Observation data forecasting method and device, computer storage medium and terminal

Abstract

The invention discloses an observation data forecasting method and device, a computer storage medium and a terminal. The method comprises the steps that troposphere information and ionosphere information are estimated in real time through a preset engine; forecasting the observation data of the tracking station by using a satellite orbit and a satellite clock error correction number in the correction number of the satellite-based enhanced service SSR and troposphere information and ionosphere information which are estimated in real time; according to the predicted tracking station observation data, precision factor information of the predicted observation value is solved; wherein the preset engine comprises a precision point positioning (PPP) engine or a precision point positioning ambiguity fixing (PPP-AR) engine. According to the embodiment of the invention, the PPP / PPP-AR engine is reused for real-time continuous operation to forecast the observation data, so that the forecast quality of the GNSS observation data is improved, and the determination of forecast precision information is realized; furthermore, the prediction error source is compensated based on the SSR correction number, and the error compensation quality is improved.

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 forecasting observation 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, which are 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 Geostationary Navigation Overlay Service (EGNOS), the Service Embedded BeiDou System (BDSBAS) and the Galileo Free High Accuracy Service (Galileo HAS). High-precision applications in related technologies, such as geodesy, deformation monitoring and drone mapping, usually use single-station real-time dynamic carrier phase difference technology (RTK), small area network real-time dynamic positioning technology (NRTK), and precise point positioning technology (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, some companies have begun to provide NRTK, PPP-RTK and other services based on large-scale GNSS tracking station networks.

[0003] GNSS signals are fragile and susceptible to natural or man-made interference. Physical obstacles such as buildings, trees, and mountains may block or weaken GNSS signals. For cost considerations, most tracking stations use fourth-generation mobile communication (4G) modules for data transmission. 4G networks often encounter unstable 4G network signals caused by high loads in hot spots, hardware, and operator networks, which in turn lead to delays and interruptions in GNSS observation data. In addition, receiver failures, power supply problems, and failures in raw data access and forwarding platforms can all lead to interruptions in GNSS observation data. In order to meet the requirements of high service availability and comprehensively consider operating costs, it is necessary to predict GNSS observation data to solve or alleviate data delays, data interruptions, and data quality degradation.

[0004] A large number of studies have focused on the above-mentioned GNSS observation data interruption problem. The more representative literature includes: Du studied the prediction technology of double-difference observation data, supporting asynchronous RTK and the generation of double-difference observation values ​​of geosynchronous orbit (GEO) satellites. The single data delay is 30 seconds, and the positioning error in the sky direction reaches 30 cm, which cannot meet the needs of high-precision positioning. At the same time, this method is based on the double-difference mode and is not widely applicable, such as PPP positioning. Lawrence proposed a prediction strategy based on the historical data of the tracking station. Using 7-second data, the prediction accuracy of 10 seconds is 10 cm, and the prediction accuracy is low. Xu Zhuo et al. also proposed a GNSS observation data prediction strategy for a single tracking station. For the accumulated error, a first-order model is used for compensation. Since the prediction error source is not well corrected, the characteristics of the accumulated error are irregular. In addition, the first-order model accumulation error fitting model is relatively simple. Zhou Guangyu et al.'s patent introduces a prediction scheme for a single tracking station. Similarly, there is no high-precision correction of the prediction error source, which cannot meet the requirements of high-precision applications.

[0005] In summary, the GNSS observation data forecast in related technologies has the following four technical defects: first, the architecture does not consider the applicability of multiple application scenarios and the reusability of existing data processing tasks; second, the forecast error source is not well compensated with high precision; third, the characteristic analysis of the forecast accumulated error is insufficient, which leads to poor compensation effect; fourth, the forecast accuracy information of the observation data is not given. How to improve the forecast quality of GNSS observation data has become a problem to be solved. Summary of the invention

[0006] The present application provides a method for forecasting observation data, including:

[0007] Use the preset engine to estimate tropospheric and ionospheric information in real time;

[0008] The satellite orbit and satellite clock correction in the satellite-based augmentation service (SSR) correction number, as well as the real-time estimated tropospheric information and ionospheric information, are used to forecast the observation data of the tracking station;

[0009] Based on the forecast tracking station observation data, the precision factor information of the forecast observation value is solved;

[0010] The preset engine includes: a precise point positioning PPP engine or a precise point positioning ambiguity fixed PPP-AR engine.

