CN109001776A - A kind of navigation data processing method and system based on cloud computing - Google Patents

A kind of navigation data processing method and system based on cloud computing Download PDF

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Publication number
CN109001776A
CN109001776A CN201810564935.0A CN201810564935A CN109001776A CN 109001776 A CN109001776 A CN 109001776A CN 201810564935 A CN201810564935 A CN 201810564935A CN 109001776 A CN109001776 A CN 109001776A
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satellite
data
observation
gnss
low orbit
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穆旭成
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Beijing Future Navigation Technology Co Ltd
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Beijing Future Navigation Technology Co Ltd
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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
    • 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
    • G01S19/421—Determining position by combining or switching between position solutions or signals derived from different satellite radio beacon positioning systems; by combining or switching between position solutions or signals derived from different modes of operation in a single system
    • G01S19/425—Determining position by combining or switching between position solutions or signals derived from different satellite radio beacon positioning systems; by combining or switching between position solutions or signals derived from different modes of operation in a single system by combining or switching between signals derived from different satellite radio beacon positioning systems

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The present invention provides a kind of navigation data processing method and system based on cloud computing, which comprises high rail GNSS satellite Deep space tracking data and the GNSS satellite of low orbit satellite observe data in acquisition；It is pre-processed using the high rail GNSS satellite Deep space tracking data of distributed cloud computing centering and the GNSS satellite of low orbit satellite observation data；According to pretreated data construct earth station to middle high rail GNSS satellite the first observation model and middle high rail GNSS satellite to low orbit satellite the second observation model；Linearization process is carried out to the first observation model and the second observation model according to preset initial parameter value；The first observation model and the second observation model after linearisation is calculated using least square method, obtains the joint orbit determination parameter of middle high rail GNSS satellite and low orbit satellite.The present invention can high-precision determining low orbit satellite track, realize navigation satellite and low orbit satellite joint orbit determination and time synchronization, to provide real-time high-precision service, lifting system service performance for user.

Description

A kind of navigation data processing method and system based on cloud computing
Technical field
The present invention relates to technical field of satellite navigation more particularly to a kind of navigation data processing method based on cloud computing and System.
Background technique
Precision orbit determination theory starts from the 1950s, with the development of satellite tracking technology, accuracy of observation Raising and the continuous of data processing error model and kinetic model are refined, and navigation satellite orbit determination accuracy has obtained significantly mentioning Height, especially GPS, GLONASS satellite orbit determination accuracy are increased to current Centimeter Level from initial several hundred rice.Traditional navigation is defended Its satellite tracking data of star precise orbit determination method all derives from earth station, and the geographical distribution of tracking station will directly affect navigation The precision of precision orbit determination.It is GPS, GLONASS and Galileo system of medium earth orbit satellite (MEO) compared to full constellation, I State's Beidou, the quasi- zenith star (QZSS) of Japan, India's RNAV system (IRNSS) use different Constellation Design characteristics, introduce Geo-synchronous orbit satellite (GEO) or inclination geo-synchronous orbit satellite (IGSO).
In realizing process of the present invention, inventor find existing navigation data processing method the prior art has at least the following problems:
Existing GNSS enhancing system depends on GEO satellite, but GEO satellite is kept not substantially with respect to ground reference station It is dynamic, lead to have strong correlation between observation and track, fuzziness and clock deviation, orbit determination accuracy is poor.Moreover, because GEO satellite Orbit altitude is higher, therefore the transmission delay that enhancement information is broadcast is long, path loss.Further, since GEO satellite limited amount, And dipper system overseas cloth station relatively difficult to achieve, cause monitoring station to be distributed mainly on CONTINENTAL AREA OF CHINA, cannot achieve really complete Ball covering, zonal monitoring station distribution influence navigation satellite broadcast ephemeris precision and corresponding positioning, navigation and time service service.
Summary of the invention
In view of the above problems, the invention proposes a kind of navigation data processing method and system based on cloud computing, can Dependence of the navigation satellite orbit determination to earth station is effectively reduced, while again can high-precision determining low orbit satellite track, it is ensured that navigation The joint orbit determination and time synchronization of satellite and low orbit satellite, to provide real-time high-precision service, lifting system service for user Performance.
One aspect of the present invention provides a kind of navigation data processing method based on cloud computing, comprising:
High rail GNSS satellite Deep space tracking data and the GNSS satellite of low orbit satellite observe data in acquisition；
Distributed cloud computing is respectively adopted to the GNSS of middle high the rail GNSS satellite Deep space tracking data and low orbit satellite Satellite Observations carry out data prediction, to remove noise data；
According to the first observation model and middle high rail of pretreated data building earth station to middle high rail GNSS satellite Second observation model of the GNSS satellite to low orbit satellite；
First observation model and second observation model are carried out respectively according to preset initial parameter value linear Change processing；
The first observation model and the second observation model after calculating linearization process using least square method, obtain middle high rail The joint orbit determination parameter of GNSS satellite and low orbit satellite.
Optionally, high rail GNSS satellite Deep space tracking data in the acquisition, comprising:
Be arranged with the IP and communication network of ground station receiver, it is real-time using RTCM network protocol by IP and communication network The Deep space tracking data for obtaining the ground station receiver acquisition, by the Deep space tracking data conversion at the sight of RINEX format Measured data；Or
The storage equipment that the ground station receiver is accessed by ftp agreement or hard disk downloading mode, obtains the ground The Deep space tracking data for receiver real-time storage of standing, by the Deep space tracking data conversion at the observation data of RINEX format.
Optionally, the GNSS satellite for obtaining low orbit satellite observes data, comprising:
The GNSS satellite for receiving the low orbit satellite of repeater satellite transmission observes data, and the GNSS satellite of the low orbit satellite is seen Measured data is acquired by the GNSS receiver that low orbit satellite carries；The GNSS satellite observation data of the low orbit satellite are turned Change the observation data of RINEX format into.
Optionally, described that distributed cloud computing is respectively adopted to the middle high rail GNSS satellite Deep space tracking data and low rail The GNSS satellite observation data of satellite carry out data prediction, comprising:
Distributed cloud computing is respectively adopted and removes the middle high rail GNSS satellite Deep space tracking data and low orbit satellite In GNSS satellite observation data not there is carrier phase and the observation data and elevation of satellite of pseudorange to be less than cut-off simultaneously high The observation data for spending angle, obtain initial observation data；And
Rough error, outlier and/or cycle slip in the initial observation data is detected, and removes the initial observation number There are the epoch data of rough error, outlier and/or cycle slip according to middle.
