CN112394376B - Large-scale GNSS network observation data non-differential whole network parallel processing method - Google Patents
Large-scale GNSS network observation data non-differential whole network parallel processing method Download PDFInfo
- Publication number
- CN112394376B CN112394376B CN202011310618.XA CN202011310618A CN112394376B CN 112394376 B CN112394376 B CN 112394376B CN 202011310618 A CN202011310618 A CN 202011310618A CN 112394376 B CN112394376 B CN 112394376B
- Authority
- CN
- China
- Prior art keywords
- parallel
- differential
- network
- station
- satellite
- 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
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
- G01S19/01—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
- G01S19/33—Multimode operation in different systems which transmit time stamped messages, e.g. GPS/GLONASS
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The invention relates to a non-differential whole network parallel processing method for large-scale GNSS network observation data, in particular to a non-differential whole network parallel computing method for large-scale GNSS network observation data based on a bottom multi-core parallel computing technology. Firstly, screening a certain number of stations from a large-scale GNSS network as core stations, estimating satellite orbits and satellite clock errors in parallel, performing parallel non-differential solution calculation between stations, and estimating decimal phase deviation of wide lanes and narrow lanes of a satellite end in parallel; secondly, carrying out parallel calculation of non-differential floating solutions of the inter-station parallel to other stations in the GNSS network, and parallelly fixing the non-differential ambiguity of all stations by using the decimal phase deviation of the wide lane and the narrow lane estimated by the core station, and parallelly generating carrier distances of all stations in the GNSS network; and finally, carrying out integral network unified calculation under a non-differential mode by using the pseudo-range observation value and the carrier distance, wherein parameters to be estimated comprise station coordinates, satellite orbits, receiver and satellite clock errors, double-differential ambiguity, troposphere delay, decimal phase deviation, earth rotation parameters and the like.
Description
Technical Field
The invention relates to a non-differential whole network parallel processing method for large-scale GNSS network observation data, and belongs to the technical field of large-scale GNSS network observation data processing.
Background
As an important ground infrastructure for countries, a GNSS (Global Navigation Satellite System) reference station network consisting of hundreds or even thousands of continuously operating reference stations has been established in many countries and regions. Large-scale global GNSS network data provides abundant computing resources, but also presents a significant challenge in data processing. The time consumption for understanding the data processing is increased in geometric progression along with the expansion of the scale of the GNSS network, the global GNSS large network data processing faces the difficult problems of low calculation efficiency and incapability of resolving, and a corresponding high-efficiency data processing model and method are urgently needed to serve as a support, so that the advantages of the large network are fully exerted, and the precision, reliability and instantaneity of the large network service are improved. The search for efficient and rapid processing of large-scale GNSS network data is one of the current hot spot directions, and is receiving more and more attention and importance.
Extensive attention and research is paid to the aspects of improving models and algorithms, applying parallel computing technology and the like by research institutions and researchers at home and abroad, and published documents mainly comprise: the method comprises the steps of (1) performing GNSS data parallel processing research under the multi-core environment of (Global GPS reference frame solutions of unlimited size) and (Parallel resolution of large-scale GNSS network un-difference ambiguity) of (Advances in Space Research), A new data processing strategy for huge GNSS global networks and An enhanced strategy for GNSS data processing of massive networks of (Journal of Geodesy) of (mapping school report) of China, (2) performing GNSS large-network double-difference model parallel fast resolving method of (GNSS large-network) and (PPP network resolving UPD ambiguity fixed base station-free differential large-scale CORS station whole network fast accurate resolving of, (3) performing (global measurement and earth dynamics) of (a GNSS large-network data fast efficient processing strategy).
In the method for calculating the non-differential whole network of the large-scale GNSS network, the existing solution adopts a serial calculation method to synchronously process the data of all reference stations in the large-scale GNSS network, and sequentially and serially estimate satellite orbits and clock errors, serially estimate decimal phase deviations of wide lanes and narrow lanes, serially fix non-differential ambiguity, serially generate carrier distances and serially calculate the whole network. In parallel solution to large-scale GNSS network observation data, the existing research is focused on the aspects of baseline vector adjustment, double-difference baseline solution, single-station non-difference floating solution, fixed solution calculation and the like.