[0011] 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 method of observing data forecasting is implemented.

[0012] 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:

[0013] The processor is configured to execute the computer program in the memory;

[0014] When the computer program is executed by the processor, the method for forecasting observed data as described above is implemented.

[0015] In another aspect, the embodiment of the present application further provides a device for forecasting observation data, comprising: an estimation unit, a forecasting unit and a solution processing unit; wherein:

[0016] The estimation unit is configured to: estimate tropospheric information and ionospheric information in real time using a preset engine;

[0017] The forecast unit is set to: forecast the observation data of the tracking station by using the satellite orbit, satellite clock correction number in the satellite-based augmentation service SSR correction number, and the real-time estimated tropospheric information and ionospheric information;

[0018] The solution processing unit is set to: solve the precision factor information of the predicted observation value according to the predicted tracking station observation data;

[0019] Among them, the preset engines include: precise point positioning PPP engine or precise point positioning ambiguity fixed PPP-AR engine.

[0020] The disclosed embodiment reuses the PPP / PPP-AR engine to run continuously in real time to forecast observation data, thereby improving the forecast quality of GNSS observation data and realizing the determination of forecast accuracy information; further, the forecast error source is compensated based on the SSR correction number, thereby improving the error compensation quality.

[0021] 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

[0022] 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.

[0023] Figure 1 A flowchart of a method for forecasting observation data according to an embodiment of the present disclosure;

[0024] Figure 2 A schematic diagram of observation data of a tracking station predicted by an embodiment of the present disclosure;

[0025] Figure 3 The present invention is a structural block diagram of the device for forecasting observation data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] 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.

[0027] 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 implemented 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.

[0028] 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.

[0029] Figure 1 Flow chart of the method for forecasting observation data according to an embodiment of the present disclosure, as shown in FIG. Figure 1 As shown, including:

[0030] Step 101: using a preset engine to estimate tropospheric information and ionospheric information in real time;

[0031] Step 102: forecast the observation data of the tracking station by using the satellite orbit, satellite clock correction number in the satellite-based augmentation service (SSR) correction number, and the real-time estimated tropospheric information and ionospheric information;

[0032] Step 103, according to the forecasted tracking station observation data, solving the precision factor information of the predicted observation value;

[0033] Among them, the preset engines include: precise point positioning (PPP) engine or precise point positioning ambiguity fixed (PPP-AR) engine.

[0034] The disclosed embodiment utilizes the PPP / PPP-AR engine to estimate high-precision tropospheric and ionospheric information in real time, and combines the high-precision satellite orbit and satellite clock correction numbers in the SSR correction numbers to perform high-precision prediction of the tracking station observation data, and solves the precision factor information of the predicted observation value based on the predicted tracking station observation data, fully considering the resource reuse of the large-scale GNSS data processing center, improving the forecast quality of the GNSS observation data, and realizing the determination of the forecast accuracy information.

[0035] In an exemplary embodiment, the GNSS original observation equation of the embodiment of the present disclosure can be expressed as:

[0036]

[0037] in, is the carrier observation value of tracking station i tracking satellite s at frequency k, is the pseudorange observation value of tracking station i tracking satellite s at frequency k, is the geometric distance from tracking station i to satellite s, is the ionospheric delay at frequency 1 in the line of sight, f and λ are the signal frequency and wavelength, is the tropospheric delay in the line of sight, dt is the receiver clock error, and dt s is the satellite clock error, δ i,k is the carrier hardware delay of the receiver at frequency k, δ s,k is the carrier hardware delay of the satellite at frequency k, d i,k is the pseudorange hardware delay of the receiver at frequency k, d s,k is the pseudorange hardware delay of the satellite at frequency k, N k is the ambiguity of frequency k, is the carrier observation noise, is the pseudorange observation noise.