Optionally, described that mould is observed to first observation model and described second respectively according to preset initial parameter value Type carries out linearization process, comprising:
Data are observed according to pretreated middle high rail GNSS satellite Deep space tracking data and the GNSS satellite of low orbit satellite High rail GNSS satellite is to low orbit satellite and the observed range O of earth station in generating respectively；
According to high rail GNSS satellite in the calculating of preset initial parameter value to low orbit satellite and the geometric distance of earth station；
The geometric distance calculated is corrected, obtains middle high rail GNSS satellite to low orbit satellite and the meter of earth station Calculate distance C；
By the observed range O of middle high rail GNSS satellite to earth station and distance C progress difference operation is calculated, first is generated and sees Survey the priori residual error of model, and by the observed range O of middle high rail GNSS satellite to low orbit satellite and the calculating distance C into Row difference operation generates the priori residual error of the second observation model；
According to the approximation of parameter vector in each observation model, corresponding parameter vector is calculated using observation model inclined Derivative obtains the first information matrix of the first observation model and the second information matrix of the second observation model；
It is corresponding that the first observation model is constructed according to first information matrix described in the priori residual sum of first observation model Linearisation after observational equation, and the second information matrix according to the priori residual sum of second observation model building Observational equation after the corresponding linearisation of second observation model.
Optionally, first observation model and the second observation mould calculated using least square method after linearization process Type obtains the joint orbit determination parameter of middle high rail GNSS satellite and low orbit satellite, comprising:
According to the carrier wave of the middle high rail GNSS satellite Deep space tracking data and the GNSS satellite of low orbit satellite observation data Phase, Pseudo-range Observations priori precision and first information matrix and the second information matrix are calculated linear using least square method Change treated the first observation model and the second observation model, obtains the joint orbit determination ginseng of middle high rail GNSS satellite and low orbit satellite Number.
Optionally, in described obtain after high rail GNSS satellite and the joint orbit determination parameter of low orbit satellite, the method Further include:
Use is sent by File Transfer Protocol by the joint orbit determination parameter of the middle high rail GNSS satellite and low orbit satellite Family terminal, so that user terminal is according to the monitoring data of rail GNSS satellite high in real-time reception and/or the GNSS of low orbit satellite Satellite Observations carry out joint orbit determination.
Another aspect of the present invention provides a kind of navigation data processing system based on cloud computing, comprising:
Acquiring unit observes number for rail GNSS satellite Deep space tracking data high in obtaining and the GNSS satellite of low orbit satellite According to；
Pretreatment unit, for be respectively adopted distributed cloud computing to the middle high rail GNSS satellite Deep space tracking data and The GNSS satellite observation data of low orbit satellite carry out data prediction, to remove noise data；
Model construction unit is seen for constructing earth station according to pretreated data to the first of middle high rail GNSS satellite Survey model and middle high rail GNSS satellite to low orbit satellite the second observation model；
Linearization process unit, for according to preset initial parameter value respectively to first observation model and described the Two observation models carry out linearization process；
Computing unit, for using the first observation model and the second observation mould after least square method calculating linearization process Type obtains the joint orbit determination parameter of middle high rail GNSS satellite and low orbit satellite.
Optionally, the acquiring unit, specifically for be arranged with the IP and communication network of ground station receiver, by IP and Communication network obtains the Deep space tracking data of the ground station receiver acquisition using RTCM network protocol in real time, by the ground Tracking data is converted into the observation data of RINEX format；Or, accessing the earth station by ftp agreement or hard disk downloading mode The storage equipment of receiver obtains the Deep space tracking data of the ground station receiver real-time storage, by the Deep space tracking number According to the observation data for being converted into RINEX format.
Optionally, the acquiring unit, the GNSS satellite for being specifically also used to receive the low orbit satellite of repeater satellite transmission are seen The GNSS satellite observation data of measured data, the low orbit satellite are acquired by the GNSS receiver that low orbit satellite carries；It will The GNSS satellite of the low orbit satellite observes data conversion into the observation data of RINEX format.
Optionally, the pretreatment unit, specifically for removing the middle high rail GNSS respectively using distributed cloud computing There is no the observation of carrier phase and pseudorange simultaneously in satellite ground tracking data and the GNSS satellite of low orbit satellite observation data Data and elevation of satellite are less than the observation data of elevation mask, obtain initial observation data；And to the initial observation Rough error, outlier and/or cycle slip in data are detected, and remove in the initial observation data there are rough error, outlier and/or The epoch data of cycle slip.
Optionally, the linearization process unit, comprising:
Generation module, for the GNSS according to pretreated middle high rail GNSS satellite Deep space tracking data and low orbit satellite Satellite Observations generate respectively in high rail GNSS satellite to low orbit satellite and the observed range O of earth station；
First computing module, for high rail GNSS satellite in being calculated according to preset initial parameter value to low orbit satellite and ground The geometric distance at face station；
Correction module obtains middle high rail GNSS satellite and defends to low rail for being corrected to the geometric distance calculated Star and the calculating distance C of earth station；
Second computing module, it is poor for carrying out the observed range O of middle high rail GNSS satellite to earth station and calculating distance C It is worth operation, generates the priori residual error of the first observation model, and by the observed range O of middle high rail GNSS satellite to low orbit satellite Difference operation is carried out with the calculating distance C, generates the priori residual error of the second observation model；
Third computing module, for the approximation according to parameter vector in each observation model, using observation model to phase The parameter vector answered calculates partial derivative, obtains the first information matrix of the first observation model and the second information of the second observation model Matrix；
Module is constructed, for the building of the first information matrix according to the priori residual sum of first observation model first Observational equation after the corresponding linearisation of observation model, and second according to the priori residual sum of second observation model Information matrix constructs the observational equation after the corresponding linearisation of the second observation model.
Optionally, the computing unit is specifically used for according to the middle high rail GNSS satellite Deep space tracking data and low rail Carrier phase, Pseudo-range Observations priori precision and the first information matrix and the second letter of the GNSS satellite observation data of satellite Matrix is ceased, the first observation model and the second observation model after linearization process is calculated using least square method obtain middle high rail The joint orbit determination parameter of GNSS satellite and low orbit satellite.
Optionally, the system also includes:
Release unit will after rail GNSS satellite high in described obtain and the joint orbit determination parameter of low orbit satellite The middle high rail GNSS satellite and the joint orbit determination parameter of low orbit satellite are sent to user terminal by File Transfer Protocol, for User terminal observes data according to the monitoring data of rail GNSS satellite high in real-time reception and/or the GNSS satellite of low orbit satellite Carry out joint orbit determination.
Navigation data processing method and system provided in an embodiment of the present invention based on cloud computing, by using distributed cloud The GNSS satellite observation data for calculating the high rail GNSS satellite Deep space tracking data of centering and low orbit satellite carry out data processing, with To the joint orbit determination parameter of middle high rail GNSS satellite and low orbit satellite, realize that senior middle school's low orbit satellite joint precise orbit determination and time are same Step.The present invention can effectively reduce dependence of the navigation satellite orbit determination to earth station, while again can high-precision determining low orbit satellite Track, it is ensured that the joint orbit determination and time synchronization of navigation satellite and low orbit satellite, so that real-time high-precision service is provided for user, Lifting system service performance.
The above description is only an overview of the technical scheme of the present invention, in order to better understand the technical means of the present invention, And it can be implemented in accordance with the contents of the specification, and in order to allow above and other objects of the present invention, feature and advantage can It is clearer and more comprehensible, the followings are specific embodiments of the present invention.
