ionosphere estimation value and the troposphere estimation value above the user terminal; Filter the extracted ionosphere estimation value and troposphere estimation value above the user end; The second regional atmospheric modeling and the second quality factor are determined based on the extracted ionosphere estimation value and troposphere estimation value above the reference station and the screened ionosphere estimation value and troposphere estimation value above the user terminal.

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