[0038] In an exemplary embodiment, the embodiment of the present disclosure uses the satellite orbit, satellite clock correction number, and real-time estimated tropospheric information and ionospheric information in the SSR correction number to forecast the observation data of the tracking station, including:

[0039] By making a single difference between the observations at epoch n+m and epoch n, we get:

[0040]

[0041] Use the observations at epoch n to determine the observations at epoch n+m:

[0042]

[0043] In the formula, is the carrier observation value of epoch n+m when tracking station i tracks satellite s at frequency point k, is the pseudorange observation value of epoch n+m when tracking station i tracks satellite s at frequency k, is the carrier observation value of tracking station i tracking satellite s at frequency point k at epoch n, is the pseudorange observation value of epoch n when tracking station i tracks satellite s at frequency point k, Δ is the single difference operator, f1 represents the signal frequency of frequency point 1, and f k represents the signal frequency at frequency point k, represents the single difference between epoch n+m and epoch n of the geometric distance from tracking station i to satellite s, The single difference between epoch n+m and epoch n represents the ionospheric delay at frequency point 1 in the line of sight direction, Δdt i,(n,n+m) The single difference between epoch n+m and epoch n represents the receiver clock error, Δdt s (n,n+m) Represents the single difference between epoch n+m and epoch n of the satellite clock error, Δδ i,k,(n,n+m) The single difference between epoch n+m and epoch n of the carrier hardware delay of the receiver at frequency k, Δδ s,k n,n+m The single difference between epoch n+m and epoch n of the carrier hardware delay of the satellite at frequency k, ΔN k,(n,n+m) represents the single difference between epoch n+m and epoch n of the ambiguity of frequency point k, represents the single difference between epoch n+m and epoch n of the carrier observation noise, The single difference between epoch n+m and epoch n represents the tropospheric delay in the line of sight, Δd s,k (n,n+m) Represents the single difference between epoch n+m and epoch n of the pseudorange hardware delay of the satellite at frequency k.

[0044] In an exemplary embodiment, the observation data of the tracking station predicted by the embodiment of the present disclosure includes one or any combination of the following:

[0045] consider The predicted observation value of

[0046] consider The predicted observation value of

[0047] consider The predicted observation value of

[0048] In one illustrative example, It only involves the prediction of the observation value of the tracking station, and the position of the tracking station remains unchanged. Referring to the relevant technology, the precise satellite orbit r in step 102 of the embodiment of the present disclosure s It can be obtained from the SSR correction number:

[0049] r s =r eph -δr s (7)

[0050] Among them, r eph is the satellite orbit in the broadcast ephemeris, δr s It is the precise satellite orbit correction number broadcast by the satellite-based augmentation service.

[0051] For c(Δdt i,(n,n+m) -Δdt s (n,n+m) ), since the receiver clock error can be easily estimated in the positioning engine, the receiver clock error can be considered unchanged in the observation value forecast; the GNSS satellite uses a highly stable atomic clock, and the accuracy can be maintained at the centimeter level within tens of seconds. The satellite clock error can be considered unchanged in the short-term observation value forecast. For the long-term observation value forecast, such as the minute level, referring to the relevant technology, the precise satellite clock error correction number in step 102 of the embodiment of the present disclosure can be obtained from the SSR correction number:

[0052] dt s =dt eph -δdt s / c (8)

[0053] dt eph is the satellite clock error in the broadcast ephemeris, δdt s It is the precise satellite clock correction number broadcast by the satellite-based augmentation service.

[0054] In the embodiment of the present disclosure, referring to the related art, the observation data of the high-precision tracking station forecast may also include the following data in addition to step 102: i,k,(n,n+m) -Δδ s,k n,n+m )、c(Δd i,k,(n,n+m) +Δd s,k (n,n+m) ), since the receiver-side, satellite-side carrier and pseudorange hardware delays are stable in the short term, they can be considered unchanged in the observation value forecast, that is:

[0055] c(Δδ i,k,(n,n+m) -Δδ s,k n,n+m )=0

[0056] c(Δd i,k,(n,n+m) +Δd s,k (n,n+m) )=0 (9)

[0057] For λ k ΔN k,(n,n+m) , the integer ambiguity does not change in the continuous tracking state and can be considered unchanged in the observation value forecast.

[0058] for The carrier and pseudorange observation noises are random noises, and the statistical characteristics of the differences between epochs are also consistent. The observation value noise of the reference epoch can be used in the observation data forecast.