Detailed description of the invention
By reading the following detailed description of the preferred embodiment, various other advantages and benefits are common for this field Technical staff will become clear.The drawings are only for the purpose of illustrating a preferred embodiment, and is not considered as to the present invention Limitation.And throughout the drawings, the same reference numbers will be used to refer to the same parts.In the accompanying drawings:
Fig. 1 is a kind of flow chart of navigation data processing method based on cloud computing of the embodiment of the present invention；
Fig. 2 is flow chart of data processing schematic diagram in the navigation data processing method based on cloud computing of the embodiment of the present invention；
Fig. 3 is the linearisation flow chart of observational equation in the embodiment of the present invention；
Fig. 4 is the flow chart of another navigation data processing based on cloud computing of the embodiment of the present invention；
Fig. 5 is a kind of structural schematic diagram of navigation data processing system based on cloud computing of the embodiment of the present invention；
Fig. 6 is the structural schematic diagram of another navigation data processing system based on cloud computing of the embodiment of the present invention.
Specific embodiment
Exemplary embodiments of the present disclosure are described in more detail below with reference to accompanying drawings.Although showing the disclosure in attached drawing Exemplary embodiment, it being understood, however, that may be realized in various forms the disclosure without should be by embodiments set forth here It is limited.On the contrary, these embodiments are provided to facilitate a more thoroughly understanding of the present invention, and can be by the scope of the present disclosure It is fully disclosed to those skilled in the art.
Those skilled in the art of the present technique are appreciated that unless otherwise defined, all terms used herein (including technology art Language and scientific term), there is meaning identical with the general understanding of those of ordinary skill in fields of the present invention.Should also Understand, those terms such as defined in the general dictionary, it should be understood that have in the context of the prior art The consistent meaning of meaning, and unless otherwise will not be explained in an idealized or overly formal meaning by specific definitions.
All equipped with spaceborne GNSS receiver to carry out precise orbit determination, radial track determines current most of low orbit satellites Precision can be better than 1cm.Relative to ground survey station, low orbit satellite speed of related movement is fast, therefore it is empty compared to navigation satellite Between geometry variation it is fast, GNSS satellite is tracked by low orbit satellite, can effectively solve Beidou GEO satellite tracking station geometrical condition The problem of difference.In addition, low orbit satellite is without geographical restrictions, therefore it can solve the problem of Beidou area tracking net part.Again, Low orbit satellite can extend navigation satellite signal range of receiving, peomote grinding for current nautical star antenna phase center correction Study carefully.In view of to have at low cost, constellation networking fast etc. a little for low orbit satellite, therefore GNSS signal can be carried and broadcast equipment to ground Navigator fix signal is broadcast in face, it is made to have navigation satellite function, due to its high dynamic, positions ground installation quickly and high The maintenance of precision references frame is possibly realized.It is therefore desirable to carry out senior middle school's low orbit satellite Combined Treatment, to determine in unified space-time Senior middle school's low-orbit satellite precise ephemeris and clock deviation under frame.
As current each navigation system is gradually built, high rail navigation satellite there will be in 100 in orbit, simultaneously A low rail group of stars will also provide navigation Service.At this point, joint determines navigation and low orbit satellite Precise Orbit and clock deviation, will make wait estimate ginseng Number increases at geometry grade, and the normal equation that formation million is tieed up, such flood tide calculating task is that common computer is impossible, right Computer calculated performance proposes challenge.With the development of computer and network technology, make it possible cloud computing.Cloud computing It is another great change after the 1980's mainframe computer to the big change of client-server.Cloud computing is distributed Traditional computers and the networks such as calculating, parallel computation, effectiveness calculating, network storage, virtualization, load balancing, hot-standby redundancy The product of technology development fusion.Cloud computing, which makes to calculate, to be distributed on a large amount of distributed computer, rather than local computer or remote In journey server, computer and storage system can be accessed according to demand, there is super large by resource switch to needs using upper The features such as scale, virtualization, high reliability, versatility, enhanced scalability.With the increase of the demand of calculating, using cloud computing side Case can dynamically adjust computing resource configuration, not only can satisfy computational efficiency demand but also can satisfy cloud storage demand.It can Meets the needs of senior middle school's low orbit satellite joint orbit determination and time synchronization.
Fig. 1 diagrammatically illustrates the process of the navigation data processing method based on cloud computing of one embodiment of the invention Figure.Referring to Fig.1, the embodiment of the present invention the navigation data processing method based on cloud computing specifically includes the following steps:
High rail GNSS satellite Deep space tracking data and the GNSS satellite of low orbit satellite observe data in S11, acquisition.
S12, distributed cloud computing is respectively adopted to the middle high rail GNSS satellite Deep space tracking data and low orbit satellite GNSS satellite observes data and carries out data prediction, to remove noise data.
S13, according to pretreated data construct earth station to middle high rail GNSS satellite the first observation model and middle height Second observation model of the rail GNSS satellite to low orbit satellite.
S14, first observation model and second observation model are carried out respectively according to preset initial parameter value Linearization process.
S15, the first observation model and the second observation model after linearization process are calculated using least square method, obtain The joint orbit determination parameter of high rail GNSS satellite and low orbit satellite.
Navigation data processing method provided in an embodiment of the present invention based on cloud computing, by using distributed cloud computing pair Middle high rail GNSS satellite Deep space tracking data and the GNSS satellite of low orbit satellite observation data carry out data prediction, according to pre- place Data after reason construct observation model, then linearization process are carried out to observation model, by the observation model after linearisation It is solved, to obtain the joint orbit determination parameter of middle high rail GNSS satellite and low orbit satellite, judges that each joint after resolving is fixed Whether rail parameter restrains, and the condition of convergence is that carrier wave residual error is better than 10mm, and pseudorange accuracy is better than 1m.It, will if result is not converged The parametric results of calculating save as initial parameter values, re-start calculating, until numerical convergence, carry out data management by cloud With distribution, realization senior middle school's low orbit satellite joint precise orbit determination and time synchronization, specific flow chart of data processing, referring to fig. 2.The present invention Dependence of the navigation satellite orbit determination to earth station can be effectively reduced, while again can high-precision determining low orbit satellite track, it is ensured that The joint orbit determination and time synchronization of navigation satellite and low orbit satellite, to provide real-time high-precision service, lifting system for user Service performance.
In the embodiment of the present invention, high rail GNSS satellite Deep space tracking data in the acquisition are specifically included: setting and ground Stand the IP and communication network of receiver, obtains the ground station reception in real time using RTCM network protocol by IP and communication network The Deep space tracking data of machine acquisition, by the Deep space tracking data conversion at the observation data of RINEX format；Or, being assisted by ftp View or hard disk downloading mode access the storage equipment of the ground station receiver, obtain the ground station receiver real-time storage Deep space tracking data, by the Deep space tracking data conversion at the observation data of RINEX format.
In the embodiment of the present invention, the GNSS satellite for obtaining low orbit satellite observes data, specifically includes: receiving relaying and defends The GNSS satellite of the low orbit satellite of star transmission observes data, and the GNSS satellite observation data of the low orbit satellite pass through low orbit satellite The GNSS receiver of carrying is acquired；By the GNSS satellite observation data conversion of the low orbit satellite at the sight of RINEX format Measured data.
