CN114895336B - Forecasting method for observed value of reference station in GNSS real-time dynamic positioning system - Google Patents
Forecasting method for observed value of reference station in GNSS real-time dynamic positioning system Download PDFInfo
- Publication number
- CN114895336B CN114895336B CN202210813342.XA CN202210813342A CN114895336B CN 114895336 B CN114895336 B CN 114895336B CN 202210813342 A CN202210813342 A CN 202210813342A CN 114895336 B CN114895336 B CN 114895336B
- Authority
- CN
- China
- Prior art keywords
- reference station
- delay rate
- rate
- forecasting
- observation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000013277 forecasting method Methods 0.000 title claims abstract description 7
- 238000000034 method Methods 0.000 claims abstract description 35
- 239000005433 ionosphere Substances 0.000 claims abstract description 33
- 239000005436 troposphere Substances 0.000 claims abstract description 33
- 230000002159 abnormal effect Effects 0.000 claims abstract description 6
- 238000000819 phase cycle Methods 0.000 claims abstract description 6
- 238000005259 measurement Methods 0.000 claims description 9
- 238000012937 correction Methods 0.000 claims description 8
- 238000007781 pre-processing Methods 0.000 claims description 4
- 239000013598 vector Substances 0.000 claims description 4
- 241001061260 Emmelichthys struhsakeri Species 0.000 abstract description 4
- 230000005540 biological transmission Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010224 classification analysis Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012805 post-processing Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/35—Constructional details or hardware or software details of the signal processing chain
- G01S19/37—Hardware or software details of the signal processing chain
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/40—Correcting position, velocity or attitude
- G01S19/41—Differential correction, e.g. DGPS [differential GPS]
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/52—Determining velocity
Abstract
The invention provides a forecasting method of observed values of a reference station in a GNSS real-time dynamic positioning system, which belongs to the technical field of global satellite navigation systems, and is characterized in that historical observation data of a plurality of GNSS satellites provided by the reference station in the latest 2T time duration are collected, the observation data are preprocessed, abnormal values in the observation data are identified and removed, and phase cycle slip is detected and marked in a segmented manner; forecasting an observed value in the T duration according to the forecasted troposphere delay rate and ionosphere delay rate and positions and rates of a satellite and a reference station; and establishing a compensation model of the observation value prediction error by using the difference between the predicted data and the actually measured observation data, obtaining the compensation error through the compensation model, and applying the compensation error to the observation value prediction of the future T duration. The invention provides a method for forecasting with high precision by using historical observation data of a single reference station, and provides guarantee for RTK positioning of a rover user by using non-delay reference station data.
Description
Technical Field
The invention relates to the technical field of global satellite navigation systems, in particular to a forecasting method of an observed value of a reference station in a GNSS real-time dynamic positioning system.
Background
The position of any point location can be determined by utilizing a GNSS absolute point location technology only depending on single receiving equipment, but the positioning accuracy is limited by ephemeris error and pseudo range noise, so that the positioning accuracy is not high easily, and the accuracy of the positioning method is in a meter level. In order to improve the positioning accuracy, a Real-time Kinematic (RTK) positioning method is generally adopted, and since a GNSS observation error and an ephemeris error have correlation in a certain spatial range, the RTK positioning method obtains a centimeter-level positioning technical means by using the correlation and a double-difference method.
However, a user using the real-time dynamic positioning method needs to receive not only the ranging signal of the satellite but also the reference station observation information broadcasted from the service system by using the communication device, and the reference station observation data is inevitably affected by communication delay, receiver failure, and the like.
Aiming at the problems, generally, an asynchronous RTK means is adopted, namely outdated reference station observation data and current latest data of a rover station are directly utilized to form a baseline, and when the data delay is 30s, the positioning error of the method in the elevation direction can reach 30cm, the requirement of high-precision positioning still cannot be met, and the method is mainly used for post-processing.
The forecasting of the observed value of the reference station in the GNSS real-time dynamic positioning system mainly has the following two technical difficulties or problems:
firstly, how to comprehensively utilize the change rate of the carrier phase and the Doppler observation value among epochs to separate out the influence of orbit error, satellite clock error, ionosphere error rate and troposphere error rate on observation value prediction; and aiming at various time variation characteristics of error rate influencing the forecasting precision of the observed value, a corresponding error model is designed.