[0059] for Zenith tropospheric delay (ZTD) is relatively stable. ZTD may adopt a classical model value at a reference time, or a PPP / PPP-AR estimated value at a reference time. The tropospheric information of the embodiment of the present disclosure may include a single difference value between epoch n+m and epoch n of the tropospheric delay in the line of sight direction. The single difference value between epoch n+m and epoch n of the tropospheric delay in the line of sight direction is:

[0060]

[0061] It is the change of the tropospheric delay mapping function in the line of sight direction at the forecast time relative to the reference time.

[0062] for The ionospheric error is greatly affected by local time, geomagnetic environment, and solar activity. The accuracy of non-real-time models and VTEC models is limited, and the ionospheric prediction accuracy cannot be guaranteed. The embodiment of the present disclosure uses the high-precision ionospheric information estimation value estimated by PPP / PPP-AR to perform short-term ionospheric forecasting, and uses the high-precision ionospheric information estimation value of the historical 1 epoch:

[0063]

[0064] The estimation accuracy of PPP / PPP-AR ionospheric information is relatively high, and the estimated value of the ionospheric information can also be used as the true value in engineering. The ionospheric predictor in formula (11) adopts a polynomial fitting and prediction strategy.

[0065] In an exemplary embodiment, the embodiment of the present disclosure solves the precision factor information of the predicted observation value according to the predicted tracking station observation data, including:

[0066] Compensating the forecast tracking station observation data to obtain first compensated forecast observation data and second compensated forecast observation data after compensation for forward forecast and reverse forecast respectively;

[0067] The precision factor information is determined based on the obtained second compensated prediction observation data.

[0068] In an exemplary embodiment, the forward forecasting and directional forecasting methods of the embodiments of the present disclosure are error correction methods in the related art. Taking forward forecasting as an example, the following may be included:

[0069] Determine the forecast accumulated error based on the forecast observation data;

[0070] According to the determined forecast accumulated error, the forecast accumulated error is fitted by using a quadratic polynomial;

[0071] After the observation data is interrupted, the predicted observation data is compensated by using the result of the forecast accumulated error fitting to obtain the first compensated predicted observation data.

[0072] The disclosed embodiment compensates for the forecast error source based on the SSR correction number, and adopts a polynomial model to fit and compensate for the forecast cumulative error. When the observation data is interrupted, the forecast observation data and the precision factor of the predicted observation value are provided to the downstream application module, and the downstream application module can determine the application weight of the predicted observation value according to the precision factor.

[0073] In an illustrative example, the observation value forecast error will gradually accumulate over time, especially under active ionospheric conditions, the error accumulation speed is faster, and the error accumulation presents a smooth curve in the time domain. When the observation data of the tracking station is not missing, the observation value forecast and the forecast accumulation error can be obtained at the same time, and the forecast accumulation error can be fitted using a quadratic polynomial. After the observation data is interrupted, the fitting model can be used to compensate for the predicted observation value, thereby further improving the accuracy of the observation value forecast.

[0074] In an exemplary embodiment, when the forecasted observation data includes carrier observations:

[0075] The accumulated error of the forecast is: In the formula, is the carrier observation value of tracking station i tracking satellite s, is the carrier observation value without compensation using the result of forecast accumulated error fitting, is the forecast accumulated error of the carrier observation value;

[0076] The quadratic polynomial fitting of the forecast accumulated error using historical o epochs is:

[0077] In the formula, is the forecast accumulated error of the carrier observation value at epoch no, is the forecast accumulated error of the carrier observation at epoch n-o+1, is the forecast accumulated error of the carrier observation at epoch n;

[0078] The forecast observation data is compensated by the result of forecast accumulated error fitting, and the first compensated forecast observation data is obtained as follows:

[0079] In the formula, is the carrier observation value at epoch n+m that is not compensated by the result of forecast accumulated error fitting;

[0080] From the reference time n′ epoch, the observation value is reversely predicted to the n′-m′ epoch to obtain the second compensated predicted observation data; thus, the prediction accuracy of the carrier observation value obtained is:

[0081]

[0082] In the formula, σ represents the accuracy, is the carrier observation value of tracking station i tracking satellite s at epoch n′-m′, It is the carrier observation value predicted at epoch n′-m′ compensated by the result of forecast accumulated error fitting.