In a specific embodiment, it obtains earth station's GNSS satellite and observes data, can be received by ground tracking station Machine obtains, and there are two types of modes for acquisition methods, first is that directly by setting ground station receiver IP and communication network, using RTCM net The data of network agreement real-time storage receiver acquisition are converted into the RINEX format observation data of standard；Second is that passing through subsequent mode It obtains, receiver disk storage periodically can be accessed by ftp or hard disk downloading mode, download its day file stored afterwards, turned Standard RINEX format observation data are changed into, by tidal data recovering to cloud data management system.Low orbit satellite GNSS satellite is obtained to see Measured data, the data receive GNSS by the GNSS receiver that low orbit satellite carries and observe value signal and by repeater satellite transmission To ground data center, decoding storage obtains low rail from data center by FTP mode at standard RINEX format, orbit determination user Spaceborne GNSS observes data, by tidal data recovering to cloud data management system.
It is described that distributed cloud computing is respectively adopted to the middle high rail GNSS satellite Deep space tracking number in the embodiment of the present invention Data prediction is carried out according to the GNSS satellite observation data with low orbit satellite, comprising: distributed cloud computing removal institute is respectively adopted State in high rail GNSS satellite Deep space tracking data and the GNSS satellite observation data of low orbit satellite does not have carrier phase simultaneously And the observation data and elevation of satellite of pseudorange are less than the observation data of elevation mask, obtain initial observation data；And Rough error, outlier and/or cycle slip in the initial observation data is detected, and removes and exists in the initial observation data The epoch data of rough error, outlier and/or cycle slip.
In the present embodiment, ground monitoring station GNSS data and low orbit satellite GNSS data that cloud management system is collected into Quality control is carried out, treatment process uses distributed method, speed up processing.Data quality control detailed process is: first Deletion does not have L simultaneously1、L2、P1、P2Observation data and elevation of satellite be less than elevation mask observation data.Then Geometry-free combination combination, Melbourne-Wubbena combination (MW combination) and nothing are respectively formed with raw observation The difference (LC-PC) of ionospheric combination combines, and is combined using LC-PC and carries out rough error and outlier detects, using Geometry-free and MW combined detection observes the cycle slip in data.
In a specific embodiment, data prediction, ground are carried out to the GNSS data that cloud control data corporation is collected The spaceborne GNSS data of face station and low orbit satellite is using the distributed pretreatment in single station and quality control.When not considering observation noise, If the observational equation of carrier phase and pseudorange are as follows:
P1=ρ+c δ tr-cδts+ISB-δion+δtrop+δmul+εP (3)
P2=ρ+c δ tr-cδts+ISB-δion+δtrop+δmul+εP (4)
L in formula1、L2For carrier phase observable in two frequencies, P1、P2For Pseudo-range Observations in two frequencies, λ1、λ2It is two Wavelength in frequency, ρ represent the geometric distance of receiver to satellite launch moment, and c is the light velocity, δ trFor receiver clock-offsets, δ tsFor Satellite clock correction, δtropFor tropospheric delay influence, δionPostpone for the ionosphere effect unrelated with frequency, N1、N2It is two frequencies Corresponding integer ambiguity.By composition LG, MW and LC-PC combination of (1) (2) (3) (4) formula, for detecting in GNSS observation Outlier, rough error and cycle slip.Various combined expression-forms and characteristic distribution are described below:
LG combination:
Eliminating such as orbit error, receiver clock-offsets and satellite clock correction and tropospheric error influences, only comprising ionization The influence of layer and the combination of LG real number fuzziness.When adjacent epoch Ionospheric variability not acutely and carrier phase observable there is no L when cycle slipLGIt should be the gentle curve of variation, Δ LLGIts changing value is smaller.When not having cycle slip method, the value is 0, and all Jumping revaluate when occurring will change.
MW combination:
MW combination is equally unrelated with the geometric distance of receiver to satellite, and eliminates receiver clock-offsets and satellite clock correction, Tropospheric error and single order ionosphere effect.NδA theoretically constant, it is convenient in order to count, seek changes delta N between its epochδ, reason By upper Δ NδIt is 0, it is contemplated that observation noise, the value fluctuate near a certain value.It can cause its variation when there is cycle slip generation, but It is when onesize cycle slip occurs in two frequencies, this method will fail.By above-mentioned analysis, both combinations eliminate all Multiple error influences, and unrelated with the motion state of receiver, is very suitable to carry out the Detection of Cycle-slip of carrier phase.
LC-PC combination:
Observation noise is exaggerated 3 times due to the presence of observation noise, and without ionospheric combination, therefore LC-PC is combined It is practical insensitive for small cycle slip, but big rough error can be detected and as one of the index for measuring observation noise, such as This observation can be cast out when observation noise is excessive, it is accurate and stable to guarantee to resolve.In summary three kinds of observation data Combination, single distributed data pretreatment process of standing are as follows:
Multiple threads are opened, per thread handles a survey station data, all survey stations of circular treatment, until all surveys respectively Station is disposed.Wherein per thread treatment process is to read the observation data of an epoch, recycles every satellite composition ionization Layer residual error, MW combination observation and LC-PC combination observation.Given threshold check criteria (error mark in combination observation three times It is quasi-), and the observation for the threshold value that transfinites is marked；All epoch data are read to be picked according to Data Data quality control requirement Outlier detection is carried out using LC-PC combination difference value except the data that the continuous observation time is shorter, and to remaining data；It adopts respectively With the cycle slip and rough error in Ionosphere Residual Error combination and MW combined detection observation, the cycle slip detected and rough error epoch are rejected.
In the embodiment of the present invention, the first observation of earth station to middle high rail GNSS satellite is constructed according to pretreated data The second observation model of model and middle high rail GNSS satellite to low orbit satellite, comprising: be based on spaceborne GNSS observation and ground GNSS observes the carrier phase observational equation of all epochs of observation in station coordinates priori value building orbit determination segmental arc.
Specifically, forming earth station to the follow-up observation value of GNSS satellite by earth station and low orbit satellite and low rail being defended The observational equation of star is extended formula (1)-(4), and wherein earth station GNSS carrier phase and pseudorange observation equation can be write Are as follows:
Lsit=ρsit+cδtr-cδts+ISB+λN+δion+δtrop+δmul+εL (10)
Psit=ρsit+cδtr-cδts+ISB-δion+δtrop+δmul+εP (11)
Using ionospheric error δ after double frequency iono-free combinationionIt is eliminated, formula (11) (12) can be written as at this time:
Lsit=ρsit+cδtr-cδts+ISB+λN+δtrop+δmul+εL (12)
Psit=ρsit+cδtr-cδts+ISB+δtrop+δmul+εP (13)
Wherein ISB parameter is GNSS not different frequency deviation between homologous ray, is removed in the satellite navigation system emitted at present It is all made of Code Division Multiple Access outside GLONASS, therefore can only estimate an ISB parameter, but GLONASS uses CDMA Technology will estimate straggling parameter IFB between multiple systems, for spaceborne GNSS observation because therefore it is not propagated in troposphere Not by tropospheric error δtropIt influences, can then ignore δ in formula (10) (11) at this timetrop, therefore spaceborne GNSS carrier phase And pseudorange observation equation can be written as:
Lleo=ρleo+cδtr-cδts+ISB+λN+δmul+εL (14)
Pleo=ρleo+cδtr-cδts+ISB+δmul+εP (15)
L in formulasitFor ground tracking station carrier phase observable, PsitFor ground tracking station Pseudo-range Observations, LleoFor low orbit satellite Spaceborne GNSS carrier observations, PleoFor the spaceborne GNSS Pseudo-range Observations of low orbit satellite, ρsitFor GNSS satellite to ground tracking station Geometric distance, ρleoFor the geometric distance of GNSS satellite to low orbit satellite, c is the light velocity in vacuum, δ trFor receiver clock-offsets, δ ts For satellite clock correction, ISB deviation between GNSS system, IFB is GLONASS system inter-frequency deviation, and λ is carrier wavelength, and N is carrier wave phase Position fuzziness parameter, δionFor ionospheric error, δtropFor tropospheric error, δmulFor Multipath Errors, εLFor carrier phase observation Noise.
