CN117970389A - RAPPP observation model refinement method based on OSR correction enhancement - Google Patents
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- 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
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- 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
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- 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
- G01S19/42—Determining position
- G01S19/43—Determining position using carrier phase measurements, e.g. kinematic positioning; using long or short baseline interferometry
- G01S19/44—Carrier phase ambiguity resolution; Floating ambiguity; LAMBDA [Least-squares AMBiguity Decorrelation Adjustment] method
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Abstract
The invention belongs to the field of navigation systems of electronic information systems in ship system technology, and particularly relates to a RAPPP observation model refinement method based on OSR correction enhancement. The method classifies the OSR corrections of the server into four types according to the number of measuring stations of the OSR corrections of the server actually broadcast in the regional reference network, obtains the OSR corrections of the user side under different types by interpolating the OSR corrections of the different types, then builds a RAPPP observation model based on the OSR information corrections of different types at the user side, and builds a proper RAPPP random model by weighting satellite observation information corrected of different types.
Description
Technical Field
The invention belongs to the field of navigation systems of electronic information systems in ship system technology, and particularly relates to a RAPPP observation model refinement method based on OSR correction enhancement.
Background
The traditional measurement type receiver can be matched with a measurement type antenna to capture stable and reliable observation signals generally, so that higher positioning accuracy is obtained, and the method is widely applied to construction of large Cross-domain resource sharing CORS (Cross-Origin Resource Sharing) network stations. With the advent of the consumer-level global satellite navigation system GNSS (Global Navigation SATELLITE SYSTEM) receiver, the system has become the first choice for various popular navigation applications such as map navigation, smart phones and autopilot due to the characteristics of low price, modularization, low power consumption, etc., which creates very favorable conditions for the popularization and application of GNSS positioning technology in various industries. In recent years, the development of GNSS chip technology is mature, and the appearance of consumer dual-frequency receivers provides a possibility for further improving the positioning effect of consumer terminals. But some GPS satellites (e.g., block IIR) only support transmitting civil L1 frequencies, not civil L2 and L5. However, consumer receivers can only capture civil signals with single frequency due to limited frequency band supporting capability, and cannot support capture of multi-frequency signals like measurement type receivers. In addition, the baseband interference immunity of the consumer-level receiver is not strong, and the captured satellite signals may have larger observation noise, so that the GNSS observation data acquired in practical application is totally inferior to the measurement-type receiver in quantity and quality.
On the other hand, the traditional precise single-point positioning PPP (Precise Point Positioning) model can eliminate the influence of ionosphere delay errors based on the double-frequency observation data, but usually requires long initialization time, which is particularly difficult for consumer terminals with weak anti-interference and single-frequency observation data, and greatly restricts the accuracy and instantaneity of GNSS positioning. The regional enhanced precise single-point positioning RAPPP (Regional Augmentation Precise Point Positioning) technology utilizes non-poor regional enhanced information to enable a user to realize rapid fixation of single-difference ambiguity between stars in a real-time PPP processing mode, and obtains positioning precision consistent with the existing network differential positioning mode, so that the problems of accuracy and instantaneity caused by mixed use of consumption-level single/double data can be effectively relieved. RAPPP shows great potential in the application field of consumer-level equipment, but most RAPPP methods are mainly aimed at measuring type receivers, evaluation and improvement strategies aiming at consumer-level terminal positioning application are still to be further promoted, and the traditional RAPPP method is mainly applied by utilizing corrections generated by fixed solutions, and due to satellite common view problems and the use of partial ambiguity fixed strategies, satellite observation data corresponding to all clients are not provided with corresponding correction information, and under application environments such as complex and changeable urban canyons, the utilization rate of satellite resource corrections of the clients is improved as much as possible, so that the method is very important for guaranteeing the continuity and reliability of RAPPP positioning.
Disclosure of Invention
The invention aims to solve the problems in the prior art, and provides a RAPPP observation model refinement method based on observation domain OSR (Observation Space Representation) correction enhancement, which surrounds the generation and utilization aspects of RAPPP service end corrections.
In order to solve the technical problems, the invention adopts the following technical scheme.