[0083] The prediction accuracy of the observation value in the embodiment of the present disclosure is related to the satellite altitude angle, the ionosphere active conditions, the troposphere active conditions, etc. In the short term, it can be considered that the above influencing factors will not change much. The embodiment of the present disclosure uses reverse prediction to determine the precision factor information of the forward prediction observation value. Figure 2 , the embodiments of the present disclosure consider The predicted observation value of As shown in point 2-1, consider The predicted observation value of As shown in point 2-2, consider The predicted observation value of As shown in point 2-3, the forecast observation data is compensated by the result of forecast accumulated error fitting As shown in point 2-4, the carrier observation value of tracking station i tracking satellite s at epoch n+m As shown in point 2-5, the interval 2-6 from point 2-4 to 2-5 is Interval 2-7 is From the reference time n′ epoch, the observation value is predicted backward to the n′-m′ epoch, where m′ is close to m; since m′ is close to m, and the time span from the n′-m′ epoch to the n+m epoch is not large, the factors affecting the forecast accuracy do not change much, so the above calculation formula for the forecast accuracy of the carrier observation value can be considered to be Approximately

[0084] In an exemplary embodiment, when the observation data predicted by the embodiment of the present disclosure includes pseudorange observation values:

[0085] The accumulated error of the forecast is: In the formula, is the pseudorange observation value of tracking station i tracking satellite s, is the pseudorange observation value without compensation using the result of forecast accumulated error fitting, is the forecast accumulated error of the carrier observation value;

[0086] The quadratic polynomial fitting of the forecast accumulated error using historical o epochs is:

[0087] In the formula, is the forecast accumulated error of the pseudorange observation at epoch no, is the forecast accumulated error of the pseudorange observation at epoch n-o+1, is the forecast accumulated error of the pseudorange observation at epoch n;

[0088] The forecast observation data is compensated by the result of forecast accumulated error fitting, and the first compensated forecast observation data is obtained as follows:

[0089] In the formula, is the pseudorange observation value of epoch n+m that is not compensated by the result of forecast accumulated error fitting;

[0090] From the reference time n′ epoch, the observation value is reversely predicted to the n′-m′ epoch to obtain the second compensated predicted observation data; the prediction accuracy of the pseudorange observation value obtained based on this is:

[0091]

[0092] In the formula, σ represents the accuracy, is the pseudorange observation value of tracking station i tracking satellite s at epoch n′-m′, It is the pseudorange observation value of the epoch n′-m′ forecast compensated by the result of forecast accumulated error fitting.

[0093] The embodiment of the present disclosure also provides a computer storage medium, in which a computer program is stored. When the computer program is executed by a processor, the above-mentioned method of observing data forecasting is implemented.

[0094] The embodiment of the present disclosure also provides a terminal, including: a memory and a processor, wherein a computer program is stored in the memory;

[0095] in,

[0096] The processor is configured to execute the computer program in the memory;

[0097] When the computer program is executed by a processor, the method for forecasting observed data as described above is implemented.

[0098] Figure 3 The structure diagram of the device for forecasting observation data according to the embodiment of the present disclosure is as follows: Figure 3 As shown, it includes: an estimation unit, a forecasting unit and a solution processing unit; wherein,

[0099] The estimation unit is configured to: estimate tropospheric information and ionospheric information in real time using a preset engine;

[0100] The forecast unit is set to: forecast the observation data of the tracking station by using the satellite orbit, satellite clock correction number in the satellite-based augmentation service SSR correction number, and the real-time estimated tropospheric information and ionospheric information;

[0101] The solution processing unit is set to: solve the precision factor information of the predicted observation value according to the predicted tracking station observation data;

[0102] Among them, the preset engines include: precise point positioning PPP engine or precise point positioning ambiguity fixed PPP-AR engine.