In the embodiment of the present invention, it is described according to preset initial parameter value respectively to first observation model and described Two observation models carry out linearization process, specifically include:
Data are observed according to pretreated middle high rail GNSS satellite Deep space tracking data and the GNSS satellite of low orbit satellite High rail GNSS satellite is to low orbit satellite and the observed range O of earth station in generating respectively；
According to preset initial parameter value calculate in high rail GNSS satellite to low orbit satellite and the geometric distance of earth station, In, the initial parameter value includes low orbit satellite initial reference track, GNSS satellite reference orbit and ground station coordinates.
The geometric distance calculated is corrected, obtains middle high rail GNSS satellite to low orbit satellite and the meter of earth station Calculate distance C；
By the observed range O of middle high rail GNSS satellite to earth station and distance C progress difference operation is calculated, first is generated and sees Survey the priori residual error of model, and by the observed range O of middle high rail GNSS satellite to low orbit satellite and the calculating distance C into Row difference operation generates the priori residual error of the second observation model；
According to the approximation of parameter vector in each observation model, corresponding parameter vector is calculated using observation model inclined Derivative obtains the first information matrix of the first observation model and the second information matrix of the second observation model；
It is corresponding that the first observation model is constructed according to first information matrix described in the priori residual sum of first observation model Linearisation after observational equation, and the second information matrix according to the priori residual sum of second observation model building Observational equation after the corresponding linearisation of second observation model.
In the present embodiment, parameter to be solved is initial for GNSS satellite original state parameter and low orbit satellite in observational equation State parameter, survey station position, ground receiver clock deviation, low orbit satellite receiver clock-offsets, earth station's tropospheric delay parameter, ground Straggling parameter, frequency offset parameter between fuzziness of standing parameter, low orbit satellite fuzziness parameter, system.Given parameters initial value And observational equation is unfolded near initial value, wherein GNSS satellite and low orbit satellite original state parameter include that orbit determination segmental arc is opened Begin moment satellite position, speed, optical pressure parameter, provides initial position by preliminary orbit information and speed, optical pressure parameter are set as Zero；Survey station coordinate initial value is given by priori coordinate information；Ground receiver clock deviation initial value is set as zero；Low orbit satellite receives Machine clock deviation is set as zero；Earth station's tropospheric delay parameter is set as saastamoinen model calculation value；Earth station's fuzziness Initial parameter value is set as zero；Low orbit satellite fuzziness initial parameter value is set as zero；Deviation and inter-frequency deviation parameter between system It is set as zero.
In the present embodiment, simultaneously observational equation is unfolded near initial value for given parameters initial value, specially according to ground GNSS carrier phase and Pseudo-range Observations priori precision in tracking station and low orbit satellite, and solve the priori precision of parameter, group At normal equation, and solve its corrected value.ρ in formula (10)-(15)sitIt, can be with for the geometric distance of satellite to ground tracking station It indicates are as follows:
ρleoFor the geometric distance of satellite to low orbit satellite, can indicate are as follows:
R in formulasIt is the letter of satellite orbit parameter x in orbit determination segmental arc for signal emission time GNSS satellite position vector Number, rsitFor signal time of reception receiver location vector, rleoFor signal time of reception low orbit satellite position vector.
Generally in high, normal, basic rail joint precise orbit determination, unknown parameter specifically includes that GNSS satellite orbit parameter, low rail are defended Star orbit parameter, earth station location parameter, low orbit satellite location parameter, ground tracking station receiver clock-offsets, low orbit satellite receive Deviation between machine clock deviation, ground station receiver fuzziness, spaceborne GNSS receiver fuzziness, GNSS system, between GLONASS system frequency Deviation, carrier phase ambiguity parameter, tropospheric error etc..By formula (12) (13) (14) (15) to above-mentioned parameter derivation, and Expansion can obtain error equation at reference orbit and parameters initial value are as follows:
ρ in formulasit,0、ρleo,0To utilize GNSS satellite reference orbit, low orbit satellite reference orbit and ground monitoring station coordinates The signal emission time GNSS satellite antenna average phase center being calculated to signal time of reception low orbit satellite and ground is supervised Survey station average phase distance between centers, ρsitFor GNSS satellite to the geometric distance between earth station's transmitting signal, ρleoIt is defended for GNSS Star to the geometric distance between low orbit satellite transmitting signal,
xsitFor ground receiver position vector, xgnssFor GNSS satellite position vector, xg0nss、xl 0 eoFor satellite initial shape State parameter (satellite initial time position, speed, kinetic parameter), δ tr,0、δts,0、N0Respectively receiver clock-offsets, satellite clock Difference, fuzziness parameter priori value, dtr、dts、dN0Respectively receiver clock-offsets, satellite clock correction, fuzziness parameter correction, ISB deviation and inter-frequency deviation correction between system, and c, λ are then respectively the GNSS carrier wavelength of the light velocity and respective frequencies.Its In:
WhereinIt can be obtained according to formula (16) and formula (17), more than the direction for being expressed as GNSS observation signal String valueAnd It is logical Solution variation equation is crossed to obtain；Observational equation is c to the partial derivative of receiver clock-offsets, satellite clock correction；Observational equation is to fuzziness The partial derivative of parameter is carrier wavelength lambda；Observational equation is 1 between the partial derivative of straggling parameter system.Observational equation linearisation Detailed process is as follows, and pretreated spaceborne and earth station's GNSS observation is converted to distance first, generates GNSS satellite and arrives Low orbit satellite and the observed range (O) of earth station refer to rail by low orbit satellite initial reference track, GNSS satellite at the same time Road and ground station coordinates calculate GNSS satellite to low orbit satellite and the geometric distance of earth station, at the same to the geometry calculated away from From model correction is subject to, GNSS satellite is calculated to the calculating distance (C) of low orbit satellite and earth station, is passed through observed range (O) It subtracts calculating distance (C) and generates observational equation priori residual error.By the approximation of parameter vector, using observational equation to parameter to Amount seeks local derviation, generates the information matrix A of observational equation, the observational equation referring to Fig. 3, after so far can forming linearisation.