And secondly, evaluating the error of the historical forecast observation value in real time, and how to further optimize the forecast precision of the current observation value by utilizing the forecast error.
The two technical difficulties directly influence the forecasting precision of the RTK reference station observation value, further influence the use experience of the rover user on the RTK service, and are problems which need to be solved in the current RTK continuous stable high-precision service system.
Therefore, for a user with a very high real-time requirement, how to generate a high-precision observation data prediction value in a short period of several minutes by using the historical data of the reference station is a problem to be solved urgently by using the real-time dynamic positioning method to achieve the user positioning continuity and high-precision position information.
Disclosure of Invention
In view of the above, the present invention provides a method for predicting an observed value of a reference station in a GNSS real-time dynamic positioning system, and therefore, the present invention performs classification analysis on predictability and delay sensitivity of error sources, such as a satellite ephemeris error, an ionosphere difference rate, a troposphere difference rate, and the like, which affect the prediction of the GNSS observed value, and provides a method for performing high-precision prediction by using historical observation data of a single reference station, so as to provide a guarantee for a rover user to perform RTK positioning by using data of a non-delay reference station.
The technical scheme of the invention is realized as follows: a forecasting method for an observed value of a reference station in a GNSS real-time dynamic positioning system comprises the steps of collecting historical observation data of a plurality of GNSS satellites provided by the reference station within the latest 2T time, preprocessing the observation data, identifying abnormal values in the observation data, removing the abnormal values, detecting phase cycle slip and marking in a segmented mode;
the method is characterized in that: the observation data comprises pseudo range, phase, doppler and signal to noise ratio and accurate reference station coordinates;
determining a receiver clock error rate, a troposphere delay rate and an ionosphere delay rate;
modeling the residual errors of the troposphere delay rate and the ionosphere delay rate respectively, calculating the delay rate of the T duration of a forecast part, calculating the prior correction quantity of the troposphere delay rate and the ionosphere delay rate of the satellite-earth sight direction of the forecast part, and calculating the troposphere delay rate and the ionosphere delay rate of the forecast part by utilizing the prior correction quantity and the delay rate;
forecasting an observed value in the T duration according to the forecasted troposphere delay rate and ionosphere delay rate and positions and rates of a satellite and a reference station;
and establishing a compensation model of the observation value forecasting error by using the difference between the forecasted data and the actually measured observation data, obtaining the compensation error through the compensation model, and applying the compensation error to the observation value forecasting of the future T duration.
In addition to the above technical means, preferably, T is 10 seconds or more and 10 minutes or less.
On the basis of the technical scheme, preferably, a speed measurement observation equation is established by utilizing historical observation data of a plurality of satellites of the GNSS and the limit epoch difference observation value.
On the basis of the above technical solution, preferably, in the observation data within the T duration, the value of i is from-299 to 0, and the troposphere delay rate and the ionosphere delay rate time series
And
eliminating Chang Pianliang and linear components in troposphere delay rate and ionosphere delay rate by utilizing a linear model to obtain zero-mean-change troposphere and ionosphere delay rate residuals which are respectively recorded as
And
(ii) a And then, respectively adopting a first-order autoregressive model and a second-order autoregressive model to establish a fitting model for the convective stratum delay and the ionospheric delay rate residual.
On the basis of the technical scheme, preferably, the receiver clock difference rate is ignored, and the reference station phase observation value is forecasted epoch by epoch according to the calculated satellite rate, the satellite clock rate, the ionosphere delay rate and the troposphere delay rate, wherein the observation forecasting time is T.
On the basis of the above technical solution, preferably, on the premise of not interrupting the actual observation data, the prediction error is calculated by using the actual observation value and the prediction observation value.
On the basis of the above technical solution, preferably, when the actual observation data and the predicted observation data are two different reference stations, the two reference stations are subtracted at the same time to obtain an inter-station difference, at this time, the influence of the receiver clock error rate is eliminated, but the residual errors are still influenced by the ephemeris error and the atmospheric error, and the like, the residual errors within the time length T are fitted by using a quadratic polynomial model, the observation data prediction error is fitted by using a formula, and the prediction error of the next prediction arc segment is predicted by using the fitting model.