Disclosure of Invention
The invention aims to provide a large-scale GNSS network observation data non-difference whole network parallel processing method, which is used for solving the problem that the current GNSS network scale is continuously enlarged to cause the time consumption of data understanding to be exponentially increased.
In order to achieve the above object, the present invention provides a method comprising:
the invention discloses a large-scale GNSS network observation data non-difference whole network parallel processing method, which comprises the following steps:
1) Selecting a set number of measuring stations as core stations, and parallelly resolving satellite orbit, satellite clock error and earth rotation parameters according to the observation data of the core stations;
2) Estimating the wide lane decimal phase deviation and the narrow lane decimal phase deviation of a satellite end by utilizing satellite orbit, satellite clock error and earth rotation parameters;
3) Constructing non-differential ambiguity for each core station, and fixing ionosphere-free combined ambiguity by using the rounded wide lane decimal phase deviation and narrow lane decimal phase deviation;
for other stations except the core station, carrying out inter-station parallel non-difference fixed solution calculation by utilizing satellite orbit, satellite clock error, earth rotation parameters, wide lane decimal phase deviation and narrow lane decimal phase deviation of a satellite end, constructing non-difference ambiguity for each other station, and fixing ionosphere-free combined ambiguity;
4) Generating carrier distance observation values of the corresponding stations in parallel according to the carrier phase observation values of each station and the fixed ionosphere-free combined ambiguity;
5) And combining the pseudo-range observation value and the carrier distance observation value of each measuring station, constructing a normal equation in parallel, carrying out parallel inversion on the normal equation, and carrying out whole-network parallel calculation based on a non-difference mode.
The invention provides a novel method for multi-core parallel processing of the non-poor whole network of the large-scale GNSS network observation data, which realizes the parallel execution of a plurality of links of the calculation of the non-poor whole network of the large-scale GNSS network observation data, improves the utilization rate of the calculation on a multi-core platform and improves the calculation efficiency of the whole network.
Further, the core stations are not less than 100 stations distributed uniformly around the world.
In step 1), the satellite orbit and the earth rotation parameters are estimated through Kalman filtering in a non-differential mode, a non-differential observation equation is constructed by taking a single core station as parallel granularity, and constraint is carried out on the non-differential observation equation by constructing double-differential ambiguity and fixing the ambiguity.
Further, in step 2), the satellite end wide lane decimal phase deviation and the narrow lane decimal phase deviation are estimated in parallel by performing inter-station parallel non-differential floating solution calculation on the core station.
Further, in step 4), the carrier phase observation value is corrected by using the fixed ionosphere-free combined ambiguity and the antenna phase wrapping effect to obtain a carrier distance observation value.
Further, in step 5), the pseudorange hardware delay of the satellite end is subtracted from the pseudorange observed value.
Further, in step 5), the method for performing the whole network parallel calculation based on the non-difference mode includes: taking the pseudo-range observation value and the carrier distance observation value as observed quantities; constructing a normal equation of each epoch in parallel by taking the epoch as granularity; solving a normal equation by adopting a parameter elimination algorithm based on recursive least square; the normal equation is inverted by matrix block inversion and Cholesky parallel decomposition.
Currently, the requirements of the unified solution of the non-poor whole network of the large-scale GNSS network observation data on the real-time performance and the calculation efficiency are higher and higher, in order to shorten the time of calculating the non-poor whole network of the large-scale GNSS network observation data and improve the timeliness of calculating the non-poor whole network of the large-scale GNSS network observation data, a multi-core parallel calculation technology-parallel task library (task parallel library, TPL) is adopted to design a method for calculating the non-poor whole network of the large-scale GNSS network observation data under a multi-core platform, and the core station parallel solution of satellite orbit, satellite clock error and earth rotation parameters is realized; the core station estimates the decimal phase deviation of the wide lane and the decimal phase deviation of the narrow lane in parallel; and the whole network non-difference ambiguity is fixed in parallel, the whole network carrier distance is generated in parallel, and the multi-core parallel execution of the whole network calculation is performed in a non-difference mode.
The method is simple and easy to operate, improves the utilization rate of the multi-core platform, shortens the time for calculating the non-bad whole network of the large-scale GNSS network observation data, and improves the efficiency for calculating the non-bad whole network of the large-scale GNSS network observation data.