A RAPPP observation model refinement method based on OSR correction enhancement comprises the following steps:
s1, solving a server OSR correction in a regional reference network based on a PPP mode;
s2, obtaining a user side OSR correction through interpolation in the step S1, further obtaining corrected satellite observation values, classifying according to the number of stations with fixed solutions at the server side, wherein the observation value corrected by the OSR correction at the server side without the fixed solution correction is Obs0, the observation value corrected by the OSR correction at the server side with only one fixed solution is Obs1, the observation value corrected by the OSR correction at the server side with only two fixed solutions is Obs2, and the observation value corrected by the OSR correction at the server side with three regional reference stations is Obs3;
S3, performing refined modeling processing on different types of observation information when a RAPPP observation model is constructed, aiming at observation values of the types of the Obs0, the Obs1 and the Obs2, only improving floating point solution precision, and not performing fixed processing on corresponding ambiguity Amb0, amb1 and Amb 2; aiming at the observation value of the Obs3 type, after RAPPP model is solved to obtain Amb3 floating ambiguity, fixing the observation value;
S4, when a RAPPP random model is constructed, weighting is carried out on satellite observation information Obs0, obs1, obs2 and Obs3 of different types;
s5, according to the construction of RAPPP observation models and random models in the S3 and S4 strategies, solving to obtain the position information of the user side observation station.
Further, in step 1, the regional reference network is a triangle network, and the total number of reference stations in the regional reference network is 3, where the generation of the OSR correction based on the server side of the fixed solution involves at least 1 reference station, and at most no more than 3 reference stations.
Further, in step 2, the interpolation method is a linear combination method or an inverse distance weighting method.
Further, the linear combination method expression is:
Wherein,
In the method, in the process of the invention,Subscribes to a reference station; /(I)Coordinates representing the reference station plane coordinate direction; /(I)For interpolation coefficients, (/ >)，/>) Representing the coordinates of the observation station at the user end.
Further, when the user terminal observation station is positioned in the network, the interpolation coefficient of the linear combination method is smaller than 1; when the client observation station is located outside the network, the interpolation coefficient is greater than 1.
Further, the inverse distance weighting expression is:
in the method, in the process of the invention, The geometric distance between the observation station and the reference station at the user end is set; /(I)Is the reciprocal of the distance; /(I)Is an interpolation coefficient; n is the number of reference stations,/>Is the sum of the inverse of the distances.
Further, the inverse distance weighted interpolation coefficient is less than 1.
Furthermore, the discrimination modes of the inside and the outside of the net adopt an included angle discrimination method, a ray discrimination method or an area discrimination method.
Further, in step S4, the weighting processing method for the satellite observation information Obs0, obs1, obs2 and Obs3 of different types is as follows: the observed value phase noise of the type Obs0 satellite is set to 0.15 meters, the observed value phase noise of the type Obs1 satellite is set to 0.15 meters, the observed value phase noise of the type Obs2 satellite is set to 0.05 meters, and the observed value phase noise of the type Obs3 satellite is set to 0.03 meters.
The beneficial effects of the invention are as follows:
The method classifies the OSR corrections of the server into four types according to the number of stations actually broadcasted in the regional reference network, obtains the OSR corrections of the user side under different types by interpolating the OSR corrections of the different types, then builds a RAPPP observation model based on the OSR information corrections of different types at the user side, and builds a proper RAPPP random model by weighting satellite observation information corrected of different types.
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In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart illustrating a process for processing RAPPP of the present invention based on GNSS observations of different types of consumer terminals.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention. The following examples are illustrative of the invention but are not intended to limit the scope of the invention.
The invention is described below in connection with the description of fig. 1.
OSR information is obtained by integrating various existing regional errors, and is modeled by aiming at more general errors on each frequency observation value of each satellite, and the values of the OSR information can be directly used for correcting the pseudo-range and the phase measurement value of a user. The OSR correction generation method can be roughly divided into two types: the first reference non-difference NRTK (Undifferenced Network REAL TIME KINEMATIC) technology is a correction generation strategy, and fixed double-difference ambiguity can be decomposed into non-difference ambiguities on different frequencies of different satellites through reference conversion, so that correction information of a non-difference observation value is obtained; second, the non-difference observed value correction information based on the non-difference ambiguity resolution is directly provided based on the PPP mode. The method of the present invention is based on PPP mode.
Compared with the measuring type GNSS receiver, the consumer level receiver has a different baseband processing scheme, and meanwhile, the limitation of the signal frequency band supporting capability and the large observation noise of the consumer level receiver lead the GNSS observation data acquired in practical application to be inferior to the measuring type receiver in the quantity and quality as a whole. Meanwhile, although the RAPPP server scheme has guaranteed the broadcasting of enough server OSR correction as much as possible, due to the satellite common view problem and the use of the partial ambiguity fixing strategy, not all the satellite observation data corresponding to the user side have corresponding server OSR correction information.