On the basis of the above technical solution, preferably, the above process is repeated until the ranging data of all satellites observable at the reference station are completely forecasted, the forecast data is output, and the forecast data of the next T duration is started when the next forecast time comes.
Compared with the prior art, the forecasting method of the observed value of the reference station in the GNSS real-time dynamic positioning system has the following beneficial effects:
(1) The method comprises the steps of obtaining high-precision inter-epoch observed value speed by utilizing a phase and Doppler observed value combination model, further obtaining estimated values of various error variations, establishing a targeted autoregressive fitting model by utilizing time characteristics of troposphere and ionosphere error variations, modeling prediction errors of historical observed values, further improving the prediction precision of a current prediction arc section, and finally solving short-term high-precision prediction of reference station observed data in an RTK service system;
(2) The RTK reference station continuously operates and stores historical observation data within the time length of not less than 2T, an observation value forecasting model can be effectively established, and high-precision short-term forecasting of observation data of the reference station is realized;
(3) By means of the GNSS speed measurement principle, various error rates are respectively measured by comprehensively utilizing phase and Doppler observed values, and a basis is provided for forecasting the observed value of the reference station;
(4) Respectively establishing fitting models of ionospheric difference rate and tropospheric error rate by adopting an autoregressive model, and realizing the prediction of irregular change parts in the tropospheric difference delay difference rate and the ionospheric difference delay rate;
and (4) performing prediction error modeling by using the measured data and the predicted value, and further improving the prediction precision of the observed value.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart illustrating a method for forecasting an observed value of a reference station in a GNSS real-time dynamic positioning system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1, a method for forecasting an observed value of a reference station in a GNSS real-time dynamic positioning system includes collecting historical observation data of multiple satellites of the GNSS provided by the reference station within a latest 2T duration, preprocessing the observation data, identifying and eliminating abnormal values in the observation data, and detecting and segmentally marking a phase cycle slip.
In a preferred embodiment, T is 10 seconds or more and 5 minutes or less, since the transmission of the observation data by the satellite requires time, and T is a minimum of 10 seconds for more accurate prediction of the observation value, and T is not more than 10 minutes due to an accumulated error in the transmission process, and it is found by experiment that T is preferably 5 minutes, and therefore T is defaulted to 5 minutes in the present embodiment.
The observation data comprises pseudo range, phase, doppler and signal to noise ratio and accurate reference station coordinates; determining a receiver clock error rate, a troposphere delay rate and an ionosphere delay rate; measuring speed by using phase and Doppler data, determining receiver clock error rate, troposphere and ionosphere delay rate, calculating fitting residual error of troposphere and ionosphere delay rate by using a second-order linear fitting model, respectively using first-order and second-order autoregressive models,
modeling a troposphere delay rate and an ionosphere delay rate residual error respectively, calculating the delay rate of the T duration of a forecast part, calculating the troposphere delay rate and the ionosphere delay rate prior correction quantity of the satellite-earth sight line direction of the forecast part by utilizing a publicly-obtained troposphere experience correction model and a quick ionosphere grid product, and calculating the troposphere delay rate and the ionosphere delay rate of the forecast part by utilizing the prior correction quantity and the delay rate; forecasting an observed value in the T duration according to the forecasted troposphere delay rate and ionosphere delay rate and positions and rates of a satellite and a reference station; and repeating the steps in the next T duration, establishing a compensation model of the observation value prediction error by using the difference between the predicted data and the actually-measured observation data, obtaining the compensation error through the compensation model, and applying the compensation error to the observation value prediction of the future T duration to realize the high-precision prediction of the observation value of the reference station.
The present embodiment assumes that a reference station can observe multiple satellites for a certain GNSS system and provides satellite, phase, doppler and snr observations, which assumption is very easy to satisfy in practice. Because the method is a basic requirement for the construction of the RTK reference station, the RTK reference station is generally built in a field with a wide view and less signal interference, as a preferred embodiment, the method performs data preprocessing on the latest historical observation data in the collected 2T time duration, uses the fixed satellite position and speed as an ultra-fast orbit and a broadcast ephemeris and the satellite clock error as a real-time clock error or a broadcast clock error, calculates the satellite-ground distance observed value and the theoretical value difference OMC, detects the pseudorange gross error based on the OMC, eliminates the pseudorange gross error, forms an inter-epoch difference by using the phase observed value, detects the phase cycle slip and marks the phase cycle slip.