[0103] In an exemplary embodiment, the forecasting unit of the embodiment of the present disclosure is configured as follows:

[0104] By making a single difference between the observations at epoch n+m and epoch n, we get:

[0105]

[0106] Use the observations at epoch n to determine the observations at epoch n+m:

[0107]

[0108] In the formula, is the carrier observation value of epoch n+m when tracking station i tracks satellite s at frequency point k, is the pseudorange observation value of epoch n+m when tracking station i tracks satellite s at frequency k, is the carrier observation value of tracking station i tracking satellite s at frequency point k at epoch n, is the pseudorange observation value of epoch n when tracking station i tracks satellite s at frequency point k, Δ is the single difference operator, f1 represents the signal frequency of frequency point 1, and f k represents the signal frequency at frequency point k, represents the single difference between epoch n+m and epoch n of the geometric distance from tracking station i to satellite s, The single difference between epoch n+m and epoch n represents the ionospheric delay at frequency point 1 in the line of sight direction, Δdt i,(n,n+m) The single difference between epoch n+m and epoch n represents the receiver clock error, Δdt s (n,n+m) Represents the single difference between epoch n+m and epoch n of the satellite clock error, Δδ i,k,(n,n+m) The single difference between epoch n+m and epoch n of the carrier hardware delay of the receiver at frequency k, Δδ s,k n,n+m The single difference between epoch n+m and epoch n of the carrier hardware delay of the satellite at frequency k, ΔN k,(n,n+m) represents the single difference between epoch n+m and epoch n of the ambiguity of frequency point k, represents the single difference between epoch n+m and epoch n of the carrier observation noise, The single difference between epoch n+m and epoch n represents the tropospheric delay in the line of sight, Δd s,k (n,n+m) Represents the single difference between epoch n+m and epoch n of the pseudorange hardware delay of the satellite at frequency k.

[0109] In an exemplary embodiment, the observation data of the tracking station for forecast in the embodiment of the present disclosure includes one or any combination of the following:

[0110] consider The predicted observation value of

[0111] consider The predicted observation value of

[0112] consider The predicted observation value of

[0113] In an exemplary embodiment, the solution processing unit of the embodiment of the present disclosure is configured as follows:

[0114] Compensating the forecast tracking station observation data to obtain first compensated forecast observation data and second compensated forecast observation data after compensation for forward forecast and reverse forecast respectively;

[0115] The precision factor information is determined based on the obtained second compensated prediction observation data.

[0116] In an exemplary embodiment, when the observation data predicted by the embodiment of the present disclosure includes carrier observation values:

[0117] The accumulated error of the forecast is: In the formula, is the carrier observation value of tracking station i tracking satellite s, is the carrier observation value without compensation using the result of forecast accumulated error fitting, is the forecast accumulated error of the carrier observation value;

[0118] The quadratic polynomial fitting of the forecast accumulated error using historical o epochs is:

[0119] In the formula, is the forecast accumulated error of the carrier observation value at epoch no, is the forecast accumulated error of the carrier observation at epoch n-o+1, is the forecast accumulated error of the carrier observation at epoch n;

[0120] The forecast observation data is compensated by using the result of forecast accumulated error fitting to obtain the first compensated forecast observation data:

[0121] In the formula, is the carrier observation value at epoch n+m that is not compensated by the result of forecast accumulated error fitting;

[0122] From the reference time n′ epoch, reversely predict the observation value to the n′-m′ epoch to obtain the second compensated prediction observation data;

[0123] Based on this, the carrier observation value prediction accuracy is obtained as follows:

[0124]

[0125] In the formula, σ represents the accuracy, is the carrier observation value of tracking station i tracking satellite s at epoch n′-m′, It is the carrier observation value predicted at epoch n′-m′ compensated by the result of forecast accumulated error fitting.

[0126] In an exemplary embodiment, when the observation data predicted by the embodiment of the present disclosure includes pseudorange observation values:

[0127] The accumulated error of the forecast is: In the formula, is the pseudorange observation value of tracking station i tracking satellite s, is the pseudorange observation value without compensation using the result of forecast accumulated error fitting, is the forecast accumulated error of the carrier observation value;

[0128] The quadratic polynomial fitting of the forecast accumulated error using historical o epochs is:

[0129] In the formula, is the forecast accumulated error of the pseudorange observation at epoch no, is the forecast accumulated error of the pseudorange observation at epoch n-o+1, is the forecast accumulated error of the pseudorange observation at epoch n;

[0130] The forecast observation data is compensated by using the result of forecast accumulated error fitting to obtain the first compensated forecast observation data:

[0131] In the formula, is the pseudorange observation value of epoch n+m that is not compensated by the result of forecast accumulated error fitting;

[0132] From the reference time n' epoch, the observation value is reversely predicted to the n'-m' epoch to obtain the second compensated predicted observation data. Based on this, the prediction accuracy of the pseudorange observation value is obtained as:

[0133] In the formula, σ represents the accuracy, is the pseudorange observation value of tracking station i tracking satellite s at epoch n′-m′, It is the pseudorange observation value of the epoch n′-m′ forecast compensated by the result of forecast accumulated error fitting.