According to the invention, when the server OSR correction solution is processed, the server broadcasts the server OSR correction information of the satellite based on the PPP ambiguity fixed solution, and also broadcasts the server OSR correction information of the satellite based on the PPP ambiguity floating solution (such correction information only aims at satellite observation data of PPP ambiguity fixed failure), thereby being beneficial to ensuring the consistency of the number of reference stations in the interpolation process.
In order to describe the availability of the OSR correction of the server side in the observation value of the user side, the invention is based on the reference station in the regional reference network，/>For reference station subscript,/>) The number of stations actually broadcasting the OSR correction (server OSRi) at the server is classified. Considering that the use of the Delaunay triangulation network is generally adopted when the CORS is stably utilized, the total number of reference stations in the regional reference network is 3, and at least 1 reference station is involved once the server OSR correction based on a fixed solution is generated, and at most 3 reference stations are not involved.
Therefore, the OSR correction of the user side is obtained through interpolation, the corrected satellite observation value is obtained, the satellite observation value is classified according to the number of fixed solution stations at the server side, the observation value corrected by the OSR correction of the server side without fixed solution correction is ob 0, the observation value corrected by the OSR correction of the server side with only one fixed solution is ob 1, the observation value corrected by the OSR correction of the server side with only two fixed solutions is ob 2, and the observation value corrected by the OSR correction of the server side with three fixed solutions of the regional reference station is ob 3.
According to the above observation classification method, the present invention focuses on how to fully use the Obs0, obs1 and Obs2 observations to assist the consumer level terminal RAPPP in locating. The correction modeling mainly takes distance-related error correction as a core, and usually, a reference station correction closer to a user station is selected for interpolation, and the correction accuracy obtained after interpolation is related to the selection of an interpolation model besides the degree of density of the reference station. While it is critical to assess the efficiency of an interpolation model that, in addition to the space in which the interpolation model can accurately reflect GNSS errors, the controllability of systematic errors and corrective noise of the interpolation model should be maintained. The invention mainly selects the improved linear combination method (Modified Linear Combination Method, MLCM) and inverse Distance weighting method (Distance-based Linear Interpolation Method, DIM). The MLCM expression is shown in formulas (1) and (2):
(1)
Wherein,
(2)
In the method, in the process of the invention,Subscribes to a reference station; /(I)Coordinates representing the reference station plane coordinate direction; /(I)Is an interpolation coefficient, and belongs to a two-dimensional linear difference result. (/ >，/>) Representing the coordinates of the observation station at the user end.
The DIM interpolation model expression is formula (3):
(3)
in the method, in the process of the invention, The geometric distance between the observation station and the reference station at the user end is set; /(I)Is the reciprocal of the distance; interpolation coefficient/>For the one-dimensional linear difference result, n is the number of reference stations,/>Is the sum of the inverse of the distances. As can be seen from the above formula, for the MLCM interpolation model, when the user side observation station, i.e. the user is located in the network, all interpolation coefficients are smaller than 1, and the effects of atmospheric errors, multipath errors, etc. will be weakened in the interpolation process; when the customer premise observation station, i.e. the customer is located outside the network, the interpolation coefficient may be greater than 1, the noise impact will be amplified, and the DIM model interpolation coefficient is less than 1 in any scenario. The discrimination modes of the inside and the outside of the net can be judged by adopting the modes of an included angle discrimination method, a ray discrimination method, an area discrimination method, a discrimination method and the like.
When RAPPP observation models are constructed, carrying out refined modeling processing on different types of observation information, aiming at observation values of the types of the Obs0, the Obs1 and the Obs2, only being used for improving the precision of RAPPP floating point solutions, and not carrying out fixed processing on corresponding ambiguities Amb0, amb1 and Amb 2; for the observation value of the Obs3 type, after RAPPP model is solved to obtain Amb3 floating ambiguity, the observation value is subjected to fixed processing.