In the embodiment of the present invention, first observation model and second calculated using least square method after linearization process Observation model obtains the joint orbit determination parameter of middle high rail GNSS satellite and low orbit satellite, comprising: is defended according to the middle high rail GNSS Carrier phase, the Pseudo-range Observations priori precision of star Deep space tracking data and the GNSS satellite of low orbit satellite observation data, and First information matrix and the second information matrix, the first observation model and second after linearization process is calculated using least square method Observation model obtains the joint orbit determination parameter of middle high rail GNSS satellite and low orbit satellite.
Information matrix is formed normal equation, and according to the priori precision of parameter, passes through Bayes's minimum by the embodiment of the present invention Two, which multiply estimation method, resolves high, normal, basic rail joint orbit determination parameter.Since the parameters such as receiver clock-offsets, satellite clock correction, ISB are related, The methods of benchmark constraint need to be added when solution, fixed a certain survey station receiver clock can be used or center of gravity benchmark is added.Joined When number resolves, in order to increase computational efficiency, parameter, which is divided into preset parameter, procedure parameter and specific time according to variation characteristic, to be had The preset parameter of effect, wherein preset parameter be fixed value in solution process, the parameter that will not be changed over time；Procedure parameter To change over time, each epoch needs the parameter recalculated；It is effectively and fixed for the parameter of a constant in specific time Justice is effective parameter in specific time.By ground station coordinates, GNSS satellite original state parameter, low orbit satellite original state ginseng Deviation etc. is defined as preset parameter between number, system；And ground station receiver clock deviation, low orbit satellite receiver clock-offsets, GNSS satellite Clock deviation, convection current layer parameter etc. are set as procedure parameter, and fuzziness parameter definition is specific time actual parameter.In calculating process In, it handles according to each epoch, each epoch observation is added in normal equation respectively, after forming normal equation, according to parameter Type carries out different disposal: eliminating the procedure parameter of each epoch, parameter information is preserved；Eliminate out-of-date fuzziness ginseng Number, parameter information is preserved；After the completion of the resolving of all preset parameters, the parameter for solving all cancellations is rewinded, and provide The precision information of relevant parameter.The parameter correction of solution is added to initial parameter value, obtains the adjusted value of parameter, checking computation Residual values, if carrier wave residual values are better than 10mm, pseudorange residuals value is better than 1m, then it is assumed that convergence is calculated, otherwise by calculating observation Value regards initial value, recalculates step 3-5 until convergence.Since high, normal, basic rail satellite clock correction parameter is asked together with orbit parameter Solution, therefore high, normal, basic rail satellite time synchronization is completed together with track determination.
In an alternate embodiment of the present invention where, as shown in figure 4, high rail GNSS satellite and low rail are defended in described obtain After the joint orbit determination parameter of star, the method also includes following steps:
S16, the middle high rail GNSS satellite and the joint orbit determination parameter of low orbit satellite are sent by File Transfer Protocol To user terminal, so that user terminal is according to the monitoring data of rail GNSS satellite high in real-time reception and/or low orbit satellite GNSS satellite observation data carry out joint orbit determination.
Entire treatment process is completed at cloud computing end, and the calculated result of calculating process and generation is stored in cloud service Device completes data publication by cloud data service, and user can obtain data service by conventional means such as FTP.
On the one hand the embodiment of the present invention has solved low orbit satellite Precise Orbit, to other earth observation applications of low orbit satellite Provide strong support；On the other hand orbit determination is merged by high, normal, basic rail, efficiently solves previous ground tracking station pair The problem of GNSS navigation satellite trace geometry condition difference, the precision that can obtain all kinds of satellite precise clock deviation and low orbit satellite simultaneously are fixed Rail enhances GNSS for low orbit satellite group and provides guarantee.
Therefore, the navigation data processing method provided in an embodiment of the present invention based on cloud computing, has the advantages that
1. determining the Precise Orbit and clock deviation of low orbit satellite and navigation satellite under unified Spatial-Temporal Frame simultaneously.
2. the low orbit satellite of high dynamic can improve trace geometry condition, navigation satellite orbit determination accuracy, especially GEO are promoted Satellite Orbit Determination precision.
3. low orbit satellite is without geographical restrictions, therefore can solve navigation satellite (especially Beidou) area tracking net part The problem of.
4. can satisfy the requirement of mass data processing capacity and efficiency using cloud computing method, while computing resource is dynamic State distribution can maximum reasonable distribution computational resource allocation.
For embodiment of the method, for simple description, therefore, it is stated as a series of action combinations, but this field Technical staff should be aware of, and embodiment of that present invention are not limited by the describe sequence of actions, because implementing according to the present invention Example, some steps may be performed in other sequences or simultaneously.Secondly, those skilled in the art should also know that, specification Described in embodiment belong to preferred embodiment, the actions involved are not necessarily necessary for embodiments of the present invention.
The structure that Fig. 5 diagrammatically illustrates the navigation data processing system based on cloud computing of one embodiment of the invention is shown It is intended to.Referring to Fig. 5, the navigation data processing system based on cloud computing of the embodiment of the present invention specifically includes acquiring unit 501, pre- Processing unit 502, model construction unit 503, linearization process unit 504 and computing unit 505, wherein the acquisition Unit 501 observes data for rail GNSS satellite Deep space tracking data high in obtaining and the GNSS satellite of low orbit satellite；Described Pretreatment unit 502, for distributed cloud computing to be respectively adopted to the middle high rail GNSS satellite Deep space tracking data and low rail The GNSS satellite observation data of satellite carry out data prediction, to remove noise data；The model construction unit 503 is used In the first observation model and middle high rail GNSS satellite according to pretreated data building earth station to middle high rail GNSS satellite To the second observation model of low orbit satellite；The linearization process unit 504, for being distinguished according to preset initial parameter value Linearization process is carried out to first observation model and second observation model；The computing unit 505, for using Least square method calculates the first observation model and the second observation model after linearization process, obtains middle high rail GNSS satellite and low The joint orbit determination parameter of rail satellite.
In the embodiment of the present invention, the acquiring unit 501, specifically for IP and communication network with ground station receiver is arranged Network obtains the Deep space tracking number of the ground station receiver acquisition by IP and communication network in real time using RTCM network protocol According to by the Deep space tracking data conversion at the observation data of RINEX format；Or, being visited by ftp agreement or hard disk downloading mode The storage equipment for asking the ground station receiver obtains the Deep space tracking data of the ground station receiver real-time storage, by institute Deep space tracking data conversion is stated into the observation data of RINEX format.
In the embodiment of the present invention, the acquiring unit 501 is specifically also used to receive the low orbit satellite of repeater satellite transmission GNSS satellite observes data, the GNSS receiver that the GNSS satellite observation data of the low orbit satellite are carried by low orbit satellite into Row acquisition；By the GNSS satellite observation data conversion of the low orbit satellite at the observation data of RINEX format.
In the embodiment of the present invention, the pretreatment unit 502, described in being removed respectively using distributed cloud computing Middle high rail GNSS satellite Deep space tracking data and the GNSS satellite of low orbit satellite observation data in do not have simultaneously carrier phase and The observation data and elevation of satellite of pseudorange are less than the observation data of elevation mask, obtain initial observation data；And to institute It states the rough error in initial observation data, outlier and/or cycle slip to be detected, and removes in the initial observation data and exist slightly The epoch data of difference, outlier and/or cycle slip.