As a preferred embodiment, since the phase observation value is easily affected by cycle slip to cause phase difference to generate jump, the velocity measurement observation equation is established by comprehensively using the doppler observation value and the phase epoch difference observation value as follows:
in formula (1):
j is the satellite number;
A speed measurement observation equation as shown in a formula (1) is established according to n observation satellites, wherein the positions and the speeds of the satellites are from broadcast ephemeris or real-time orbit clock error products received by a reference station, and estimated values of parameters to be estimated, including ionospheric delay difference rate, tropospheric delay difference rate and receiver clock error rate, are obtained based on the least square principle.
Modeling tropospheric delay difference rate and ionospheric delay difference rate, as a preferred embodiment, determining a time series of tropospheric delay difference rate and ionospheric delay difference rate
And
for the observation data in the time length of T, the value of i ranges from-299 to 0. Firstly, utilizing a linear model to eliminate Chang Pianliang and linear components in troposphere delay difference rate and ionosphere delay difference rate, obtaining zero-mean-value-variation troposphere delay difference rate and ionosphere delay difference rate residual errors which are respectively recorded as
And
(ii) a Then, a first-order autoregressive model and a second-order autoregressive model are respectively adopted to build a convective stratum delay and an ionospheric delay rate residual errorThe vertical fit model is as follows:
As a preferred embodiment, the reference station observation forecast is performed epoch by epoch using the satellite rate, the satellite clock rate, the ionosphere delay difference rate and the troposphere delay difference rate calculated in the above steps, with a forecast duration of T,
(3)
in formula (3):
Since the influence of the receiver clock difference rate on all satellites is the same, the technical scheme ignores the influence of the receiving clock difference rate, and the corresponding observation value forecast error can be absorbed by the user receiver clock difference rate, so that the formula (3)
The direct value is 0.
Observation prediction error compensation as a preferred embodiment, the prediction error is calculated using the measured value and the predicted value without interruption of the actual observation data of the reference station.
In formula (4):
If the actual observation value and the forecast observation value are regarded as two different observation stations, the two observation stations are subtracted at the same time to obtain the inter-station difference, at the moment, the influence of the clock error rate of the receiver is eliminated, but the difference is still influenced by residual errors such as ephemeris error and atmospheric error, and the residual errors can be well fitted by a quadratic polynomial model in the time length of T, so that the forecast error of the observation value is fitted by using the following formula, and the forecast error of the next forecast arc segment is forecasted by using the fitting model.
In formula (5):
Estimating model coefficients using the prediction error carry-over (5) calculated by equation (4)
，
And
and forecasting the forecasting error of the next forecasting arc segment by using the quadratic model, and using the forecasting error as a priori correction value of the next forecasting arc segment.
And repeating the steps until the distance measurement values of all the satellites observable by the reference station are completely forecasted, and outputting forecast data. And waiting for the next forecast time to come, and starting forecast data in the next T duration.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.
Claims (8)
1. A forecasting method of an observation value of a reference station in a GNSS real-time dynamic positioning system comprises the steps of collecting historical observation data of a plurality of GNSS satellites provided by the reference station within the latest 2T time, preprocessing the observation data, identifying abnormal values in the observation data, removing the abnormal values, detecting phase cycle slip and marking in a segmented mode;
the method is characterized in that: the observation data comprises pseudo range, phase, doppler, signal to noise ratio and accurate reference station coordinates;
determining a receiver clock error rate, a troposphere delay rate and an ionosphere delay rate;
modeling the residual errors of the troposphere delay rate and the ionosphere delay rate respectively, calculating the delay rate of the time length of a forecasting part T, calculating the prior correction quantity of the troposphere delay rate and the ionosphere delay rate of the satellite-ground sight direction of the forecasting part, and calculating the troposphere delay rate and the ionosphere delay rate of the forecasting part by using the prior correction quantity and the delay rate;
forecasting an observed value in the T duration according to the forecasted troposphere delay rate and ionosphere delay rate and positions and rates of a satellite and a reference station;
and establishing a compensation model of the observation value prediction error by using the difference between the predicted data and the actually measured observation data, obtaining the compensation error through the compensation model, and applying the compensation error to the observation value prediction of the future T duration.