Thus, for the Obs2 observations, consider that only two reference stations' server OSR information is available. For the Obs0 observation value, because there is no server OSR information generated by the server based on the fixed solution, the atmospheric delay information and the satellite phase fractional deviation still need to be estimated by using a standard PPP model, and considering that the convergence time of the standard PPP positioning mode is generally longer, and the type conversion of the satellite observation value between the front epoch and the back epoch in the dynamic experiment is uncontrollable, at this time, the introduction of the Obs0 observation value data may not effectively improve the positioning performance of the traditional RAPPP scheme. In the tested regional triangle network, the phase observation type in Obs0 is only defined as an observation value corrected by server OSR information based on ambiguity floating solution broadcast by three regional reference stations. For the Obs1 observation value, the essence is a PPP technology enhanced by a single station, the action effect is similar to that of a differential positioning technology, the inter-station distance is mainly depended, for a small-scale triangle network (within 20 km), the client correction can be directly processed by using the server OSR correction value of a fixed reference station, for the triangular network with larger distance between stations, the correction can adopt a strategy similar to Obs0, and the server OSR information based on the ambiguity floating node, which is broadcast by two regional reference stations, is introduced for interpolation correction. At this time, the same observation model can be used for the observation values of Obs0, obs1, obs2, and Obs 3.
The correction numbers in the invention belong to comprehensive error correction information based on the OSR form of the server, and can be directly corrected on the pseudo-range and carrier phase observation values to eliminate the influence of the troposphere unmodeled error, the ionosphere delay error and the satellite-side pseudo-range and phase hardware delay (or correction based on related deviation products). Meanwhile, when the service side and the user side track are consistent with the clock product, the influence of satellite track and clock error is eliminated. Only the information such as position parameters, receiver clock errors, non-differential ambiguity parameters and the like are left in the non-differential observation equation based on the PPP mode, and the pseudo-range and carrier phase observation equation is solved through the existing observation model and the random model.
The receiver pseudorange hardware delay will be absorbed by the receiver clock difference; in addition to the position parameters, the clock error parameters and the ambiguity parameters are estimated to be the biased estimated values with certain bias terms, and in order to ensure the integer predictability of the ambiguity, the hardware delay influence on the satellite end and the receiver end can be eliminated by constructing a single-difference ambiguity form during the ambiguity processing. In addition, the atmospheric delay error can neglect the influence of the atmospheric delay error in the small-scale area with small fluctuation of the topography, otherwise, the atmospheric delay error needs to be estimated by setting a proper prior variance of atmospheric parameters according to the scale of a reference network used and the fluctuation of the topography thereof.
FIG. 1 depicts a RAPPP process flow based on different types of GNSS observations at the consumer level. In consideration of the fact that the introduction of the observed values of all satellites is not conducive to the solution of the final PPP ambiguity fixing solution, the method and the device perform reasonable weighting on the observed values of different types during actual positioning processing, and the observed values of the satellites of the types of the Obs0, the Obs1 and the Obs2 are low in weight, the noise setting threshold value during random modeling is large, and the method and the device are only used for improving the overall ambiguity fixing solution precision, and do not perform fixing processing on the corresponding ambiguities (Amb 0, amb1 and Amb 2). Therefore, the RAPPP terminal determines whether to fix the ambiguity according to the Obs3 type observed value standard, and only if the Obs3 observed value is provided and the Amb3 floating solution precision and variance reach the fixed conditions, the terminal tries to fix Amb 3. When the RAPPP random model is constructed, when the observed value information of different types of satellites exists, the observed value phase noise of the satellites of the types of the Obs0, the Obs1, the Obs2 and the Obs3 can be respectively set to be (0.15,0.15,0.05,0.03) meters, and the pseudo-range observed value noise setting is the same and kept at an empirical value setting of 3 meters.
The method classifies OSR corrections of a server into four types according to the number of OSR correction measuring stations actually broadcast in a regional reference network, obtains OSR corrections of a user side under different types by interpolating the OSR corrections of different types, then builds a RAPPP observation model based on OSR information correction of different types at the user side, and builds a proper RAPPP random model by weighting satellite observation information after correction of different types.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications could be made by those skilled in the art without departing from the principles of the present invention and should also be considered as being within the scope of the invention.
Claims (9)
1. The RAPPP observation model refinement method based on OSR correction enhancement is characterized by comprising the following steps:
s1, solving a server OSR correction in a regional reference network based on a PPP mode;
s2, obtaining a user side OSR correction through interpolation in the step S1, further obtaining corrected satellite observation values, classifying according to the number of stations with fixed solutions at the server side, wherein the observation value corrected by the OSR correction at the server side without the fixed solution correction is Obs0, the observation value corrected by the OSR correction at the server side with only one fixed solution is Obs1, the observation value corrected by the OSR correction at the server side with only two fixed solutions is Obs2, and the observation value corrected by the OSR correction at the server side with three regional reference stations is Obs3;
S3, performing refined modeling processing on different types of observation information when a RAPPP observation model is constructed, aiming at observation values of the types of the Obs0, the Obs1 and the Obs2, only improving the precision of RAPPP floating point solution, and not performing fixed processing on corresponding ambiguities Amb0, amb1 and Amb 2; aiming at the observation value of the Obs3 type, after RAPPP model is solved to obtain Amb3 floating ambiguity, fixing the observation value;
S4, when a RAPPP random model is constructed, weighting is carried out on satellite observation information Obs0, obs1, obs2 and Obs3 of different types;
s5, according to the construction of RAPPP observation models and random models in the S3 and S4 strategies, solving to obtain the position information of the user side observation station.