Further, parameters to be estimated obtained by parallel calculation of the whole network include: station coordinates, satellite orbit, double-difference ambiguity, tropospheric delay, receiver and satellite clock bias, fractional phase bias and earth rotation parameters.
Further, for the non-ionospheric combined ambiguity of which each station is not fixed in the step 3), constructing a station star double-difference non-ionospheric combined ambiguity, and fixing the non-ionospheric combined ambiguity.
For the ambiguity that each station cannot be fixed through the non-differential mode, further try to fix through the double-differential mode, and improve the success rate of the ambiguity fixation, so that more observation data can be utilized during the whole network resolving, and further improve the accuracy and reliability of the parameter resolving to be estimated.
Drawings
FIG. 1 is a flow chart of non-differential whole network parallel computation of observation data of a large-scale GNSS network under a multi-core platform of the invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings.
As shown in fig. 1, for the observation data of a large-scale GNSS network reference station (station-finding), the method of the present invention discloses a flow of non-differential whole network parallel processing of the large-scale GNSS network observation data under a multi-core platform, which comprises the following steps:
step 1: screening a certain number of measuring stations which are uniformly distributed worldwide in a large-scale GNSS network as core stations, and calculating satellite orbit, satellite clock error and earth rotation parameters in parallel based on task parallelism and data parallelism;
step 2: carrying out inter-station parallel non-differential floating solution calculation on a core station by utilizing satellite orbit, satellite clock error and earth rotation parameters, and estimating the wide lane decimal phase deviation and the narrow lane decimal phase deviation of a satellite end in parallel;
step 3: performing parallel calculation of non-differential floating solutions of inter-station parallelism on other stations in the large-scale GNSS network, and fixing the non-differential ambiguity for each station by using the decimal phase deviation of the wide lane and the narrow lane estimated by the core station to obtain the fixed non-differential ionosphere-free combined ambiguity;
step 4: the carrier phase observation values of all stations in the large-scale GNSS network are deducted based on a two-stage parallel mechanism between stations and between satellites, the corresponding fixed non-differential ionosphere-free combined ambiguity is converted into a carrier distance observation value without ambiguity, and the pseudo-range hardware delay of a satellite end is deducted for the pseudo-range observation values of all stations;
step 5: and (3) combining the pseudo-range and the carrier distance obtained in the step (4), constructing a normal equation in parallel, carrying out parallel inversion on the normal equation, and carrying out whole network parallel calculation based on a non-difference mode.
In the step 1, the number of core stations uniformly distributed worldwide is not less than 100, the strategy of satellite orbit, satellite clock error and earth rotation parameter estimation is Kalman filtering under a non-differential mode, a non-differential observation equation is constructed by taking a single station as parallel granularity, and the non-differential equation is constrained by constructing double-differential ambiguity and fixing.
In the step 3), the method for parallel computation of the non-difference fixed solution comprises the following steps:
aiming at a core station, the calculation of non-differential solution is not needed, the wide lane decimal phase deviation and the narrow lane decimal phase deviation obtained in the step 2) are directly utilized, the wide lane ambiguity and the narrow lane ambiguity are obtained after rounding, and the fixed non-differential ionosphere-free combined ambiguity is obtained by the calculation of the fixed wide lane ambiguity and the narrow lane ambiguity;
and for other stations in the GNSS network, performing parallel non-difference fixed solution calculation among the stations by using satellite orbit, satellite clock error, earth rotation parameters, wide lane decimal phase deviation and narrow lane decimal phase deviation of a satellite end.
In step 4), the carrier distance is generated by utilizing the non-differential ionosphere-free combined ambiguity fixed in step 3) and the antenna phase winding effect obtained by model calculation, and the carrier distance is obtained by correcting the ionosphere-free combined carrier phase observed quantity
In the method, in the process of the invention,
for carrier distance, L is the original ionosphere-free combined carrier phase observation, λ is the ionosphere-free combined wavelength, N is the fixed non-differential ionosphere-free combined ambiguity, and ζ is the antenna phase wrapping effect.
For the ionosphere-free combined ambiguity without non-differential fixation of each measuring station in the step 3), further building the ionosphere-free combined ambiguity with double differential of station stars, trying to fix the double differential ambiguity, effectively improving the success rate of ambiguity fixation, increasing the effective carrier distance observation value which can be used for the whole network parallel calculation in the step 5), and further improving the precision and reliability of parameter calculation to be estimated.