[0134] 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 temporary medium). As 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 of ordinary skill 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 forecasting observation data, characterized in that: include: Use the preset engine to estimate tropospheric and ionospheric information in real time; The satellite orbit and satellite clock correction in the satellite-based augmentation service (SSR) correction number, as well as the real-time estimated tropospheric information and ionospheric information, are used to forecast the observation data of the tracking station; Based on the forecast tracking station observation data, the precision factor information of the forecast observation value is solved; The preset engine includes: a precise point positioning PPP engine or a precise point positioning ambiguity fixed PPP-AR engine.

2. The method according to claim 1, characterized in that The method uses the satellite orbit, satellite clock correction, and real-time estimated tropospheric information and ionospheric information in the SSR correction number to forecast the observation data of the tracking station, including: By making a single difference between the observations at epoch n+m and epoch n, we get: Use the observations at epoch n to determine the observations at epoch n+m: In the formula, is the carrier observation value of epoch n+m when tracking station i tracks satellite s at frequency point k, is the pseudorange observation value of epoch n+m when tracking station i tracks satellite s at frequency k, is the carrier observation value of tracking station i tracking satellite s at frequency point k at epoch n, is the pseudorange observation value of epoch n when tracking station i tracks satellite s at frequency point k, Δ is the single difference operator, f1 represents the signal frequency of frequency point 1, and f k represents the signal frequency at frequency point k, represents the single difference between epoch n+m and epoch n of the geometric distance from tracking station i to satellite s, The single difference between epoch n+m and epoch n represents the ionospheric delay at frequency point 1 in the line of sight direction, Δdt i,(n,n+m) The single difference between epoch n+m and epoch n represents the receiver clock error, Δdt s (n,n+m) Represents the single difference between epoch n+m and epoch n of the satellite clock error, Δδ i,k,(n,n+m) The single difference between epoch n+m and epoch n of the carrier hardware delay of the receiver at frequency k, Δδ s,k n,n+m The single difference between epoch n+m and epoch n of the carrier hardware delay of the satellite at frequency k, ΔN k,(n,n+m) represents the single difference between epoch n+m and epoch n of the ambiguity of frequency point k, represents the single difference between epoch n+m and epoch n of the carrier observation noise, The single difference between epoch n+m and epoch n represents the tropospheric delay in the line of sight, Δd s,k (n,n+m) Represents the single difference between epoch n+m and epoch n of the pseudorange hardware delay of the satellite at frequency k.

3. The method according to claim 2, characterized in that The observation data of the tracking station to be forecasted includes one or any combination of the following: consider Δdt s (n,n+m) The predicted observation value of consider Δdt s (n,n+m) , The predicted observation value of consider Δdt s (n,n+m) , The predicted observation value of 4. The method according to any one of claims 1 to 3, characterized in that: The step of solving the precision factor information of the predicted observation value based on the predicted tracking station observation data includes: Compensating the forecast tracking station observation data to obtain first compensated forecast observation data and second compensated forecast observation data after compensation for forward forecast and reverse forecast respectively; The precision factor information is determined based on the obtained second compensated prediction observation data.

5. The method according to claim 4, characterized in that When the forecast observation data includes carrier observation values: The accumulated error of the forecast is: In the formula, is the carrier observation value of tracking station i tracking satellite s, is the carrier observation value that is not compensated by the result of forecast accumulated error fitting, is the forecast accumulated error of the carrier observation value; The quadratic polynomial fitting of the forecast accumulated error using historical o epochs is: In the formula, is the forecast accumulated error of the carrier observation at epoch no, is the forecast accumulated error of the carrier observation at epoch n-o+1, is the forecast accumulated error of the carrier observation at epoch n; The forecast observation data is compensated by using the result of forecast accumulated error fitting to obtain the first compensated forecast observation data: In the formula, is the carrier observation value at epoch n+m that is not compensated by the result of forecast accumulated error fitting; From the reference time n′ epoch, reversely predict the observation value to the n′-m′ epoch to obtain the second compensated predicted observation data; Based on this, the carrier observation value prediction accuracy is obtained as follows: In the formula, σ represents the accuracy, is the carrier observation value of tracking station i tracking satellite s at epoch n′-m′, It is the carrier observation value predicted at epoch n′-m′ compensated by the result of forecast accumulated error fitting.