In the embodiment of the present invention, the linearization process unit 504, including generation module, the first computing module, amendment mould Block, the second computing module, third computing module and building module, in which: the generation module, after according to pretreatment In high rail GNSS satellite Deep space tracking data and the GNSS satellite of low orbit satellite observation data generate respectively in high rail GNSS defend Star is to low orbit satellite and the observed range O of earth station；First computing module, based on according to preset initial parameter value High rail GNSS satellite is to low orbit satellite and the geometric distance of earth station in calculation；The correction module, for calculating Geometric distance is corrected, and obtains middle high rail GNSS satellite to low orbit satellite and the calculating distance C of earth station；Second meter Module is calculated, for by the observed range O of middle high rail GNSS satellite to earth station and calculating distance C and carrying out difference operation, generation the The priori residual error of one observation model, and by the observed range O of middle high rail GNSS satellite to low orbit satellite and it is described calculate away from Difference operation is carried out from C, generates the priori residual error of the second observation model；The third computing module, for according to each sight The approximation for surveying Model Parameter vector calculates partial derivative to corresponding parameter vector using observation model, obtains the first observation The first information matrix of model and the second information matrix of the second observation model；The building module, for according to described the First information matrix described in the priori residual sum of one observation model constructs the observation side after the corresponding linearisation of the first observation model Journey, and the second observation model of building of the second information matrix according to the priori residual sum of second observation model are corresponding Observational equation after linearisation.
Further, the computing unit is specifically used for according to middle high rail GNSS satellite Deep space tracking data and low Carrier phase, Pseudo-range Observations priori precision and the first information matrix and second of the GNSS satellite observation data of rail satellite Information matrix, the first observation model and the second observation model after linearization process is calculated using least square method, obtains middle height The joint orbit determination parameter of rail GNSS satellite and low orbit satellite.
In an alternate embodiment of the present invention where, as shown in fig. 6, the system also includes release unit 506, the publication Unit 506, after rail GNSS satellite high in described obtain and the joint orbit determination parameter of low orbit satellite, by the middle high rail GNSS satellite and the joint orbit determination parameter of low orbit satellite are sent to user terminal by File Transfer Protocol, for user terminal root The monitoring data of received middle high rail GNSS satellite and/or the GNSS satellite of low orbit satellite observation data combine fixed when factually Rail.
For system embodiments, since it is basically similar to the method embodiment, related so being described relatively simple Place illustrates referring to the part of embodiment of the method.
Navigation data processing method and system provided in an embodiment of the present invention based on cloud computing, by using distributed cloud The GNSS satellite observation data for calculating the high rail GNSS satellite Deep space tracking data of centering and low orbit satellite carry out data processing, with To the joint orbit determination parameter of middle high rail GNSS satellite and low orbit satellite, realize that senior middle school's low orbit satellite joint precise orbit determination and time are same Step.The present invention can effectively reduce dependence of the navigation satellite orbit determination to earth station, while again can high-precision determining low orbit satellite Track, it is ensured that the joint orbit determination and time synchronization of navigation satellite and low orbit satellite, so that real-time high-precision service is provided for user, Lifting system service performance.
The apparatus embodiments described above are merely exemplary, wherein described, unit can as illustrated by the separation member It is physically separated with being or may not be, component shown as a unit may or may not be physics list Member, it can it is in one place, or may be distributed over multiple network units.It can be selected according to the actual needs In some or all of the modules achieve the purpose of the solution of this embodiment.Those of ordinary skill in the art are not paying creativeness Labour in the case where, it can understand and implement.
Through the above description of the embodiments, those skilled in the art can be understood that each embodiment can It realizes by means of software and necessary general hardware platform, naturally it is also possible to pass through hardware.Based on this understanding, on Stating technical solution, substantially the part that contributes to existing technology can be embodied in the form of software products in other words, should Computer software product may be stored in a computer readable storage medium, such as ROM/RAM, magnetic disk, CD, including several fingers It enables and using so that a computer equipment (can be personal computer, server or the network equipment etc.) executes each implementation Method described in certain parts of example or embodiment.
In addition, it will be appreciated by those of skill in the art that although some embodiments in this include institute in other embodiments Including certain features rather than other feature, but the combination of the feature of different embodiment means in the scope of the present invention Within and form different embodiments.For example, in the following claims, embodiment claimed it is any it One can in any combination mode come using.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although Present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: it still may be used To modify the technical solutions described in the foregoing embodiments or equivalent replacement of some of the technical features； And these are modified or replaceed, technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution spirit and Range.

Claims (14)

1. a kind of navigation data processing method based on cloud computing characterized by comprising
High rail GNSS satellite Deep space tracking data and the GNSS satellite of low orbit satellite observe data in acquisition；
Distributed cloud computing is respectively adopted to the GNSS satellite of middle high the rail GNSS satellite Deep space tracking data and low orbit satellite It observes data and carries out data prediction, to remove noise data；
Earth station is constructed according to pretreated data to defend to the first observation model of middle high rail GNSS satellite and middle high rail GNSS Second observation model of the star to low orbit satellite；
First observation model and second observation model are carried out at linearisation respectively according to preset initial parameter value Reason；
The first observation model and the second observation model after calculating linearization process using least square method, obtain middle high rail GNSS The joint orbit determination parameter of satellite and low orbit satellite.
2. the method according to claim 1, wherein high rail GNSS satellite Deep space tracking data in the acquisition, Include:
Be arranged with the IP and communication network of ground station receiver, obtained in real time by IP and communication network using RTCM network protocol The Deep space tracking data of the ground station receiver acquisition, by the Deep space tracking data conversion at the observation number of RINEX format According to；Or
The storage equipment that the ground station receiver is accessed by ftp agreement or hard disk downloading mode obtains the earth station and connects The Deep space tracking data of receipts machine real-time storage, by the Deep space tracking data conversion at the observation data of RINEX format.
3. the method according to claim 1, wherein it is described obtain low orbit satellite GNSS satellite observe data, Include:
The GNSS satellite for receiving the low orbit satellite of repeater satellite transmission observes data, and the GNSS satellite of the low orbit satellite observes number It is acquired according to the GNSS receiver carried by low orbit satellite；By the GNSS satellite of low orbit satellite observation data conversion at The observation data of RINEX format.
4. the method according to claim 1, wherein described be respectively adopted distributed cloud computing to the middle high rail GNSS satellite Deep space tracking data and the GNSS satellite of low orbit satellite observation data carry out data prediction, comprising:
It is respectively adopted that distributed cloud computing removes the middle high rail GNSS satellite Deep space tracking data and the GNSS of low orbit satellite is defended In star observation data not there is carrier phase and the observation data and elevation of satellite of pseudorange to be less than elevation mask simultaneously Data are observed, initial observation data are obtained；And
Rough error, outlier and/or cycle slip in the initial observation data is detected, and is removed in the initial observation data There are the epoch data of rough error, outlier and/or cycle slip.