2. The method for refining an observation model RAPPP based on OSR correction enhancement according to claim 1, wherein in step1, the regional reference network is a triangle network, the total number of reference stations in the regional reference network is 3, and the server OSR correction generation based on a fixed solution involves at least 1 reference station and at most 3 reference stations.
3. The OSR correction enhancement-based RAPPP observation model refinement method according to claim 1, wherein in step 2, the interpolation method is a linear combination method or an inverse distance weighting method.
4. The OSR correction enhancement-based RAPPP observation model refinement method of claim 3, wherein the linear combination method expression is:
Wherein,
In the method, in the process of the invention,Subscribes to a reference station; /(I)Coordinates representing the reference station plane coordinate direction; /(I)For interpolation coefficients, (/ >)，) Representing the coordinates of the observation station at the user end.
5. The method for refining an OSR-correction-enhancement-based RAPPP observation model according to claim 4, wherein the interpolation coefficient is less than 1 when the client observation station is located in the network; when the client observation station is located outside the network, the interpolation coefficient is greater than 1.
6. The OSR correction enhancement-based RAPPP observation model refinement method of claim 3, wherein the inverse distance weighting expression is:
in the method, in the process of the invention, Subscribes to a reference station; /(I)The geometric distance between the observation station and the reference station at the user end is set; /(I)Is the reciprocal of the distance; /(I)Is an interpolation coefficient; n is the number of reference stations,/>Is the sum of the inverse of the distances.
7. The OSR correction enhancement-based RAPPP observation model refinement method of claim 6, wherein the inverse distance weighting interpolation factor is less than 1.
8. The method for refining an OSR-correction-enhancement-based RAPPP observation model according to claim 5, wherein the in-network and out-of-network discrimination methods are included angle discrimination, ray discrimination or area discrimination.
9. The method for refining an observation model RAPPP based on OSR correction enhancement according to claim 1, wherein in step S4, the weighting processing method for different types of satellite observation information Obs0, obs1, obs2 and Obs3 is as follows: the observed value phase noise of the type Obs0 satellite is set to 0.15 meters, the observed value phase noise of the type Obs1 satellite is set to 0.15 meters, the observed value phase noise of the type Obs2 satellite is set to 0.05 meters, and the observed value phase noise of the type Obs3 satellite is set to 0.03 meters.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20210072407A1 (en) * | 2019-09-10 | 2021-03-11 | Trimble Inc. | Protection level generation methods and systems for applications using navigation satellite system (nss) observations |
| CN112711048A (en) * | 2020-12-15 | 2021-04-27 | 中山大学 | SSR transmission method and high-precision positioning system based on Beidou third RDSS short message |
| CN112835082A (en) * | 2021-01-05 | 2021-05-25 | 广州星际互联科技有限公司 | GNSS area enhanced ionosphere and troposphere atmospheric product quality index calculation method, electronic equipment and storage medium |
| CN114355395A (en) * | 2021-12-16 | 2022-04-15 | 浙江时空道宇科技有限公司 | Terminal positioning method, device, equipment and storage medium |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20210072407A1 (en) * | 2019-09-10 | 2021-03-11 | Trimble Inc. | Protection level generation methods and systems for applications using navigation satellite system (nss) observations |
| CN112711048A (en) * | 2020-12-15 | 2021-04-27 | 中山大学 | SSR transmission method and high-precision positioning system based on Beidou third RDSS short message |
| CN112835082A (en) * | 2021-01-05 | 2021-05-25 | 广州星际互联科技有限公司 | GNSS area enhanced ionosphere and troposphere atmospheric product quality index calculation method, electronic equipment and storage medium |
| CN114355395A (en) * | 2021-12-16 | 2022-04-15 | 浙江时空道宇科技有限公司 | Terminal positioning method, device, equipment and storage medium |
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