2. The method as claimed in claim 1, wherein the method for forecasting the observed values of the reference station in the GNSS real-time dynamic positioning system comprises: t is not less than 10 seconds and not more than 10 minutes.
3. A method as claimed in claim 1 or 2, wherein the method for forecasting the observed value of the reference station in the GNSS real-time dynamic positioning system comprises: the method comprises the following steps of establishing a speed measurement observation equation by utilizing historical observation data of a plurality of satellites of the GNSS and a limit epoch difference observation value as follows:
j is the satellite number;
4. The method as claimed in claim 3, wherein the method for forecasting the observed values of the reference station in the GNSS real-time dynamic positioning system comprises: in the observation data in the T duration, i takes a value from-299 to 0, and the troposphere delay rate and the ionosphere delay rate time sequence
And
eliminating Chang Pianliang and linear components in troposphere delay rate and ionosphere delay rate by utilizing a linear model to obtain zero-mean-change troposphere and ionosphere delay rate residuals which are respectively recorded as
And
(ii) a Then, a first-order autoregressive model and a second-order autoregressive model are respectively adopted to establish a fitting model for the convective stratum delay and the ionospheric delay rate residual error as follows:
5. The method as claimed in claim 4, wherein the method for forecasting the observed value of the reference station in the GNSS real-time dynamic positioning system comprises: and ignoring the clock difference rate of the receiver, and forecasting the phase observed value of the reference station by epochs according to the satellite rate, the satellite clock rate, the ionosphere delay rate and the troposphere delay rate which are obtained by calculation, wherein the observation and forecasting duration is T.
6. The method as claimed in claim 5, wherein the method for forecasting the observed value of the reference station in the GNSS real-time dynamic positioning system comprises: and on the premise of not interrupting the actual observation data, calculating a forecast error by using the actual observation value and the forecast observation value.
7. The method as claimed in claim 6, wherein the method for forecasting the observed value of the reference station in the GNSS real-time dynamic positioning system comprises: when the actual observation data and the forecast observation data are two different reference stations, the actual observation data and the forecast observation data are subtracted at the same time to obtain the inter-station difference, the influence of the clock difference rate of the receiver is eliminated at the moment, but the actual observation data and the forecast observation data are still influenced by residual errors formed by ephemeris errors and atmospheric errors, the residual errors in the T duration are fitted by a quadratic polynomial model, the forecast errors of the observation data are fitted by a formula, and the forecast errors of the next forecast arc segment are forecast by the fitting model.
8. The method as claimed in claim 7, wherein the method for forecasting the observed value of the reference station in the GNSS real-time dynamic positioning system comprises: and repeating the process until the distance measurement data of all satellites observable by the reference station are completely forecasted, outputting forecast data, and starting forecast data of the next T duration when the next forecast time comes.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210813342.XA CN114895336B (en) | 2022-07-12 | 2022-07-12 | Forecasting method for observed value of reference station in GNSS real-time dynamic positioning system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210813342.XA CN114895336B (en) | 2022-07-12 | 2022-07-12 | Forecasting method for observed value of reference station in GNSS real-time dynamic positioning system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114895336A CN114895336A (en) | 2022-08-12 |
| CN114895336B true CN114895336B (en) | 2022-10-28 |
Family
ID=82729483
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210813342.XA Active CN114895336B (en) | 2022-07-12 | 2022-07-12 | Forecasting method for observed value of reference station in GNSS real-time dynamic positioning system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114895336B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115657448B (en) * | 2022-12-09 | 2023-03-28 | 电信科学技术第五研究所有限公司 | Frequency offset analysis method for clock discipline |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006010573A (en) * | 2004-06-28 | 2006-01-12 | Furuno Electric Co Ltd | Carrier smoothing positioning system |
| CN102498415B (en) * | 2009-09-19 | 2014-04-16 | 天宝导航有限公司 | Gnss signal processing with rover ambiguity fixing |