In the step 5), the whole network parallel resolving method under the non-difference mode comprises the following steps:
firstly, taking the obtained pseudo-range and carrier distance as observables, constructing a normal equation of each epoch based on data parallelism by taking the epoch as granularity when constructing the normal equation, and solving the normal equation by adopting a parameter elimination algorithm based on recursive least square; when the normal equation is inverted, matrix block inversion, cholesky parallel decomposition method and the like can be adopted.
Parameters to be estimated for the whole network solution in the non-differential mode include station coordinates, satellite orbits, double-differential ambiguities, troposphere delays, receiver and satellite clock differences, decimal phase deviations, earth rotation parameters and the like.
The observations of the present invention include pseudorange and carrier phase observations for multiple frequencies of GPS, galileo, BDS and GLONASS systems.
In the foregoing, the protection scope of the present invention is not limited to the preferred embodiments of the present invention, and any simple changes or equivalent substitutions of the technical solutions that can be obviously obtained by those skilled in the art within the technical scope of the present invention disclosed in the present invention fall within the protection scope of the present invention.
The method for parallel processing of the non-bad whole network of the large-scale GNSS network observation data is actually used for realizing multi-core parallel execution of a plurality of links of calculation of the non-bad whole network of the large-scale GNSS network observation data. The experiment utilizes the observation data of 500 GNSS reference stations distributed globally, and adopts three schemes of dual-core, four-core and six-core parallel respectively. Through testing, compared with the traditional single-core serial method, the method for processing the non-difference whole network multi-core parallel of the large-scale GNSS observation data provided by the invention has the advantages that the calculation time is greatly shortened, the calculation efficiency is improved, and the acceleration ratio of dual-core, four-core and six-core parallel is respectively 1.60, 3.05 and 4.50 times. The effect of practical application is closely related to the performance of a hardware system, the size of the GNSS network scale, the quality of observed data and the like.
Compared with the prior art, the invention has the following outstanding beneficial technical effects:
(1) And the timeliness of the non-poor whole network calculation of the large-scale GNSS network observation data is improved.
The invention provides a method for parallel calculation of a large-scale GNSS network observation data non-difference whole network, which realizes parallel calculation of satellite orbit, satellite clock difference and earth rotation parameters of a certain number of core stations which are uniformly distributed in a GNSS network under a multi-core platform; the core station estimates the decimal phase deviation of the wide lane and the narrow lane in parallel; and the whole network non-difference ambiguity is fixed in parallel, the whole network carrier distance is generated in parallel, and the whole network calculation is performed in parallel under the non-difference mode, so that the time for calculating the whole network of the large-scale GNSS network observation data is shortened, and the calculation efficiency is improved.
(2) Is easy to expand.
The method provided by the invention has wider applicability and stronger expansibility, is suitable for processing the multi-frequency observation values of various satellite navigation systems, and is used for carrying out whole network calculation based on a non-difference mode. The method can be used for carrying out whole network parallel computation under the non-differential mode on the multi-frequency observation data of the selected satellite navigation system, is not only limited to the 2 frequencies, but also applicable to 3, 4 and 5 frequencies, is effectively applied to the technical field of geodetics and measurement engineering in the discipline of surveying and mapping science and technology, and realizes the non-differential whole network parallel computation of the observation data of the large-scale GNSS network.
Claims (8)
1. A large-scale GNSS network observation data non-differential whole network parallel processing method is characterized by comprising the following steps:
1) Selecting a set number of measuring stations as core stations, and parallelly resolving satellite orbit, satellite clock error and earth rotation parameters according to the observation data of the core stations; estimating satellite orbit and earth rotation parameters through Kalman filtering in a non-differential mode, constructing a non-differential observation equation by taking a single core station as parallel granularity, and restraining the non-differential observation equation by constructing double-differential ambiguity and fixing the ambiguity;
2) Estimating the wide lane decimal phase deviation and the narrow lane decimal phase deviation of a satellite end by utilizing satellite orbit, satellite clock error and earth rotation parameters;
3) Constructing non-differential ambiguity for each core station, and fixing ionosphere-free combined ambiguity by using the rounded wide lane decimal phase deviation and narrow lane decimal phase deviation;
for other stations except the core station, carrying out inter-station parallel non-difference fixed solution calculation by utilizing satellite orbit, satellite clock error, earth rotation parameters, wide lane decimal phase deviation and narrow lane decimal phase deviation of a satellite end, constructing non-difference ambiguity for each other station, and fixing ionosphere-free combined ambiguity;
4) Generating carrier distance observation values of the corresponding stations in parallel according to the carrier phase observation values of each station and the fixed ionosphere-free combined ambiguity;
5) And combining the pseudo-range observation value and the carrier distance observation value of each measuring station, constructing a normal equation in parallel, carrying out parallel inversion on the normal equation, and carrying out whole-network parallel calculation based on a non-difference mode.