6. The method according to claim 4, characterized in that When the forecasted observation data include pseudorange observation values: The accumulated error of the forecast is: In the formula, is the pseudorange observation value of tracking station i tracking satellite s, is the pseudorange observation value without compensation using the result of forecast accumulated error fitting, is the forecast accumulated error of the carrier observation value; The quadratic polynomial fitting of the forecast accumulated error using historical o epochs is: In the formula, is the forecast accumulated error of the pseudorange observation at epoch no, is the forecast accumulated error of the pseudorange observation at epoch n-o+1, is the forecast accumulated error of the pseudorange observation at epoch n; The forecast observation data is compensated by using the result of forecast accumulated error fitting to obtain the first compensated forecast observation data: In the formula, is the pseudorange observation value of epoch n+m that is not compensated by the result of forecast accumulated error fitting; From the reference time n' epoch, the observation value is reversely predicted to the n'-m' epoch to obtain the second compensated predicted observation data. Based on this, the prediction accuracy of the pseudorange observation value is obtained as: In the formula, σ represents the accuracy, is the pseudorange observation value of tracking station i tracking satellite s at epoch n′-m′, It is the pseudorange observation value of the epoch n′-m′ forecast compensated by the result of forecast accumulated error fitting.

7. 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 forecasting observation data according to any one of claims 1 to 6 is implemented.

8. 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 forecasting observed data according to any one of claims 1 to 6 is implemented.

9. A device for forecasting observation data, comprising: Estimation unit, forecasting unit and solution processing unit; wherein, The estimation unit is configured to: estimate tropospheric information and ionospheric information in real time using a preset engine; The forecast unit is set to: forecast the observation data of the tracking station by using the satellite orbit, satellite clock correction number in the satellite-based augmentation service SSR correction number, and the real-time estimated tropospheric information and ionospheric information; The solution processing unit is set to: solve the precision factor information of the predicted observation value according to the predicted tracking station observation data; Among them, the preset engines include: precise point positioning PPP engine or precise point positioning ambiguity fixed PPP-AR engine.

10. The device according to claim 9, characterized in that The forecast unit is configured as follows: By making a single difference between the observations at epoch n+m and epoch n, we get: Use the observations at epoch n to determine the observations at epoch n+m: In the formula, is the carrier observation value of epoch n+m when tracking station i tracks satellite s at frequency point k, is the pseudorange observation value of epoch n+m when tracking station i tracks satellite s at frequency k, is the carrier observation value of tracking station i tracking satellite s at frequency point k at epoch n, is the pseudorange observation value of epoch n when tracking station i tracks satellite s at frequency point k, Δ is the single difference operator, f1 represents the signal frequency of frequency point 1, and f k represents the signal frequency at frequency point k, represents the single difference between epoch n+m and epoch n of the geometric distance from tracking station i to satellite s, The single difference between epoch n+m and epoch n represents the ionospheric delay at frequency point 1 in the line of sight direction, Δdt i,(n,n+m) The single difference between epoch n+m and epoch n represents the receiver clock error, Δdt s (n,n+m) Represents the single difference between epoch n+m and epoch n of the satellite clock error, Δδ i,k,(n,n+m) The single difference between epoch n+m and epoch n of the carrier hardware delay of the receiver at frequency k, Δδ s,k n,n+m The single difference between epoch n+m and epoch n of the carrier hardware delay of the satellite at frequency k, ΔN k,(n,n+m) represents the single difference between epoch n+m and epoch n of the ambiguity of frequency point k, represents the single difference between epoch n+m and epoch n of the carrier observation noise, The single difference between epoch n+m and epoch n represents the tropospheric delay in the line of sight, Δd s,k (n,n+m) Represents the single difference between epoch n+m and epoch n of the pseudorange hardware delay of the satellite at frequency k.

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