| CN103728876A (en) * | 2014-01-02 | 2014-04-16 | 东南大学 | Satellite clock error real-time estimation method based on area multi-reference-station joint resolving |
| CN104090280B (en) * | 2014-06-04 | 2015-08-26 | 郑州轻工业学院 | A kind of ionosphere delay correction forecasting procedure based on region CORS |
| CN104536023B (en) * | 2015-01-09 | 2017-05-03 | 山东天星北斗信息科技有限公司 | Non-delayed sub-meter differential positioning method of high and low frequency error shunting prediction |
| CN109001972B (en) * | 2018-08-13 | 2020-06-12 | 中国科学院国家授时中心 | Beidou wide-area time service system and method |
| CN109917424B (en) * | 2019-03-08 | 2020-06-19 | 山东科技大学 | Residual error correction method for troposphere delay in NWP (N-WP) inversion under multi-factor constraint |
| US10809388B1 (en) * | 2019-05-01 | 2020-10-20 | Swift Navigation, Inc. | Systems and methods for high-integrity satellite positioning |
| CN112686481A (en) * | 2021-03-22 | 2021-04-20 | 国能大渡河流域水电开发有限公司 | Runoff forecasting method and processor |
| CN113204042B (en) * | 2021-05-21 | 2022-11-18 | 北京交通大学 | Multi-constellation combined train positioning method based on precise single-point positioning |
-
2022
- 2022-07-12 CN CN202210813342.XA patent/CN114895336B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN114895336A (en) | 2022-08-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109709591B (en) | GNSS high-precision positioning method for intelligent terminal | |
| CN109001771B (en) | Navigation satellite and low-orbit satellite real-time clock error determining and forecasting method and system | |
| CN110231037B (en) | GNSS maneuvering satellite orbit determination method with additional clock error model constraint | |
| EP3035080B1 (en) | Navigation satellite system positioning involving the generation of correction information | |
| EP3130943B1 (en) | Navigation satellite system positioning involving the generation of tropospheric correction information | |
| RU2565386C2 (en) | Method, apparatus and system for determining position of object, having global navigation satellite system receiver, by processing non-differential data, similar to carrier phase measurements, and external data similar to ionospheric data | |
| Ge et al. | Resolution of GPS carrier-phase ambiguities in precise point positioning (PPP) with daily observations | |
| CN111983654B (en) | Method for constructing ionosphere phase scintillation factor in arctic region based on GNSS | |
| CN101346604A (en) | Computing long term orbit and clock models with variable time-horizons | |
| CN103344978A (en) | Area enhanced precision positioning service method suitable for large-scale users | |
| JP2007529010A (en) | A method of performing short-term backup two-frequency navigation when measurement data is not available at one of the two frequencies | |
| Wielgosz et al. | Troposphere modeling for precise GPS rapid static positioning in mountainous areas | |
| KR101175589B1 (en) | Detecting Method of GPS Clock Signal Jump Using Carrier Phase Measurements in Real -Time | |
| JP2013534623A (en) | Global Navigation Satellite System-System for measuring seismic motion or vibration of structures based on GNSS and / or pseudo satellites | |
| CN113325446B (en) | Multimode common-frequency GNSS carrier phase time transfer method and system | |
| CN114895336B (en) | Forecasting method for observed value of reference station in GNSS real-time dynamic positioning system | |
| CN115373005A (en) | High-precision product conversion method between satellite navigation signals | |
| CN104316943A (en) | Pseudo distance and Doppler combination differential positioning system and pseudo distance and Doppler combination differential positioning method | |
| CN115993617B (en) | GNSS system time deviation monitoring method | |
| CN114924295A (en) | Carrier phase smoothing pseudorange positioning method, device and storage medium | |
| CN114167457A (en) | GNSS system time deviation monitoring and evaluating method | |
| JP4861226B2 (en) | Inter-frequency bias estimation apparatus and inter-frequency bias estimation method | |
| US20110068979A1 (en) | Reducing complexity of calculations performed by a navigation system receiver | |
| CN114355410B (en) | Satellite navigation real-time precise single-point positioning system and method based on parallel computing | |
| CN115902968A (en) | PPP terminal positioning method based on Beidou third GEO broadcast enhancement information |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: A Prediction Method for Reference Station Observations in GNSS Real Time Dynamic Positioning System Effective date of registration: 20231007 Granted publication date: 20221028 Pledgee: Industrial Bank Limited by Share Ltd. Wuhan branch Pledgor: Kepler satellite technology (Wuhan) Co.,Ltd. Registration number: Y2023980060139 |
|
| PE01 | Entry into force of the registration of the contract for pledge of patent right |