2. The method for parallel processing of non-differential whole network of large-scale GNSS network observations according to claim 1, wherein the core stations are not less than 100 stations distributed uniformly around the world.
3. The method for non-differential whole network parallel processing of large-scale GNSS network observation data according to claim 1, wherein in the step 2), the satellite-side wide lane decimal phase deviation and the satellite-side narrow lane decimal phase deviation are estimated in parallel by performing inter-station parallel non-differential solution calculation on a core station.
4. The method according to claim 1, wherein in step 4), the carrier distance observations are obtained by correcting the carrier phase observations using a fixed ionosphere-free combined ambiguity and antenna phase wrapping effect.
5. The method according to claim 1, wherein in step 5), the pseudorange observations are subtracted by a satellite-side pseudorange hardware delay.
6. The method for performing non-differential whole network parallel processing on large-scale GNSS network observation data according to claim 1, wherein in step 5), the method for performing whole network parallel calculation based on the non-differential mode is as follows: taking the pseudo-range observation value and the carrier distance observation value as observed quantities; constructing a normal equation of each epoch in parallel by taking the epoch as granularity; solving a normal equation by adopting a parameter elimination algorithm based on recursive least square; the normal equation is inverted by matrix block inversion and Cholesky parallel decomposition.
7. The method for parallel processing of non-differential whole network of large-scale GNSS network observation data according to claim 1, wherein the parameters to be estimated obtained by parallel calculation of whole network include: station coordinates, satellite orbit, double-difference ambiguity, tropospheric delay, receiver and satellite clock bias, fractional phase bias and earth rotation parameters.
8. The method for parallel processing of non-differential whole network of large-scale GNSS network observation data according to claim 1, wherein for the non-ionospheric combined ambiguities of each station unfixed in the step 3), the non-ionospheric combined ambiguities of station star double differences are constructed, and the fixation of the non-ionospheric combined ambiguities is performed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011310618.XA CN112394376B (en) | 2020-11-20 | 2020-11-20 | Large-scale GNSS network observation data non-differential whole network parallel processing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011310618.XA CN112394376B (en) | 2020-11-20 | 2020-11-20 | Large-scale GNSS network observation data non-differential whole network parallel processing method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112394376A CN112394376A (en) | 2021-02-23 |
| CN112394376B true CN112394376B (en) | 2023-07-04 |
Family
ID=74605958
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011310618.XA Active CN112394376B (en) | 2020-11-20 | 2020-11-20 | Large-scale GNSS network observation data non-differential whole network parallel processing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112394376B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113282406B (en) * | 2021-04-27 | 2024-08-23 | 中国人民解放军陆军勤务学院 | GNSS large network observation data collaborative parallel computing method |
| CN113281794A (en) * | 2021-04-27 | 2021-08-20 | 中国人民解放军陆军勤务学院 | Method for parallel estimation of single-difference wide-lane FCB between satellites |
| CN113848577A (en) * | 2021-08-19 | 2021-12-28 | 中国能源建设集团江苏省电力设计院有限公司 | Large-scale GNSS network parallel resolving method and system based on dynamic partitioning |
| CN114355420B (en) * | 2021-12-15 | 2023-05-09 | 中国科学院国家授时中心 | PPP product positioning method and device for distributed Beidou position service center |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014065664A1 (en) * | 2012-10-25 | 2014-05-01 | Fugro N.V. | Ppp-rtk method and system for gnss signal based position determination |