5. according to the method described in claim 4, it is characterized in that, it is described according to preset initial parameter value respectively to described One observation model and second observation model carry out linearization process, comprising:
According to pretreated middle high rail GNSS satellite Deep space tracking data and the GNSS satellite of low orbit satellite observation data difference High rail GNSS satellite is to low orbit satellite and the observed range O of earth station in generation；
According to high rail GNSS satellite in the calculating of preset initial parameter value to low orbit satellite and the geometric distance of earth station；
The geometric distance calculated is corrected, obtain middle high rail GNSS satellite to low orbit satellite and earth station calculating away from From C；
By the observed range O of middle high rail GNSS satellite to earth station and distance C progress difference operation is calculated, generates the first observation mould The priori residual error of type, and the observed range O of middle high rail GNSS satellite to low orbit satellite and calculating distance C progress is poor It is worth operation, generates the priori residual error of the second observation model；
According to the approximation of parameter vector in each observation model, local derviation is calculated to corresponding parameter vector using observation model Number, obtains the first information matrix of the first observation model and the second information matrix of the second observation model；
The corresponding line of the first observation model is constructed according to first information matrix described in the priori residual sum of first observation model Property after observational equation, and the second information matrix according to the priori residual sum of second observation model building second Observational equation after the corresponding linearisation of observation model.
6. according to the method described in claim 5, it is characterized in that, after the calculating linearization process using least square method First observation model and the second observation model obtain the joint orbit determination parameter of middle high rail GNSS satellite and low orbit satellite, comprising:
According to the middle high rail GNSS satellite Deep space tracking data and the GNSS satellite of low orbit satellite observation data carrier phase, Pseudo-range Observations priori precision and first information matrix and the second information matrix are calculated at linearisation using least square method The first observation model and the second observation model after reason obtain the joint orbit determination parameter of middle high rail GNSS satellite and low orbit satellite.
7. the method according to claim 1, wherein high rail GNSS satellite and low orbit satellite in described obtain After joint orbit determination parameter, the method also includes:
User's end is sent by File Transfer Protocol by the middle high rail GNSS satellite and the joint orbit determination parameter of low orbit satellite End, so that user terminal is according to the monitoring data of rail GNSS satellite high in real-time reception and/or the GNSS satellite of low orbit satellite Observation data carry out joint orbit determination.
8. a kind of navigation data processing system based on cloud computing characterized by comprising
Acquiring unit observes data for rail GNSS satellite Deep space tracking data high in obtaining and the GNSS satellite of low orbit satellite；
Pretreatment unit, for distributed cloud computing to be respectively adopted to the middle high rail GNSS satellite Deep space tracking data and low rail The GNSS satellite observation data of satellite carry out data prediction, to remove noise data；
Model construction unit observes mould to the first of middle high rail GNSS satellite for constructing earth station according to pretreated data The second observation model of type and middle high rail GNSS satellite to low orbit satellite；
Linearization process unit, for being seen respectively to first observation model and described second according to preset initial parameter value It surveys model and carries out linearization process；
Computing unit, the first observation model and the second observation model after linearization process is calculated for use least square method, Obtain the joint orbit determination parameter of middle high rail GNSS satellite and low orbit satellite.
9. system according to claim 8, which is characterized in that the acquiring unit connects specifically for being arranged with earth station The IP and communication network of receipts machine obtain the ground station receiver using RTCM network protocol by IP and communication network in real time and adopt The Deep space tracking data of collection, by the Deep space tracking data conversion at the observation data of RINEX format；Or, by ftp agreement or Hard disk downloading mode accesses the storage equipment of the ground station receiver, obtains the ground of the ground station receiver real-time storage Tracking data, by the Deep space tracking data conversion at the observation data of RINEX format.
10. system according to claim 8, which is characterized in that the acquiring unit is specifically also used to receive repeater satellite The GNSS satellite of the low orbit satellite of transmission observes data, and the GNSS satellite observation data of the low orbit satellite are taken by low orbit satellite The GNSS receiver of load is acquired；By the GNSS satellite observation data conversion of the low orbit satellite at the observation of RINEX format Data.
11. system according to claim 8, which is characterized in that the pretreatment unit is specifically used for using distributed cloud It calculates different in the GNSS satellite observation data for remove respectively the middle high rail GNSS satellite Deep space tracking data and low orbit satellite When with carrier phase and pseudorange observation data and elevation of satellite be less than elevation mask observation data, obtain just Begin observation data；And rough error, outlier and/or the cycle slip in the initial observation data are detected, and removes described initial There are the epoch data of rough error, outlier and/or cycle slip in observation data.
12. system according to claim 11, which is characterized in that the linearization process unit, comprising:
Generation module, for the GNSS satellite according to pretreated middle high rail GNSS satellite Deep space tracking data and low orbit satellite Observation data generate respectively in high rail GNSS satellite to low orbit satellite and the observed range O of earth station；
First computing module, for high rail GNSS satellite in being calculated according to preset initial parameter value to low orbit satellite and earth station Geometric distance；
Correction module, for being corrected to the geometric distance calculated, obtain middle high rail GNSS satellite to low orbit satellite and The calculating distance C of earth station；
Second computing module, for the observed range O of middle high rail GNSS satellite to earth station and calculating distance C to be carried out difference fortune It calculates, generates the priori residual error of the first observation model, and by middle high rail GNSS satellite to the observed range O of low orbit satellite and institute It states and calculates distance C progress difference operation, generate the priori residual error of the second observation model；
Third computing module, for the approximation according to parameter vector in each observation model, using observation model to corresponding Parameter vector calculates partial derivative, obtains the first information matrix of the first observation model and the second information square of the second observation model Battle array；
Module is constructed, for the first observation of the building of the first information matrix according to the priori residual sum of first observation model Observational equation after the corresponding linearisation of model, and the second information according to the priori residual sum of second observation model Matrix constructs the observational equation after the corresponding linearisation of the second observation model.
13. system according to claim 12, which is characterized in that the computing unit is specifically used for according to the middle height The carrier phase of rail GNSS satellite Deep space tracking data and the GNSS satellite of low orbit satellite observation data, Pseudo-range Observations priori essence Degree and first information matrix and the second information matrix, the first observation mould after linearization process is calculated using least square method Type and the second observation model obtain the joint orbit determination parameter of middle high rail GNSS satellite and low orbit satellite.
14. system according to claim 8, which is characterized in that the system also includes:
Release unit will be described after rail GNSS satellite high in described obtain and the joint orbit determination parameter of low orbit satellite Middle high rail GNSS satellite and the joint orbit determination parameter of low orbit satellite are sent to user terminal by File Transfer Protocol, for user Terminal is carried out according to the monitoring data of rail GNSS satellite high in real-time reception and/or the GNSS satellite observation data of low orbit satellite Joint orbit determination.
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CN115098828B (en) * 2022-08-26 2022-11-04 北京控制工程研究所 Method and device for calculating low-orbit satellite orbit in near circle
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CN117377057B (en) * 2023-12-08 2024-02-13 中国科学院国家授时中心 Satellite-ground time synchronization method and system for low-orbit satellite and ground station

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Application publication date: 20181214