| CN105929430A (en) * | 2016-07-14 | 2016-09-07 | 天津市勘察院 | GNSS (global navigation satellite system) zero-baseline inter-reference station ambiguity quick fixation method |
| CN107656295A (en) * | 2017-07-31 | 2018-02-02 | 武汉大学 | A kind of GNSS high accuracy Baseline Survey methods based on original observed data |
| CN107942346A (en) * | 2017-11-21 | 2018-04-20 | 武汉大学 | A kind of high-precision GNSS ionized layer TEC observation extracting method |
| CN108549095A (en) * | 2018-04-12 | 2018-09-18 | 中国人民解放军战略支援部队信息工程大学 | A kind of region CORS nets non-poor Enhancement Method and system parallel |
| CN111025346A (en) * | 2019-11-18 | 2020-04-17 | 广州南方卫星导航仪器有限公司 | Method for rapidly estimating clock error of GNSS precision satellite and storage medium |
| CN111208520A (en) * | 2020-01-17 | 2020-05-29 | 中国人民解放军战略支援部队信息工程大学 | Positioning method and device of submarine acoustic transponder |
| CN111273327A (en) * | 2020-03-20 | 2020-06-12 | 中国人民解放军61081部队 | Precise single-point positioning method based on combined and non-combined hybrid observation model |
Family Cites Families (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8694250B2 (en) * | 2008-01-09 | 2014-04-08 | Trimble Navigation Limited | Processing multi-GNSS data from mixed-type receivers |
| US8260551B2 (en) * | 2008-01-10 | 2012-09-04 | Trimble Navigation Limited | System and method for refining a position estimate of a low earth orbiting satellite |
| US9121932B2 (en) * | 2008-01-10 | 2015-09-01 | Trimble Navigation Limited | Refining a position estimate of a low earth orbiting satellite |
| US8018377B2 (en) * | 2009-01-23 | 2011-09-13 | Her Majesty The Queen In Right Of Canada As Represented By The Minister Of Natural Resources | Decoupled clock model with ambiguity datum fixing |
| US7983185B2 (en) * | 2009-02-12 | 2011-07-19 | Zulutime, Llc | Systems and methods for space-time determinations with reduced network traffic |
| WO2010096158A2 (en) * | 2009-02-22 | 2010-08-26 | Trimble Navigation Limited | Gnss signal processing methods and apparatus with ionospheric filters |
| US8638257B2 (en) * | 2009-10-15 | 2014-01-28 | Novatel, Inc. | Ultra short baseline GNSS receiver |
| JP2012194099A (en) * | 2011-03-17 | 2012-10-11 | Seiko Epson Corp | Pseudo-distance error estimation method, position calculation method and pseudo-distance error estimation apparatus |
| US9274230B2 (en) * | 2011-09-16 | 2016-03-01 | Trimble Navigation Limited | GNSS signal processing methods and apparatus |
| CN103175516B (en) * | 2013-02-26 | 2014-11-05 | 中国人民解放军信息工程大学 | Distributed computing method for adjustment of large-scale geodesic control net |
| CN104459745B (en) * | 2014-12-25 | 2017-03-15 | 东南大学 | A kind of many constellation Long baselines network RTK obscure portions degree fast resolution algorithms |
| CN105301619A (en) * | 2015-12-02 | 2016-02-03 | 武汉大学 | Rapid processing method and system for whole large scale GNSS network data |
| JP6877854B2 (en) * | 2017-01-30 | 2021-05-26 | 三菱電機株式会社 | Positioning device and positioning method |
| CN109212562A (en) * | 2018-08-29 | 2019-01-15 | 中国人民解放军61540部队 | A method of generating carrier wave pseudo range observed quantity |
| CN109799521A (en) * | 2019-03-14 | 2019-05-24 | 苏州工业园区测绘地理信息有限公司 | A kind of tri- subtractive combination cycle-slip detection and repair method of BDS/GPS |
| CN110749911B (en) * | 2019-10-28 | 2022-07-12 | 中国人民解放军战略支援部队信息工程大学 | Method and device for parallel preprocessing of satellite slope path distance of clean station of large-scale GNSS network |
| CN111045042B (en) * | 2019-12-20 | 2022-03-04 | 西安空间无线电技术研究所 | PPP-RTK enhancement method and system based on 'cloud-end' framework |
| CN111290004A (en) * | 2020-03-04 | 2020-06-16 | 高维时空(北京)网络有限公司 | Pseudo-range differential positioning method, pseudo-range differential positioning device, electronic equipment and storage medium |
-
2020
- 2020-11-20 CN CN202011310618.XA patent/CN112394376B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014065664A1 (en) * | 2012-10-25 | 2014-05-01 | Fugro N.V. | Ppp-rtk method and system for gnss signal based position determination |
| CN105929430A (en) * | 2016-07-14 | 2016-09-07 | 天津市勘察院 | GNSS (global navigation satellite system) zero-baseline inter-reference station ambiguity quick fixation method |
| CN107656295A (en) * | 2017-07-31 | 2018-02-02 | 武汉大学 | A kind of GNSS high accuracy Baseline Survey methods based on original observed data |
| CN107942346A (en) * | 2017-11-21 | 2018-04-20 | 武汉大学 | A kind of high-precision GNSS ionized layer TEC observation extracting method |
| CN108549095A (en) * | 2018-04-12 | 2018-09-18 | 中国人民解放军战略支援部队信息工程大学 | A kind of region CORS nets non-poor Enhancement Method and system parallel |
| CN111025346A (en) * | 2019-11-18 | 2020-04-17 | 广州南方卫星导航仪器有限公司 | Method for rapidly estimating clock error of GNSS precision satellite and storage medium |
| CN111208520A (en) * | 2020-01-17 | 2020-05-29 | 中国人民解放军战略支援部队信息工程大学 | Positioning method and device of submarine acoustic transponder |
| CN111273327A (en) * | 2020-03-20 | 2020-06-12 | 中国人民解放军61081部队 | Precise single-point positioning method based on combined and non-combined hybrid observation model |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112394376A (en) | 2021-02-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112394376B (en) | Large-scale GNSS network observation data non-differential whole network parallel processing method | |
| Hu et al. | Multi-GNSS fractional cycle bias products generation for GNSS ambiguity-fixed PPP at Wuhan University | |
| CN110007320B (en) | Network RTK resolving method | |
| CN105699999B (en) | A kind of method of the fixed narrow lane ambiguity of Beidou ground strengthening system base station | |
| CN108549095B (en) | Non-differential parallel enhancement method and system for regional CORS network | |
| CN108196284B (en) | GNSS network data processing method for fixing single-difference ambiguity between satellites | |
| CN109143298B (en) | Beidou and GPS observation value cycle slip detection and restoration method, equipment and storage equipment | |
| CN107942346B (en) | A kind of high-precision GNSS ionized layer TEC observation extracting method | |
| CN113848577A (en) | Large-scale GNSS network parallel resolving method and system based on dynamic partitioning | |
| CN112462396B (en) | Real-time parallel determination method for clock error of navigation satellite with high sampling rate | |
| CN113253314B (en) | Time synchronization method and system between low-orbit satellites | |
| CN109212562A (en) | A method of generating carrier wave pseudo range observed quantity | |
| CN115933356B (en) | High-precision time synchronization system and method for virtual atomic clock | |
| CN115373005A (en) | High-precision product conversion method between satellite navigation signals | |
| Zhao et al. | Accuracy and reliability of tropospheric wet refractivity tomography with GPS, BDS, and GLONASS observations | |
| CN110749911B (en) | Method and device for parallel preprocessing of satellite slope path distance of clean station of large-scale GNSS network | |
| Kuang et al. | Real-time GPS satellite orbit and clock estimation based on OpenMP | |
| Zhang et al. | Modeling, refinement and evaluation of multipath mitigation based on the hemispherical map in BDS2/BDS3 relative precise positioning | |
| CN115561793A (en) | Real-time Beidou phase decimal deviation rapid estimation method based on parallel computation | |
| Li et al. | Parallel resolution of large-scale GNSS network un-difference ambiguity | |
| Zeng et al. | Computationally efficient dual-frequency uncombined precise orbit determination based on IGS clock datum | |
| CN114355420B (en) | PPP product positioning method and device for distributed Beidou position service center | |
| CN113282406B (en) | GNSS large network observation data collaborative parallel computing method | |
| Xu et al. | BDS precise point positioning ambiguity resolution with high rate data and its application to seismic displacement and marine surveying | |
| CN114545461A (en) | Beidou tri-band fine resolving method with coordinate prior fused with GPS |
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 |